Journal of Clinical Medicine

Editorial

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4 pages, 382 KiB

Open AccessEditorial

Research Agenda of Climate Change during and after the Coronavirus Disease 2019 (COVID-19) Pandemic

by Hiroshi Nishiura and Nobuo Mimura

J. Clin. Med. 2021, 10(4), 770; https://doi.org/10.3390/jcm10040770 - 15 Feb 2021

Viewed by 4004

Abstract

The global pandemic of coronavirus disease 2019 (COVID-19) rapidly spread worldwide during the first few months of 2020 [...] Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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6 pages, 681 KiB

Open AccessEditorial

A Comparison of Case Fatality Risk of COVID-19 between Singapore and Japan

by Taishi Kayano and Hiroshi Nishiura

J. Clin. Med. 2020, 9(10), 3326; https://doi.org/10.3390/jcm9103326 - 16 Oct 2020

Cited by 16 | Viewed by 3682

Abstract

The crude case fatality risk (CFR) for coronavirus disease (COVID-19) in Singapore is remarkably small. We aimed to estimate the unbiased CFR by age for Singapore and Japan and compare these estimates by calculating the standardized mortality ratio (SMR). Age-specific CFRs for COVID-19 [...] Read more.

The crude case fatality risk (CFR) for coronavirus disease (COVID-19) in Singapore is remarkably small. We aimed to estimate the unbiased CFR by age for Singapore and Japan and compare these estimates by calculating the standardized mortality ratio (SMR). Age-specific CFRs for COVID-19 were estimated in real time, adjusting for the delay from illness onset to death. The SMR in Japan was estimated by using the age distribution of the Singapore population. Among cases aged 60–69 years and 70–79 years, the age-specific CFRs in Singapore were estimated as 1.84% (95% confidence interval: 0.46–4.72%) and 5.57% (1.41–13.97%), respectively, and those in Japan as 5.52% (4.55–6.62%) and 15.49% (13.81–17.27%), respectively. The SMR of COVID-19 in Japan, when compared with Singapore as the baseline, was estimated to be 1.46 (1.09–2.96). The overall CFR for Singapore is lower than that for Japan. It is possible that the circulating variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Singapore causes a milder clinical course of COVID-19 infection compared with other strains. If infection with a low-virulence SARS-CoV-2 variant provides protection against infection by high-virulence strains, the existence of such a strain is encouraging news for the many countries struggling to suppress this virus. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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3 pages, 187 KiB

Open AccessEditorial

COVID-19 Deaths: Are We Sure It Is Pneumonia? Please, Autopsy, Autopsy, Autopsy!

by Cristoforo Pomara, Giovanni Li Volti and Francesco Cappello

J. Clin. Med. 2020, 9(5), 1259; https://doi.org/10.3390/jcm9051259 - 26 Apr 2020

Cited by 72 | Viewed by 13686

Abstract

The current outbreak of COVID-19 severe respiratory disease, which started in Wuhan, China, is an ongoing challenge, and a major threat to public health that requires surveillance, prompt diagnosis, and research efforts to understand this emergent pathogen and to develop an effective response. [...] Read more.

The current outbreak of COVID-19 severe respiratory disease, which started in Wuhan, China, is an ongoing challenge, and a major threat to public health that requires surveillance, prompt diagnosis, and research efforts to understand this emergent pathogen and to develop an effective response. Due to the scientific community’s efforts, there is an increasing body of published studies describing the virus’ biology, its transmission and diagnosis, its clinical features, its radiological findings, and the development of candidate therapeutics and vaccines. Despite the decline in postmortem examination rate, autopsy remains the gold standard to determine why and how death happens. Defining the pathophysiology of death is not only limited to forensic considerations; it may also provide useful clinical and epidemiologic insights. Selective approaches to postmortem diagnosis, such as limited postmortem sampling over full autopsy, can also be useful in the control of disease outbreaks and provide valuable knowledge for managing appropriate control measures. In this scenario, we strongly recommend performing full autopsies on patients who died with suspected or confirmed COVID-19 infection, particularly in the presence of several comorbidities. Only by working with a complete set of histological samples obtained through autopsy can one ascertain the exact cause(s) of death, optimize clinical management, and assist clinicians in pointing out a timely and effective treatment to reduce mortality. Death can teach us not only about the disease, it might also help with its prevention and, above all, treatment. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

4 pages, 993 KiB

Open AccessEditorial

Backcalculating the Incidence of Infection with COVID-19 on the Diamond Princess

by Hiroshi Nishiura

J. Clin. Med. 2020, 9(3), 657; https://doi.org/10.3390/jcm9030657 - 29 Feb 2020

Cited by 37 | Viewed by 18856

Abstract

To understand the time-dependent risk of infection on a cruise ship, the Diamond Princess, I estimated the incidence of infection with novel coronavirus (COVID-19). The epidemic curve of a total of 199 confirmed cases was drawn, classifying individuals into passengers with and without [...] Read more.

To understand the time-dependent risk of infection on a cruise ship, the Diamond Princess, I estimated the incidence of infection with novel coronavirus (COVID-19). The epidemic curve of a total of 199 confirmed cases was drawn, classifying individuals into passengers with and without close contact and crew members. A backcalculation method was employed to estimate the incidence of infection. The peak time of infection was seen for the time period from 2 to 4 February 2020, and the incidence has abruptly declined afterwards. The estimated number of new infections among passengers without close contact was very small from 5 February on which a movement restriction policy was imposed. Without the intervention from 5 February, it was predicted that the cumulative incidence with and without close contact would have been as large as 1373 (95% CI: 570, 2176) and 766 (95% CI: 587, 946) cases, respectively, while these were kept to be 102 and 47 cases, respectively. Based on an analysis of illness onset data on board, the risk of infection among passengers without close contact was considered to be very limited. Movement restriction greatly reduced the number of infections from 5 February onwards. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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7 pages, 218 KiB

Open AccessEditorial

Communicating the Risk of Death from Novel Coronavirus Disease (COVID-19)

by Tetsuro Kobayashi, Sung-mok Jung, Natalie M. Linton, Ryo Kinoshita, Katsuma Hayashi, Takeshi Miyama, Asami Anzai, Yichi Yang, Baoyin Yuan, Andrei R. Akhmetzhanov, Ayako Suzuki and Hiroshi Nishiura

J. Clin. Med. 2020, 9(2), 580; https://doi.org/10.3390/jcm9020580 - 21 Feb 2020

Cited by 124 | Viewed by 43221

Abstract

To understand the severity of infection for a given disease, it is common epidemiological practice to estimate the case fatality risk, defined as the risk of death among cases. However, there are three technical obstacles that should be addressed to appropriately measure this [...] Read more.

To understand the severity of infection for a given disease, it is common epidemiological practice to estimate the case fatality risk, defined as the risk of death among cases. However, there are three technical obstacles that should be addressed to appropriately measure this risk. First, division of the cumulative number of deaths by that of cases tends to underestimate the actual risk because deaths that will occur have not yet observed, and so the delay in time from illness onset to death must be addressed. Second, the observed dataset of reported cases represents only a proportion of all infected individuals and there can be a substantial number of asymptomatic and mildly infected individuals who are never diagnosed. Third, ascertainment bias and risk of death among all those infected would be smaller when estimated using shorter virus detection windows and less sensitive diagnostic laboratory tests. In the ongoing COVID-19 epidemic, health authorities must cope with the uncertainty in the risk of death from COVID-19, and high-risk individuals should be identified using approaches that can address the abovementioned three problems. Although COVID-19 involves mostly mild infections among the majority of the general population, the risk of death among young adults is higher than that of seasonal influenza, and elderly with underlying comorbidities require additional care. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

3 pages, 383 KiB

Open AccessEditorial

Initial Cluster of Novel Coronavirus (2019-nCoV) Infections in Wuhan, China Is Consistent with Substantial Human-to-Human Transmission

by Hiroshi Nishiura, Natalie M. Linton and Andrei R. Akhmetzhanov

J. Clin. Med. 2020, 9(2), 488; https://doi.org/10.3390/jcm9020488 - 11 Feb 2020

Cited by 76 | Viewed by 13694

Abstract

Reanalysis of the epidemic curve from the initial cluster of cases with novel coronavirus (2019-nCoV) in December 2019 indicates substantial human-to-human transmission. It is possible that the common exposure history at a seafood market in Wuhan originated from the human-to-human transmission events within [...] Read more.

Reanalysis of the epidemic curve from the initial cluster of cases with novel coronavirus (2019-nCoV) in December 2019 indicates substantial human-to-human transmission. It is possible that the common exposure history at a seafood market in Wuhan originated from the human-to-human transmission events within the market, and the early, strong emphasis that market exposure indicated animal-to-human transmission was potentially the result of observer bias. To support the hypothesis of zoonotic origin of 2019-nCoV stemming from the Huanan seafood market, the index case should have had exposure history related to the market and the virus should have been identified from animals sold at the market. As these requirements remain unmet, zoonotic spillover at the market must not be overemphasized. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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3 pages, 181 KiB

Open AccessEditorial

The Rate of Underascertainment of Novel Coronavirus (2019-nCoV) Infection: Estimation Using Japanese Passengers Data on Evacuation Flights

by Hiroshi Nishiura, Tetsuro Kobayashi, Yichi Yang, Katsuma Hayashi, Takeshi Miyama, Ryo Kinoshita, Natalie M. Linton, Sung-mok Jung, Baoyin Yuan, Ayako Suzuki and Andrei R. Akhmetzhanov

J. Clin. Med. 2020, 9(2), 419; https://doi.org/10.3390/jcm9020419 - 4 Feb 2020

Cited by 188 | Viewed by 32497

Abstract

From 29 to 31 January 2020, a total of 565 Japanese citizens were evacuated from Wuhan, China on three chartered flights. All passengers were screened upon arrival in Japan for symptoms consistent with novel coronavirus (2019-nCoV) infection and tested for presence of the [...] Read more.

From 29 to 31 January 2020, a total of 565 Japanese citizens were evacuated from Wuhan, China on three chartered flights. All passengers were screened upon arrival in Japan for symptoms consistent with novel coronavirus (2019-nCoV) infection and tested for presence of the virus. Assuming that the mean detection window of the virus can be informed by the mean serial interval (estimated at 7.5 days), the ascertainment rate of infection was estimated at 9.2% (95% confidence interval: 5.0, 20.0). This indicates that the incidence of infection in Wuhan can be estimated at 20,767 infected individuals, including those with asymptomatic and mildly symptomatic infections. The infection fatality risk (IFR)—the actual risk of death among all infected individuals—is therefore 0.3% to 0.6%, which may be comparable to Asian influenza pandemic of 1957–1958. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

5 pages, 198 KiB

Open AccessEditorial

The Extent of Transmission of Novel Coronavirus in Wuhan, China, 2020

by Hiroshi Nishiura, Sung-mok Jung, Natalie M. Linton, Ryo Kinoshita, Yichi Yang, Katsuma Hayashi, Tetsuro Kobayashi, Baoyin Yuan and Andrei R. Akhmetzhanov

J. Clin. Med. 2020, 9(2), 330; https://doi.org/10.3390/jcm9020330 - 24 Jan 2020

Cited by 274 | Viewed by 45941

Abstract

A cluster of pneumonia cases linked to a novel coronavirus (2019-nCoV) was reported by China in late December 2019. Reported case incidence has now reached the hundreds, but this is likely an underestimate. As of 24 January 2020, with reports of thirteen exportation [...] Read more.

A cluster of pneumonia cases linked to a novel coronavirus (2019-nCoV) was reported by China in late December 2019. Reported case incidence has now reached the hundreds, but this is likely an underestimate. As of 24 January 2020, with reports of thirteen exportation events, we estimate the cumulative incidence in China at 5502 cases (95% confidence interval: 3027, 9057). The most plausible number of infections is in the order of thousands, rather than hundreds, and there is a strong indication that untraced exposures other than the one in the epidemiologically linked seafood market in Wuhan have occurred. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

Research

Jump to: Editorial, Review

15 pages, 2200 KiB

Open AccessArticle

Assessing Interventions against Coronavirus Disease 2019 (COVID-19) in Osaka, Japan: A Modeling Study

by Ko Nakajo and Hiroshi Nishiura

J. Clin. Med. 2021, 10(6), 1256; https://doi.org/10.3390/jcm10061256 - 18 Mar 2021

Cited by 10 | Viewed by 5620

Abstract

Estimation of the effective reproduction number, R(t), of coronavirus disease (COVID-19) in real-time is a continuing challenge. R(t) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation [...] Read more.

Estimation of the effective reproduction number, R(t), of coronavirus disease (COVID-19) in real-time is a continuing challenge. R(t) reflects the epidemic dynamics based on readily available illness onset data, and is useful for the planning and implementation of public health and social measures. In the present study, we proposed a method for computing the R(t) of COVID-19, and applied this method to the epidemic in Osaka prefecture from February to September 2020. We estimated R(t) as a function of the time of infection using the date of illness onset. The epidemic in Osaka came under control around 2 April during the first wave, and 26 July during the second wave. R(t) did not decline drastically following any single intervention. However, when multiple interventions were combined, the relative reductions in R(t) during the first and second waves were 70% and 51%, respectively. Although the second wave was brought under control without declaring a state of emergency, our model comparison indicated that relying on a single intervention would not be sufficient to reduce R(t) < 1. The outcome of the COVID-19 pandemic continues to rely on political leadership to swiftly design and implement combined interventions capable of broadly and appropriately reducing contacts. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Daily numbers of new coronavirus disease 2019 (COVID-19) cases in Osaka prefecture from 17 February to 28 September 2020. Cases were counted as a function of the date of illness onset. Full article ">Figure 2
Comparison between the observed and model-predicted incidence of coronavirus disease 2019 (COVID-19) in Osaka prefecture. Comparisons were made as a function of the date of illness onset. Solid circles represent observed cases, while the continuous black line shows the maximum likelihood estimate of predicted incidence. Dotted lines represent the lower and upper boundaries of the 95% confidence intervals based on the bootstrap method. Full article ">Figure 3
Estimation of the effective reproduction number, R(t), using two methods. (A) Comparison of R(t) estimates calculated using a novel method based on the observed date of illness onset (continuous black line) and using the existing method based on non-parametrically back-projected incidence of infection. Dashed lines show the 95% confidence intervals of R(t) based on the bootstrap method. The horizontal gray line indicates R(t) = 1; below this value incidence declines. (B) Chronological relationship between announcements in Osaka and R(t). Black arrows represent announcements of requests to reduce contacts or any other announcements associated with infection control. White arrows represent announcements of the cessation of specific countermeasures. Long black line shows when R(t) decline below 1, and long grey arrows indicate when reopening was declared. On 3 April, the governor requested voluntary restrictions on weekend outings. On 28 July, the governor requested residents to voluntarily abstain from social events involving the consumption of alcohol by five or more persons. On 21 May, Osaka was released from the state of emergency, and on 31 August, the governor permitted social events involving the consumption of alcohol by five or more persons. The overlaid bar chart shows incidence by the estimated date of infection. Full article ">Figure 4
Comparison of event-based models of the effective reproduction number. Unfilled circles represent estimates based on the 5-day piecewise constant model. The solid line shows the full event-based model accounting for all changes in R(t) on days 31, 39, 43, 46, 94, 162, 165, 186 and 196. The dotted line represents an alternative model ignoring any intervention dates (i.e., days 31, 39, 43 and 46) during the first wave. The dashed line represents another alternative model ignoring intervention dates (days 162 and 165) during the second wave. Full article ">Figure A1
Joinpoint analysis during the first wave of coronavirus disease 2019 in Osaka, Japan. Closed squares indicate the effective reproductive number. The colored line indicates the regression line linking identified joinpoints. Blue arrows represent starting days of the four key interventions during the first wave. Day 0 corresponds to 24 February 2020. Full article ">Figure A2
Joinpoint analysis during the second wave of coronavirus disease 2019 in Osaka, Japan. Closed squares indicate the effective reproductive number. The colored line indicates the regression line linking identified joinpoints. Blue arrows represent starting days of two key interventions during the second wave. Day 0 corresponds to 16 June 2020. Full article ">

14 pages, 1540 KiB

Open AccessArticle

Optimal Allocation of the Limited COVID-19 Vaccine Supply in South Korea

by Eunha Shim

J. Clin. Med. 2021, 10(4), 591; https://doi.org/10.3390/jcm10040591 - 4 Feb 2021

Cited by 60 | Viewed by 6744

Abstract

Initial supply of the coronavirus disease (COVID-19) vaccine may be limited, necessitating its effective use. Herein, an age-structured model of COVID-19 spread in South Korea is parameterized to understand the epidemiological characteristics of COVID-19. The model determines optimal vaccine allocation for minimizing infections, [...] Read more.

Initial supply of the coronavirus disease (COVID-19) vaccine may be limited, necessitating its effective use. Herein, an age-structured model of COVID-19 spread in South Korea is parameterized to understand the epidemiological characteristics of COVID-19. The model determines optimal vaccine allocation for minimizing infections, deaths, and years of life lost while accounting for population factors, such as country-specific age distribution and contact structure, and various levels of vaccine efficacy. A transmission-blocking vaccine should be prioritized in adults aged 20–49 years and those older than 50 years to minimize the cumulative incidence and mortality, respectively. A strategy to minimize years of life lost involves the vaccination of adults aged 40–69 years, reflecting the relatively high case-fatality rates and years of life lost in this age group. An incidence-minimizing vaccination strategy is highly sensitive to vaccine efficacy, and vaccines with lower efficacy should be administered to teenagers and adults aged 50–59 years. Consideration of age-specific contact rates and vaccine efficacy is critical to optimize vaccine allocation. New recommendations for COVID-19 vaccines under consideration by the Korean Centers for Disease Control and Prevention are mainly based on a mortality-minimizing allocation strategy. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Movement of individuals between disease and vaccination status. Full article ">Figure 2
Number of vaccines optimally distributed to each age group from the 51 million available vaccine doses (i.e., doses to vaccinate 50% of the South Korean population) for each outcome measure. Full article ">Figure 3
Comparison of outcome measures resulting from the allocation of 51 million vaccine doses for three different optimal vaccination strategies: to minimize the incidence, the number of deaths, and years of life lost (YLL). Full article ">Figure 4
Proportion of individuals vaccinated from each age group (x-axis) to minimize the total number of infections under various vaccine coverage levels (y-axis) and vaccine efficacies (50%, 70%, 90%, and 95%). Full article ">Figure 5
Proportion of individuals vaccinated from each age group (x-axis) to minimize the total number of deaths associated with COVID-19 under various vaccine coverage levels (y-axis) and vaccine efficacies (50%, 70%, 90%, and 95%). Full article ">Figure 6
Proportion of individuals vaccinated from each age group (x-axis) to minimize the years of life lost under various vaccine coverage levels (y-axis) and vaccine efficacies (50%, 70%, 90%, and 95%). Full article ">Figure 7
Impact of lower basic reproduction number on the proportion of individuals vaccinated from each age group (x-axis) to minimize three healthcare measures (i.e., total number of infections, total number of deaths, and the years of life lost) under various vaccine coverage levels (y-axis). Vaccine efficacy of 90% and a basic reproduction number (R0) of 1.5 are assumed. Full article ">

8 pages, 909 KiB

Open AccessArticle

“Go To Travel” Campaign and Travel-Associated Coronavirus Disease 2019 Cases: A Descriptive Analysis, July–August 2020

by Asami Anzai and Hiroshi Nishiura

J. Clin. Med. 2021, 10(3), 398; https://doi.org/10.3390/jcm10030398 - 21 Jan 2021

Cited by 50 | Viewed by 88705

Abstract

The Japanese government initiated the Go To Travel campaign on 22 July 2020, offering deep discounts on hotel charges and issuing coupons to be used for any consumption at travel destinations in Japan. In the present study, we aimed to describe the possible [...] Read more.

The Japanese government initiated the Go To Travel campaign on 22 July 2020, offering deep discounts on hotel charges and issuing coupons to be used for any consumption at travel destinations in Japan. In the present study, we aimed to describe the possible epidemiological impact of the tourism campaign on increasing travel-associated cases of coronavirus disease 2019 (COVID-19) in the country. We compared the incidence rates of travel-associated and tourism-related cases prior to and during the campaign. The incidence of travel-associated COVID-19 cases during the tourism campaign was approximately three times greater than the control period 22 June to 21 July 2020 and approximately 1.5 times greater than the control period of 15 to 19 July. The incidence owing to tourism was approximately 8 times and 2–3 times greater than the control periods of 22 June to 21 July and 15 to 19 July, respectively. Although the second epidemic wave in Japan had begun to decline by mid-August, enhanced domestic tourism may have contributed to increasing travel-associated COVID-19 cases during 22 to 26 July, the early stage of the Go To Travel campaign. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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18 pages, 2131 KiB

Open AccessArticle

Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial

by Hoyt Burdick, Carson Lam, Samson Mataraso, Anna Siefkas, Gregory Braden, R. Phillip Dellinger, Andrea McCoy, Jean-Louis Vincent, Abigail Green-Saxena, Gina Barnes, Jana Hoffman, Jacob Calvert, Emily Pellegrini and Ritankar Das

J. Clin. Med. 2020, 9(12), 3834; https://doi.org/10.3390/jcm9123834 - 26 Nov 2020

Cited by 8 | Viewed by 4372

Abstract

Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this [...] Read more.

Therapeutic agents for the novel coronavirus disease 2019 (COVID-19) have been proposed, but evidence supporting their use is limited. A machine learning algorithm was developed in order to identify a subpopulation of COVID-19 patients for whom hydroxychloroquine was associated with improved survival; this population might be relevant for study in a clinical trial. A pragmatic trial was conducted at six United States hospitals. We enrolled COVID-19 patients that were admitted between 10 March and 4 June 2020. Treatment was not randomized. The study endpoint was mortality; discharge was a competing event. Hazard ratios were obtained on the entire population, and on the subpopulation indicated by the algorithm as suitable for treatment. A total of 290 patients were enrolled. In the subpopulation that was identified by the algorithm, hydroxychloroquine was associated with a statistically significant (p = 0.011) increase in survival (adjusted hazard ratio 0.29, 95% confidence interval (CI) 0.11–0.75). Adjusted survival among the algorithm indicated patients was 82.6% in the treated arm and 51.2% in the arm not treated. No association between treatment and mortality was observed in the general population. A 31% increase in survival at the end of the study was observed in a population of COVID-19 patients that were identified by a machine learning algorithm as having a better outcome with hydroxychloroquine treatment. Precision medicine approaches may be useful in identifying a subpopulation of COVID-19 patients more likely to be proven to benefit from hydroxychloroquine treatment in a clinical trial. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Patient inclusion flowchart. Full article ">Figure 2
Adjusted survival curves comparing those treated and untreated with hydroxychloroquine among (A) those identified as suitable for treatment by the algorithm and (B) the full study population. Full article ">Figure 3
Hazard ratio of death comparing those treated and untreated with hydroxychloroquine across pre-defined subgroups. Abbreviations: HR: Heart rate. RR: Respiratory rate. SAPS: Simplified Acute Physiology Score. SIRS: Systemic Inflammatory Response Syndrome. WBC: white blood cell count. Full article ">Figure A1
Distribution of hydroxychloroquine initiation time among (A) patients indicated by the machine learning algorithm and (B) patients not-indicated by the machine learning algorithm. Full article ">Figure A2
Distribution of propensity of treatment scores among those treated and not treated with hydroxychloroquine. Full article ">Figure A3
Distribution of inverse probability of treatment weighting (IPTW) weights among those treated and not treated with hydroxychloroquine. Full article ">Figure A4
Adjusted survival curves comparing those treated and untreated with hydroxychloroquine among those identified as not suitable for treatment by the algorithm. Full article ">Figure A5
Feature Importance of algorithm inputs. Longitudinal data are used in the algorithm and thus the subscript indicates the time from which the feature was derived. For example, Lactate0 is the lactate measurement from the time at which the algorithm is applied, Lactate1 is the lactate measurement from the hour before the algorithm is applied, and so on. Δ denotes change from the previous hour of measurement. For example, Δ RespRate0 is the change in respiratory rate from the hour before the algorithm is applied to the time the algorithm is applied. Abbreviations used: BUN: blood urea nitrogen. DBP: diastolic blood pressure. HR: heart rate. O2 Sat: Oxygen Saturation. RespRate: Respiratory Rate. SBP: systolic blood pressure. Temp: temperature. WBC: white blood cell. Full article ">

14 pages, 1359 KiB

Open AccessArticle

Persistence of Anti-SARS-CoV-2 Antibodies in Non-Hospitalized COVID-19 Convalescent Health Care Workers

by Margherita Bruni, Valentina Cecatiello, Angelica Diaz-Basabe, Georgia Lattanzi, Erika Mileti, Silvia Monzani, Laura Pirovano, Francesca Rizzelli, Clara Visintin, Giuseppina Bonizzi, Marco Giani, Marialuisa Lavitrano, Silvia Faravelli, Federico Forneris, Flavio Caprioli, Pier Giuseppe Pelicci, Gioacchino Natoli, Sebastiano Pasqualato, Marina Mapelli and Federica Facciotti

J. Clin. Med. 2020, 9(10), 3188; https://doi.org/10.3390/jcm9103188 - 1 Oct 2020

Cited by 61 | Viewed by 11845

Abstract

Although antibody response to SARS-CoV-2 can be detected early during the infection, several outstanding questions remain to be addressed regarding the magnitude and persistence of antibody titer against different viral proteins and their correlation with the strength of the immune response. An ELISA [...] Read more.

Although antibody response to SARS-CoV-2 can be detected early during the infection, several outstanding questions remain to be addressed regarding the magnitude and persistence of antibody titer against different viral proteins and their correlation with the strength of the immune response. An ELISA assay has been developed by expressing and purifying the recombinant SARS-CoV-2 Spike Receptor Binding Domain (RBD), Soluble Ectodomain (Spike), and full length Nucleocapsid protein (N). Sera from healthcare workers affected by non-severe COVID-19 were longitudinally collected over four weeks, and compared to sera from patients hospitalized in Intensive Care Units (ICU) and SARS-CoV-2-negative subjects for the presence of IgM, IgG and IgA antibodies as well as soluble pro-inflammatory mediators in the sera. Non-hospitalized subjects showed lower antibody titers and blood pro-inflammatory cytokine profiles as compared to patients in Intensive Care Units (ICU), irrespective of the antibodies tested. Noteworthy, in non-severe COVID-19 infections, antibody titers against RBD and Spike, but not against the N protein, as well as pro-inflammatory cytokines decreased within a month after viral clearance. Thus, rapid decline in antibody titers and in pro-inflammatory cytokines may be a common feature of non-severe SARS-CoV-2 infection, suggesting that antibody-mediated protection against re-infection with SARS-CoV-2 is of short duration. These results suggest caution in using serological testing to estimate the prevalence of SARS-CoV-2 infection in the general population. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Figure 1
SARS-CoV-2 specific antibody levels of severe and mild COVID-19 patients. (A) Coomassie-stained SDS-PAGE showing receptor binding domain (RBD), Spike and Nucleocapsid (N) purified recombinant proteins used in the ELISA assays. (B–D) IgM, IgG and IgA levels in the sera of healthy subjects (light blue symbols, n = 58), non-hospitalized COVID-19 (blue symbols, n = 19) and intensive care unit (ICU) COVID-19 (dark blue symbols, n = 24) patients in ELISA assays against the RBD (B), the Spike ectodomain (C) and the N (D) SARS-CoV-2 viral proteins. (E) Differences in IgM, IgG and IgA antibody titers against RBD, Spike soluble and N protein in ICU (black bars) and non-hospitalized COVID-19 patients (white bars). p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), p < 0.0001 (****) were regarded as statistically significant. Full article ">Figure 2
Cytokine levels in sera of COVID19 patients. (A) Cytokines significantly different between ICU (dark blue symbols) and non-hospitalized (blue symbols) COVID-19 patients. (B) Cytokines not significantly different between ICU (dark blue symbols) and non-hospitalized (blue symbols) COVID-19 patients (C) Chemokines levels in sera of patients (ICU, dark blue, not hospitalized blue symbols, healthy subjects light blue). p < 0.05 (*), p < 0.01 (**) p < 0.001 (***), p < 0.0001 (****) were regarded as statistically significant. ns, not significant. Full article ">Figure 3
Longitudinal evaluation of antibody titers and cytokines in non-hospitalized COVID-19 patients (against all viral antigens). (A) Schematic representation of the study (B–D) IgM, IgG and IgA levels in the sera of non-hospitalized COVID-19 patients immediately (T1) and one month after (T2) cessation of viral detection by PCR, in the ELISA assays against the RBD (B), the Spike (C) and the N (D) SARS-CoV-2 viral proteins. (E) Cumulative fold decrease between T1 and T2 antibody titers in ELISA assays against the RBD (squares), the Spike ectodomain (circles) and the N (triangles) SARS-CoV-2 viral proteins. (F) Longitudinal variation of serum cytokines and chemokines in non-hospitalized COVID-19 patients. Statistical significance was calculated using Kruskal–Wallis nonparametric test for multiple comparisons. p < 0.05 (*), p < 0.01 (**) p < 0.001 (***) were regarded as statistically significant. ns, not significant. Full article ">

9 pages, 1578 KiB

Open AccessArticle

Containment, Contact Tracing and Asymptomatic Transmission of Novel Coronavirus Disease (COVID-19): A Modelling Study

by Ryo Kinoshita, Asami Anzai, Sung-mok Jung, Natalie M. Linton, Takeshi Miyama, Tetsuro Kobayashi, Katsuma Hayashi, Ayako Suzuki, Yichi Yang, Andrei R. Akhmetzhanov and Hiroshi Nishiura

J. Clin. Med. 2020, 9(10), 3125; https://doi.org/10.3390/jcm9103125 - 27 Sep 2020

Cited by 23 | Viewed by 5436

Abstract

When a novel infectious disease emerges, enhanced contact tracing and isolation are implemented to prevent a major epidemic, and indeed, they have been successful for the control of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which have been greatly [...] Read more.

When a novel infectious disease emerges, enhanced contact tracing and isolation are implemented to prevent a major epidemic, and indeed, they have been successful for the control of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which have been greatly reduced without causing a global pandemic. Considering that asymptomatic and pre-symptomatic infections are substantial for the novel coronavirus disease (COVID-19), the feasibility of preventing the major epidemic has been questioned. Using a two-type branching process model, the present study assesses the feasibility of containing COVID-19 by computing the probability of a major epidemic. We show that if there is a substantial number of asymptomatic transmissions, cutting chains of transmission by means of contact tracing and case isolation would be very challenging without additional interventions, and in particular, untraced cases contribute to lowering the feasibility of containment. Even if isolation of symptomatic cases is conducted swiftly after symptom onset, only secondary transmissions after the symptom onset can be prevented. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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10 pages, 837 KiB

Open AccessArticle

Hospital Caseload Demand in the Presence of Interventions during the COVID-19 Pandemic: A Modeling Study

by Katsuma Hayashi, Taishi Kayano, Sumire Sorano and Hiroshi Nishiura

J. Clin. Med. 2020, 9(10), 3065; https://doi.org/10.3390/jcm9103065 - 23 Sep 2020

Cited by 6 | Viewed by 3163

Abstract

A surge in hospital admissions was observed in Japan in late March 2020, and the incidence of coronavirus disease (COVID-19) temporarily reduced from March to May as a result of the closure of host and hostess clubs, shortening the opening hours of bars [...] Read more.

A surge in hospital admissions was observed in Japan in late March 2020, and the incidence of coronavirus disease (COVID-19) temporarily reduced from March to May as a result of the closure of host and hostess clubs, shortening the opening hours of bars and restaurants, and requesting a voluntary reduction of contact outside the household. To prepare for the second wave, it is vital to anticipate caseload demand, and thus, the number of required hospital beds for admitted cases and plan interventions through scenario analysis. In the present study, we analyzed the first wave data by age group so that the age-specific number of hospital admissions could be projected for the second wave. Because the age-specific patterns of the epidemic were different between urban and other areas, we analyzed datasets from two distinct cities: Osaka, where the cases were dominated by young adults, and Hokkaido, where the older adults accounted for the majority of hospitalized cases. By estimating the exponential growth rates of cases by age group and assuming probable reductions in those rates under interventions, we obtained projected epidemic curves of cases in addition to hospital admissions. We demonstrated that the longer our interventions were delayed, the higher the peak of hospital admissions. Although the approach relies on a simplistic model, the proposed framework can guide local government to secure the essential number of hospital beds for COVID-19 cases and formulate action plans. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Figure 1
Comparison between the observed and predicted incidence of COVID-19 in Osaka and Hokkaido, 2020. Dots represent the observed incidence by the date of illness onset, whereas the solid line represents the fitted curves using maximum likelihood estimation and the dashed lines represent the 95% credible intervals derived from the parametric bootstrapping method; (A) Osaka Prefecture and (B) Hokkaido. Hokkaido was ahead of other prefectures in experiencing the first wave from early February. Following the comparison across all age groups on the top, comparisons among children (second from the top), young adults (third from the top) and older adults (bottom) are shown. Full article ">Figure 2
Reconstructed epidemic dynamics for working-age centered and elderly centered prefectures. (A) Working-age centered model, as parameterized by datasets in Osaka. The peak of hospital admissions of young adults was higher than that of older people. (B) Elderly centered model, as parameterized by Hokkaido data. The prevalence of hospital admissions of the elderly exceeded that of young adults. In both scenarios, we assumed that all confirmed cases were to be hospitalized for a fixed duration of 14 days. Full article ">Figure 3
Intervention delay and scale of damage of the outbreak. (A–C) Dotted curves represent the number of cases by the date of infection (i.e., incidence of infection). Solid curves represent the number of cases by the date of reporting. The time point at which the solid line crosses the vertical and horizontal lines is when the threshold, that is, 2.5 per 100,000 people, is satisfied. The time point at which the vertical and horizontal lines cross is when the daily incidence is below 0.5 per 100,000. (A,D) One day after the threshold. (B,E) Three days after the threshold. (C,F) Seven days after the threshold. Full article ">

9 pages, 923 KiB

Open AccessArticle

A Decision Aide for the Risk Stratification of GU Cancer Patients at Risk of SARS-CoV-2 Infection, COVID-19 Related Hospitalization, Intubation, and Mortality

by Dara J. Lundon, Brian D. Kelly, Devki Shukla, Damien M. Bolton, Peter Wiklund and Ash Tewari

J. Clin. Med. 2020, 9(9), 2799; https://doi.org/10.3390/jcm9092799 - 30 Aug 2020

Cited by 7 | Viewed by 3037

Abstract

Treatment decisions for both early and advanced genitourinary (GU) malignancies take into account the risk of dying from the malignancy as well as the risk of death due to other causes such as other co-morbidities. COVID-19 is a new additional and immediate risk [...] Read more.

Treatment decisions for both early and advanced genitourinary (GU) malignancies take into account the risk of dying from the malignancy as well as the risk of death due to other causes such as other co-morbidities. COVID-19 is a new additional and immediate risk to a patient’s morbidity and mortality and there is a need for an accurate assessment as to the potential impact on of this syndrome on GU cancer patients. The aim of this work was to develop a risk tool to identify GU cancer patients at risk of diagnosis, hospitalization, intubation, and mortality from COVID-19. A retrospective case showed a series of GU cancer patients screened for COVID-19 across the Mount Sinai Health System (MSHS). Four hundred eighty-four had a GU malignancy and 149 tested positive for SARS-CoV-2. Demographic and clinical variables of >38,000 patients were available in the institutional database and were utilized to develop decision aides to predict a positive SARS-CoV-2 test, as well as COVID-19-related hospitalization, intubation, and death. A risk tool was developed using a combination of machine learning methods and utilized BMI, temperature, heart rate, respiratory rate, blood pressure, and oxygen saturation. The risk tool for predicting a diagnosis of SARS-CoV-2 had an AUC of 0.83, predicting hospitalization for management of COVID-19 had an AUC of 0.95, predicting patients requiring intubation had an AUC of 0.97, and for predicting COVID-19-related death, the risk tool had an AUC of 0.79. The models had an acceptable calibration and provided a superior net benefit over other common strategies across the entire range of threshold probabilities. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Figure 1
Discriminative ability of each model in the hold-out dataset of patients with GU cancer (N = 97) to predict: (A) A positive SARS-CoV-2 test, (B) hospitalization, (C) intubation, and (D) death. The diagonal line at 45° represents the performance of a coin toss in discerning the respective outcome, from a non-event. The developed risk tools have a good discriminative ability demonstrating an AUC of 0.83 (95% CI, 0.72–0.94) for the prediction of a positive SARS-CoV-2 test (A), 0.95 (95% CI, 0.90–0.99) for predicting the risk of hospitalization (B), 0.97 (95% CI, 0.94–1) for the prediction of intubation and 0.79 (95% CI, 0.63–0.96) for the prediction of death in patients in this cohort. Full article ">Figure 2
Calibration curves of each model in the hold-out dataset of patients with GU cancer (N = 97) to demonstrate the reliability of each developed risk tool to predict: (A) A positive SARS-CoV-2 test, (B) hospitalization, (C) intubation, and (D) death. The diagonal line at 45° represents perfect calibration: when the predicted probability of an event perfectly matches the proportion of observed events; as the outcomes here are 0 and 1; Loess smoothing was used to estimate the observed probabilities of the outcome in relation to the predicted probabilities. The developed risk tools generally have an acceptable reliability as assessed by calibration plots: where there is over prediction of a positive SARS-CoV-2 test at predicted values <50% (A) and the risk of intubation (C) while the models to predict hospitalization and death are well calibrated in this hold-out data subset of this cohort. Full article ">Figure 3
Decision curve analysis of each model demonstrating the superior net benefit of each over other strategies in predicting (A) a positive SARS-CoV-2 test, (B) hospitalization, (C) intubation, and (D) death. The horizontal black line at Y = 0 represents the net benefit of doing nothing, the curved grey line represents the net benefit of treating everyone while the red line represents the net benefit derived from the a strategy of employing the respective risk calculators. Full article ">

11 pages, 268 KiB

Open AccessArticle

Head-to-Head Accuracy Comparison of Three Commercial COVID-19 IgM/IgG Serology Rapid Tests

by Diego O. Andrey, Patrick Cohen, Benjamin Meyer, Giulia Torriani, Sabine Yerly, Lena Mazza, Adrien Calame, Isabelle Arm-Vernez, Idris Guessous, Silvia Stringhini, Pascale Roux-Lombard, Lionel Fontao, Thomas Agoritsas, Jerôme Stirnemann, Jean-Luc Reny, Claire-Anne Siegrist, Isabella Eckerle, Laurent Kaiser and Nicolas Vuilleumier

J. Clin. Med. 2020, 9(8), 2369; https://doi.org/10.3390/jcm9082369 - 24 Jul 2020

Cited by 28 | Viewed by 6117

Abstract

Background: Comparative data of SARS-CoV-2 IgM/IgG serology rapid diagnostic tests (RDTs) is scarce. We thus performed a head-to-head comparison of three RDTs. Methods: In this unmatched case-control study, blood samples from 41 RT-PCR-confirmed COVID-19 cases and 50 negative controls were studied. The diagnostic [...] Read more.

Background: Comparative data of SARS-CoV-2 IgM/IgG serology rapid diagnostic tests (RDTs) is scarce. We thus performed a head-to-head comparison of three RDTs. Methods: In this unmatched case-control study, blood samples from 41 RT-PCR-confirmed COVID-19 cases and 50 negative controls were studied. The diagnostic accuracy of three commercially available COVID-19 RDTs: NTBIO (RDT-A), Orient-Gene (RDT-B), and MEDsan (RDT-C), against both a recombinant spike-expressing immunofluorescence assay (rIFA) and Euroimmun IgG ELISA, was assessed. RDT results concordant with the reference methods, and between whole blood and plasma, were established by the Kendall coefficient. Results: COVID-19 cases’ median time from RT-PCR to serology was 22 days (interquartile range (IQR) 13–31 days). Whole-blood IgG detection with RDT-A, -B, and -C showed 0.93, 0.83, and 0.98 concordance with rIFA. Against rIFA, RDT-A sensitivity (SN) was 92% (95% CI: 78–98) and specificity (SP) 100% (95% CI: 91–100), RDT-B showed 87% SN (95% CI: 72–95) and 98% SP (95% CI: 88–100), and RDT-C 100% SN (95% CI: 88–100) and 98% SP (95% CI: 88–100). Against ELISA, SN and SP were above 90% for all three RDTs. Conclusions: RDT-A and RDT-C displayed IgG detection SN and SP above 90% in whole blood. These RDTs could be considered in the absence of routine diagnostic serology facilities. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

11 pages, 2845 KiB

Open AccessArticle

Global Comparison of Changes in the Number of Test-Positive Cases and Deaths by Coronavirus Infection (COVID-19) in the World

by Akihiro Hisaka, Hideki Yoshioka, Hiroto Hatakeyama, Hiromi Sato, Yoshihiro Onouchi and Naohiko Anzai

J. Clin. Med. 2020, 9(6), 1904; https://doi.org/10.3390/jcm9061904 - 18 Jun 2020

Cited by 14 | Viewed by 5492

Abstract

Global differences in changes in the numbers of population-adjusted daily test-positive cases (NPDP) and deaths (NPDD) by COVID-19 were analyzed for 49 countries, including developed and developing countries. The changes as a proportion of national population were compared, adjusting by the beginning of [...] Read more.

Global differences in changes in the numbers of population-adjusted daily test-positive cases (NPDP) and deaths (NPDD) by COVID-19 were analyzed for 49 countries, including developed and developing countries. The changes as a proportion of national population were compared, adjusting by the beginning of test-positive cases increase (BPI) or deaths increase (BDI). Remarkable regional differences of more than 100-fold in NPDP and NPDD were observed. The trajectories of NPDD after BDI increased exponentially within 20 days in most countries. Machine learning analysis suggested that NPDD on 30 days after BDI was the highest in developed Western countries (1180 persons per hundred million), followed by countries in the Middle East (128), Latin America (97), and Asia (7). Furthermore, in Western countries with positive rates of the PCR test of less than 7.0%, the increase in NPDP was slowing-down two weeks after BPI, and subsequent NPDD was only 15% compared with those with higher positive rates, which suggested that the situation of testing might have affected the velocity of COVID-19 spread. The causes behind remarkable differences between regions possibly include genetic factors of inhabitants because distributions of the race and of the observed infection increasing rates were in good agreement globally. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Graphical abstract
Full article ">Figure 1
Time courses of NPDP and NPDD after BPI by novel coronavirus diseases 2019 (COVID-19) in Asian countries (except for the Middle East. Panel (A) and non-Asian countries (Panel (B)). The upper dotted lines and lower solid lines represent NPDP and NPDD, respectively. The colors of NPDP and NPDD correspond to each country. Lines in very light gray indicate lines in non-Asian and Asian countries in Panels A and B, respectively, for comparison. Refer to <a href="#jcm-09-01904-f002" class="html-fig">Figure 2</a> and <a href="#jcm-09-01904-f003" class="html-fig">Figure 3</a> to identify each country. NPDP: number of population-adjusted daily test-positive cases. NPDD: number of population-adjusted daily deaths. BPI: beginning of the test-positive cases increase. Full article ">Figure 2
Time courses of NPDP and NPDD by COVID-19 after BPI in Asian countries (Panels (A–L)), the Middle East (Panels (M–P)), Oceania (Panel (Q)), South Africa (Panel (R)), and Latin America (Panels (S–Y). Numbers in red represent the positive rate of the PCR test. The countries are placed in decreasing order of the positive rate in each region. The upper and lower bold lines in each panel are NPDP and NPDD, respectively, except for Vietnam where no death has been recorded. The scales in all panels are the same as those in <a href="#jcm-09-01904-f001" class="html-fig">Figure 1</a>. *: Positive rate in Guangdong. NA: Not available. NPDP: number of population-adjusted daily test-positive cases. NPDD: number of population-adjusted daily deaths. BPI: beginning of the test-positive cases increase. Full article ">Figure 3
Time courses of NPDP and NPDD after BPI by COVID-19 in countries in Europe and North America (Panels (A–X)). Numbers in red represent the positive rate of the PCR test. The countries are placed in decreasing order of positive rate. The upper and lower bold lines in each panel are NPDP and NPDD, respectively. The scales in all panels are the same as those in <a href="#jcm-09-01904-f001" class="html-fig">Figure 1</a>. NA: Not available. NPDP: number of population-adjusted daily test-positive cases. NPDD: number of population-adjusted daily deaths. BPI: beginning of the test-positive cases increase. Full article ">Figure 4
Estimation of the time course of NPDP after BPI in Western countries by machine learning analysis classified by the positive rate of the PCR test. Green, orange, and purple lines indicate positive rates of 0.0–6.9%, 7.0–16.9%, and 17.0–28.0%, respectively. Each colored area represents a 5–95% confidence interval of the median estimated by bootstrap analysis. The orange and purple lines and areas completely overlapped. NPDP: number of population-adjusted daily test-positive cases. BPI: beginning of the test-positive cases increase. PCR: Polymerase chain reaction. Full article ">Figure 5
Time courses of NPDD after BDI by COVID-19. Green, red, blue, and purple lines represent Asia, the Middle East, Latin America, and Western countries, respectively. Some noticeable countries are labeled but refer to <a href="#jcm-09-01904-f002" class="html-fig">Figure 2</a> and <a href="#jcm-09-01904-f003" class="html-fig">Figure 3</a> to identify each country. NPDD: number of population-adjusted daily deaths. BPI: beginning of the test-positive cases increase. Full article ">Figure 6
Estimation of the time course of NPDD after BDI by machine learning analysis classified by the global region (Panel (A)) and by the positive rate of the PCR test (Panel (B)). In Panel (A), yellow-green, red, blue, and purple lines indicate countries in Asia (excluding the Middle East), the Middle East, Latin America, and Western (Europe, Oceania, and North America), respectively. In Panel (B), only Western countries were analyzed. Green, orange, and blue lines indicate the positive rates of 0.0–6.9%, 7.0–16.9%, and 17.0–28.0%, respectively. Each colored area represents a 5–95% confidence interval of the median estimated by bootstrap analysis. NPDD: number of population-adjusted daily deaths. BPI: beginning of the test-positive cases increase. Full article ">

9 pages, 1862 KiB

Open AccessArticle

Estimating the Risk of COVID-19 Death during the Course of the Outbreak in Korea, February–May 2020

by Eunha Shim, Kenji Mizumoto, Wongyeong Choi and Gerardo Chowell

J. Clin. Med. 2020, 9(6), 1641; https://doi.org/10.3390/jcm9061641 - 29 May 2020

Cited by 27 | Viewed by 4441

Abstract

Background: In Korea, a total of 10,840 confirmed cases of COVID-19 including 256 deaths have been recorded as of May 9, 2020. The time-delay adjusted case fatality risk (CFR) of COVID-19 in Korea is yet to be estimated. Methods: We obtained the daily [...] Read more.

Background: In Korea, a total of 10,840 confirmed cases of COVID-19 including 256 deaths have been recorded as of May 9, 2020. The time-delay adjusted case fatality risk (CFR) of COVID-19 in Korea is yet to be estimated. Methods: We obtained the daily series of confirmed cases and deaths in Korea reported prior to May 9, 2020. Using statistical methods, we estimated the time-delay adjusted risk for death from COVID-19 in Daegu, Gyeongsangbuk-do, other regions in Korea, as well as the entire country. Results: Our model-based crude CFR fitted the observed data well throughout the course of the epidemic except for the very early stage in Gyeongsangbuk-do; this was partially due to the reporting delay. Our estimates of the risk of death in Gyeongsangbuk-do reached 25.9% (95% Credible Interval (CrI): 19.6%–33.6%), 20.8% (95% CrI: 18.1%–24.0%) in Daegu, and 1.7% (95% CrI: 1.1%–2.5%) in other regions, whereas the national estimate was 10.2% (95% CrI: 9.0%–11.5%). Conclusions: The latest estimates of CFR of COVID-19 in Korea are considerably high, even with the early implementation of public health interventions including widespread testing, social distancing, and delayed school openings. Geographic differences in the CFR are likely influenced by clusters tied to hospitals and nursing homes. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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6 pages, 1192 KiB

Open AccessArticle

Early Phylogenetic Diversification of SARS-CoV-2: Determination of Variants and the Effect on Epidemiology, Immunology, and Diagnostics

by Rene Kaden

J. Clin. Med. 2020, 9(6), 1615; https://doi.org/10.3390/jcm9061615 - 26 May 2020

Cited by 9 | Viewed by 3362

Abstract

The phylogenetic clustering of 95 SARS-CoV-2 sequences from the first 3 months of the pandemic reveals insights into the early evolution of the virus and gives first indications of how the variants are globally distributed. Variants might become a challenge in terms of [...] Read more.

The phylogenetic clustering of 95 SARS-CoV-2 sequences from the first 3 months of the pandemic reveals insights into the early evolution of the virus and gives first indications of how the variants are globally distributed. Variants might become a challenge in terms of diagnostics, immunology, and effectiveness of drugs. All available whole genome sequence data from the NCBI database (March 16, 2020) were phylogenetically analyzed, and gene prediction as well as analysis of selected variants were performed. Antigenic regions and the secondary protein structure were predicted for selected variants. While some clusters are presenting the same variant with 100% identical bases, other SARS-CoV-2 lineages show a beginning diversification and phylogenetic clustering due to base substitutions and deletions in the genomes. First molecular epidemiological investigations are possible with the results by adding metadata as travelling history to the presented data. The advantage of variants in source tracing can be a challenge in terms of virulence, immune response, and immunological memory. Variants of viruses often show differences in virulence or antigenicity. This must also be considered in decisions like herd immunity. Diagnostic methods might not work if the variations or deletions are in target regions for the detection of the pathogen. One base substitution was detected in a primer binding site. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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19 pages, 2529 KiB

Open AccessArticle

The COVID-19 Infection in Italy: A Statistical Study of an Abnormally Severe Disease

by Giuseppe De Natale, Valerio Ricciardi, Gabriele De Luca, Dario De Natale, Giovanni Di Meglio, Antonio Ferragamo, Vito Marchitelli, Andrea Piccolo, Antonio Scala, Renato Somma, Emanuele Spina and Claudia Troise

J. Clin. Med. 2020, 9(5), 1564; https://doi.org/10.3390/jcm9051564 - 21 May 2020

Cited by 46 | Viewed by 10301

Abstract

We statistically investigate the Coronavirus Disease 19 (COVID-19) pandemic, which became particularly invasive in Italy in March 2020. We show that the high apparent lethality or case fatality ratio (CFR) observed in Italy, as compared with other countries, is likely biased by a [...] Read more.

We statistically investigate the Coronavirus Disease 19 (COVID-19) pandemic, which became particularly invasive in Italy in March 2020. We show that the high apparent lethality or case fatality ratio (CFR) observed in Italy, as compared with other countries, is likely biased by a strong underestimation of the number of infection cases. To give a more realistic estimate of the lethality of COVID-19, we use the actual (March 2020) estimates of the infection fatality ratio (IFR) of the pandemic based on the minimum observed CFR and analyze data obtained from the Diamond Princess cruise ship, a good representation of a “laboratory” case-study from an isolated system in which all the people have been tested. From such analyses, we derive more realistic estimates of the real extent of the infection as well as more accurate indicators of how fast the infection propagates. We then isolate the dominant factors causing the abnormal severity of the disease in Italy. Finally, we use the death count—the only data estimated to be reliable enough—to predict the total number of people infected and the interval of time when the infection in Italy could end. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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18 pages, 1354 KiB

Open AccessArticle

An Efficient COVID-19 Prediction Model Validated with the Cases of China, Italy and Spain: Total or Partial Lockdowns?

by Samuel Sanchez-Caballero, Miguel A. Selles, Miguel A. Peydro and Elena Perez-Bernabeu

J. Clin. Med. 2020, 9(5), 1547; https://doi.org/10.3390/jcm9051547 - 20 May 2020

Cited by 32 | Viewed by 6978

Abstract

The present work develops an accurate prediction model of the COVID-19 pandemic, capable not only of fitting data with a high regression coefficient but also to predict the overall infections and the infection peak day as well. The model is based on the [...] Read more.

The present work develops an accurate prediction model of the COVID-19 pandemic, capable not only of fitting data with a high regression coefficient but also to predict the overall infections and the infection peak day as well. The model is based on the Verhulst equation, which has been used to fit the data of the COVID-19 spread in China, Italy, and Spain. This model has been used to predict both the infection peak day, and the total infected people in Italy and Spain. With this prediction model, the overall infections, the infection peak, and date can accurately be predicted one week before they occur. According to the study, the infection peak took place on 23 March in Italy, and on 29 March in Spain. Moreover, the influence of the total and partial lockdowns has been studied, without finding any meaningful difference in the disease spread. However, the infected population, and the rate of new infections at the start of the lockdown, seem to play an important role in the infection spread. The developed model is not only an important tool to predict the disease spread, but also gives some significant clues about the main factors that affect to the COVID-19 spread, and quantifies the effects of partial and total lockdowns as well. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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9 pages, 405 KiB

Open AccessArticle

Comparison of the Characteristics and Outcomes of Coronavirus Disease 2019 in Different Types of Family Infections in Taiwan

by Shih-Feng Liu, Nai-Ying Kuo and Ho-Chang Kuo

J. Clin. Med. 2020, 9(5), 1527; https://doi.org/10.3390/jcm9051527 - 19 May 2020

Cited by 3 | Viewed by 2656

Abstract

Background: There were some family infections of coronavirus disease 2019 (COVID-19) in Taiwan to date. This study aimed to investigate the clinical characteristics and outcomes of different types of family infections with COVID-19 and to share Taiwan’s experience. Material and methods: We collected [...] Read more.

Background: There were some family infections of coronavirus disease 2019 (COVID-19) in Taiwan to date. This study aimed to investigate the clinical characteristics and outcomes of different types of family infections with COVID-19 and to share Taiwan’s experience. Material and methods: We collected cases of family infections of COVID-19 from 21 January 2020 to 16 March 2020. The data were collected from a series of press conference contents by Taiwan’s Central Epidemic Command Center (CECC). Results: During this period, there were six family infections in Taiwan, including two couple infections, one imported family cluster infection, and three domestic family cluster infections. Compared to the former two, the starters (cases 19, 24, and 27) of domestic family cluster infections showed longer symptom-onset to diagnosis (p = 0.02); longer symptom-onset to quarantine or isolation (p = 0.01); higher first-generation reproduction number (p = 0.03); and more critical presentation (endotracheal tube insertion and intensive care unit (ICU) care) (p < 0.01). In addition, compared to the former two, the starters of the latter were older, had no history of travel, and had more underlying diseases and more mortality. There are more contacts of domestic family cluster infections, making epidemiological investigations more difficult and expensive. However, the second-generation reproduction number of the above three families was zero. Conclusion: Domestic family cluster infections of COVID-19 have different characteristics and outcomes from couple infection and imported family cluster infections in this study. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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11 pages, 1893 KiB

Open AccessArticle

Forecasting COVID-19-Associated Hospitalizations under Different Levels of Social Distancing in Lombardy and Emilia-Romagna, Northern Italy: Results from an Extended SEIR Compartmental Model

by Chiara Reno, Jacopo Lenzi, Antonio Navarra, Eleonora Barelli, Davide Gori, Alessandro Lanza, Riccardo Valentini, Biao Tang and Maria Pia Fantini

J. Clin. Med. 2020, 9(5), 1492; https://doi.org/10.3390/jcm9051492 - 15 May 2020

Cited by 36 | Viewed by 5262

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) was identified in Wuhan, China, in December 2019. As of 17 April 2020, more than 2 million cases of COVID-19 have been reported worldwide. Northern Italy is one of the world’s centers of active coronavirus cases. [...] Read more.

The outbreak of coronavirus disease 2019 (COVID-19) was identified in Wuhan, China, in December 2019. As of 17 April 2020, more than 2 million cases of COVID-19 have been reported worldwide. Northern Italy is one of the world’s centers of active coronavirus cases. In this study, we predicted the spread of COVID-19 and its burden on hospital care under different conditions of social distancing in Lombardy and Emilia-Romagna, the two regions of Italy most affected by the epidemic. To do this, we used a Susceptible-Exposed-Infectious-Recovered (SEIR) deterministic model, which encompasses compartments relevant to public health interventions such as quarantine. A new compartment

L

was added to the model for isolated infected population, i.e., individuals tested positives that do not need hospital care. We found that in Lombardy restrictive containment measures should be prolonged at least until early July to avoid a resurgence of hospitalizations; on the other hand, in Emilia-Romagna the number of hospitalized cases could be kept under a reasonable amount with a higher contact rate. Our results suggest that territory-specific forecasts under different scenarios are crucial to enhance or take new containment measures during the epidemic. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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9 pages, 2213 KiB

Open AccessArticle

Estimation of Unreported Novel Coronavirus (SARS-CoV-2) Infections from Reported Deaths: A Susceptible–Exposed–Infectious–Recovered–Dead Model

by Andrea Maugeri, Martina Barchitta, Sebastiano Battiato and Antonella Agodi

J. Clin. Med. 2020, 9(5), 1350; https://doi.org/10.3390/jcm9051350 - 5 May 2020

Cited by 41 | Viewed by 5357

Abstract

In the midst of the novel coronavirus (SARS-CoV-2) epidemic, examining reported case data could lead to biased speculations and conclusions. Indeed, estimation of unreported infections is crucial for a better understanding of the current emergency in China and in other countries. In this [...] Read more.

In the midst of the novel coronavirus (SARS-CoV-2) epidemic, examining reported case data could lead to biased speculations and conclusions. Indeed, estimation of unreported infections is crucial for a better understanding of the current emergency in China and in other countries. In this study, we aimed to estimate the unreported number of infections in China prior to the 23 January 2020 restrictions. To do this, we developed a Susceptible–Exposed–Infectious–Recovered–Dead (SEIRD) model that estimated unreported infections from the reported number of deaths. Our approach relied on the fact that observed deaths were less likely to be affected by ascertainment biases than reported infections. Interestingly, we estimated that the basic reproductive number (R₀) was 2.43 (95%CI = 2.42–2.44) at the beginning of the epidemic and that 92.9% (95%CI = 92.5%–93.1%) of total cases were not reported. Similarly, the proportion of unreported new infections by day ranged from 52.1% to 100%, with a total of 91.8% (95%CI = 91.6%–92.1%) of infections going unreported. Agreement between our estimates and those from previous studies proves that our approach is reliable for estimating the prevalence and incidence of undocumented SARS-CoV-2 infections. Once it has been tested on Chinese data, our model could be applied to other countries with different surveillance and testing policies. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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The employed Susceptible–Exposed–Infectious–Recovered–Dead (SEIRD) epidemic model for SARS-CoV-2. β, σ; γ, and μ denote the transmission rate, infection rate, removing rate, and probability of infectious individuals dying, respectively. S, E, I, R, and D denote susceptible, exposed, infectious, recovered, and dead individuals, respectively. Full article ">Figure 2
Generic representation of SEIRD states along the temporal axis. Estimates were obtained through a SEIRD model with β, σ, γ, and μ set as 0.8, 0.3, 0.2, and 0.2, respectively (for viewing purposes only). Full article ">Figure 3
Number of reported cases and deaths in China from 31 December 2019 to 7 February 2020. The bars represent the cumulative number of reported coronavirus (SARS-CoV-2) cases and related deaths while the red line represents the case fatality risk (CFR). Full article ">Figure 4
Fitting the SEIRD model to the reported number of deaths. The dots represent the daily cumulative number of reported deaths while the lines along the temporal axis represent the estimate and 95% confidence intervals (Upper and Low Level: UL, LL) through the SEIRD model. Full article ">Figure 5
Estimated number of cases (A) and proportion of unreported events (B) from 31 December 2019 to 23 January 2020. Full article ">Figure 6
Estimated number of new infections (A) and proportion of unreported events (B) from 31 December 2019 to 23 January 2020. Full article ">

10 pages, 900 KiB

Open AccessArticle

Time from Symptom Onset to Hospitalisation of Coronavirus Disease 2019 (COVID-19) Cases: Implications for the Proportion of Transmissions from Infectors with Few Symptoms

by Robin N. Thompson, Francesca A. Lovell-Read and Uri Obolski

J. Clin. Med. 2020, 9(5), 1297; https://doi.org/10.3390/jcm9051297 - 1 May 2020

Cited by 14 | Viewed by 6787

Abstract

Interventions targeting symptomatic hosts and their contacts were successful in bringing the 2003 SARS pandemic under control. In contrast, the COVID-19 pandemic has been harder to contain, partly because of its wide spectrum of symptoms in infectious hosts. Current evidence suggests that individuals [...] Read more.

Interventions targeting symptomatic hosts and their contacts were successful in bringing the 2003 SARS pandemic under control. In contrast, the COVID-19 pandemic has been harder to contain, partly because of its wide spectrum of symptoms in infectious hosts. Current evidence suggests that individuals can transmit the novel coronavirus while displaying few symptoms. Here, we show that the proportion of infections arising from hosts with few symptoms at the start of an outbreak can, in combination with the basic reproduction number, indicate whether or not interventions targeting symptomatic hosts are likely to be effective. However, as an outbreak continues, the proportion of infections arising from hosts with few symptoms changes in response to control measures. A high proportion of infections from hosts with few symptoms after the initial stages of an outbreak is only problematic if the rate of new infections remains high. Otherwise, it can simply indicate that symptomatic transmissions are being prevented successfully. This should be considered when interpreting estimates of the extent of transmission from hosts with few COVID-19 symptoms. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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15 pages, 3846 KiB

Open AccessArticle

Master Regulator Analysis of the SARS-CoV-2/Human Interactome

by Pietro H. Guzzi, Daniele Mercatelli, Carmine Ceraolo and Federico M. Giorgi

J. Clin. Med. 2020, 9(4), 982; https://doi.org/10.3390/jcm9040982 - 1 Apr 2020

Cited by 141 | Viewed by 18117

Abstract

The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. In this paper, we [...] Read more.

The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. In this paper, we set out to shed light on the SARS-CoV-2/host receptor recognition, a crucial factor for successful virus infection. Based on the current knowledge of the interactome between SARS-CoV-2 and host cell proteins, we performed Master Regulator Analysis to detect which parts of the human interactome are most affected by the infection. We detected, amongst others, affected apoptotic and mitochondrial mechanisms, and a downregulation of the ACE2 protein receptor, notions that can be used to develop specific therapies against this new virus. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Figure 1
Representation of the predicted SARS-CoV-2/Human interactome [<a href="#B26-jcm-09-00982" class="html-bibr">26</a>] (available for download at <a href="http://korkinlab.org/wuhanDataset" target="_blank">http://korkinlab.org/wuhanDataset</a>), containing 200 unique interactions among 125 proteins (nodes). SARS-CoV-2 proteins are depicted as green circles, while human proteins are represented as squares. The color of human protein nodes reflects the integrated effect of MERS and SARS infections on the node network (see <a href="#app1-jcm-09-00982" class="html-app">Supplementary Table S2</a>) as a Normalized Enrichment Score (NES). Network visualization was performed via Cytoscape [<a href="#B49-jcm-09-00982" class="html-bibr">49</a>]. Full article ">Figure 2
Master Regulator Analysis of the 8 human proteins in the human/SARS-CoV-2 interactome and most concordantly affected by beta-coronavirus infection. The visualization was obtained through the mraplot function of the R CRAN package corto. In brief, for each analyzed network, the centroid is indicated by its gene symbol (e.g., EEF1A1, ACE2). The genes in each network (generated by the corto package from the GTEX healthy lung RNA-Seq dataset) are shown in a barcode-like diagram showing all transcriptome genes by means of their differential expression upon viral infection, from most downregulated (left) to most upregulated (right). Positively- (red) and negatively- (blue) correlated targets are overlayed on the differential expression signature as bars of a different color. Normalized Enrichment Score (NES) and p-value are also indicated. To the right, the 12 highest-likelihood network putative targets of each protein are shown, in red if upregulated, in blue if downregulated, with a pointed arrow if predicted to be activated by the centroid protein, and with a blunt arrow if predicted to be repressed. The figure shows two analyses based on the MERS infection signature (A) and on the SARS infection signature (B). Full results are available as <a href="#app1-jcm-09-00982" class="html-app">Supplementary Table S2</a>. Full article ">Figure 3
Scatterplot depicting the Normalized Enrichment Scores (NES) of the Master Regulator Analysis of human proteins interacting with SARS-CoV-2. Two analyses are compared using the signatures of MERS and SARS infection on human bronchial 2B4 cells. TMPRSS2, along with the 8 most significant proteins by absolute sum of NES, is labeled. Full results are available in <a href="#app1-jcm-09-00982" class="html-app">Supplementary Table S2</a>. Full article ">Figure 4
Cross-species analysis of the Angiotensin-converting enzyme 2 (ACE2) protein. (A) Maximum-likelihood evolutionary tree of ACE2 orthologs in selected vertebrates (numbers in the branch points indicate the % bootstrap supporting the branch structure). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. (B) A visualization of a human ACE2 crystal structure (resolution: 2.2 Å, PDB:1R42). The residues that are conserved across human, pangolin and bat are depicted in cornflower blue, and the residues that are conserved in human and pangolin but differ in bat (<a href="#app1-jcm-09-00982" class="html-app">Supplementary Table S3</a>) are depicted in opaque pink. Full article ">

18 pages, 310 KiB

Open AccessArticle

People with Suspected COVID-19 Symptoms Were More Likely Depressed and Had Lower Health-Related Quality of Life: The Potential Benefit of Health Literacy

by Hoang C. Nguyen, Minh H. Nguyen, Binh N. Do, Cuong Q. Tran, Thao T. P. Nguyen, Khue M. Pham, Linh V. Pham, Khanh V. Tran, Trang T. Duong, Tien V. Tran, Thai H. Duong, Tham T. Nguyen, Quyen H. Nguyen, Thanh M. Hoang, Kien T. Nguyen, Thu T. M. Pham, Shwu-Huey Yang, Jane C.-J. Chao and Tuyen Van Duong

J. Clin. Med. 2020, 9(4), 965; https://doi.org/10.3390/jcm9040965 - 31 Mar 2020

Cited by 394 | Viewed by 32991

Abstract

The coronavirus disease 2019 (COVID-19) epidemic affects people’s health and health-related quality of life (HRQoL), especially in those who have suspected COVID-19 symptoms (S-COVID-19-S). We examined the effect of modifications of health literacy (HL) on depression and HRQoL. A cross-sectional study was conducted [...] Read more.

The coronavirus disease 2019 (COVID-19) epidemic affects people’s health and health-related quality of life (HRQoL), especially in those who have suspected COVID-19 symptoms (S-COVID-19-S). We examined the effect of modifications of health literacy (HL) on depression and HRQoL. A cross-sectional study was conducted from 14 February to 2 March 2020. 3947 participants were recruited from outpatient departments of nine hospitals and health centers across Vietnam. The interviews were conducted using printed questionnaires including participants’ characteristics, clinical parameters, health behaviors, HL, depression, and HRQoL. People with S-COVID-19-S had a higher depression likelihood (OR, 2.88; p < 0.001), lower HRQoL-score (B, −7.92; p < 0.001). In comparison to people without S-COVID-19-S and low HL, those with S-COVID-19-S and low HL had 9.70 times higher depression likelihood (p < 0.001), 20.62 lower HRQoL-score (p < 0.001), for the people without S-COVID-19-S, 1 score increment of HL resulted in 5% lower depression likelihood (p < 0.001) and 0.45 higher HRQoL-score (p < 0.001), while for those people with S-COVID-19-S, 1 score increment of HL resulted in a 4% lower depression likelihood (p = 0.004) and 0.43 higher HRQoL-score (p < 0.001). People with S-COVID-19-S had a higher depression likelihood and lower HRQoL than those without. HL shows a protective effect on depression and HRQoL during the epidemic. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

7 pages, 335 KiB

Open AccessArticle

A Simulation on Potential Secondary Spread of Novel Coronavirus in an Exported Country Using a Stochastic Epidemic SEIR Model

by Kentaro Iwata and Chisato Miyakoshi

J. Clin. Med. 2020, 9(4), 944; https://doi.org/10.3390/jcm9040944 - 30 Mar 2020

Cited by 48 | Viewed by 16835

Abstract

Ongoing outbreak of pneumonia caused by novel coronavirus (2019-nCoV) began in December 2019 in Wuhan, China, and the number of new patients continues to increase. Even though it began to spread to many other parts of the world, such as other Asian countries, [...] Read more.

Ongoing outbreak of pneumonia caused by novel coronavirus (2019-nCoV) began in December 2019 in Wuhan, China, and the number of new patients continues to increase. Even though it began to spread to many other parts of the world, such as other Asian countries, the Americas, Europe, and the Middle East, the impact of secondary outbreaks caused by exported cases outside China remains unclear. We conducted simulations to estimate the impact of potential secondary outbreaks in a community outside China. Simulations using stochastic SEIR model were conducted, assuming one patient was imported to a community. Among 45 possible scenarios we prepared, the worst scenario resulted in the total number of persons recovered or removed to be 997 (95% CrI 990–1000) at day 100 and a maximum number of symptomatic infectious patients per day of 335 (95% CrI 232–478). Calculated mean basic reproductive number (R₀) was 6.5 (Interquartile range, IQR 5.6–7.2). However, better case scenarios with different parameters led to no secondary cases. Altering parameters, especially time to hospital visit. could change the impact of a secondary outbreak. With these multiple scenarios with different parameters, healthcare professionals might be able to better prepare for this viral infection. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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7 pages, 488 KiB

Open AccessArticle

Prediction of the Epidemic Peak of Coronavirus Disease in Japan, 2020

by Toshikazu Kuniya

J. Clin. Med. 2020, 9(3), 789; https://doi.org/10.3390/jcm9030789 - 13 Mar 2020

Cited by 204 | Viewed by 37388

Abstract

The first case of coronavirus disease 2019 (COVID-19) in Japan was reported on 15 January 2020 and the number of reported cases has increased day by day. The purpose of this study is to give a prediction of the epidemic peak for COVID-19 [...] Read more.

The first case of coronavirus disease 2019 (COVID-19) in Japan was reported on 15 January 2020 and the number of reported cases has increased day by day. The purpose of this study is to give a prediction of the epidemic peak for COVID-19 in Japan by using the real-time data from 15 January to 29 February 2020. Taking into account the uncertainty due to the incomplete identification of infective population, we apply the well-known SEIR compartmental model for the prediction. By using a least-square-based method with Poisson noise, we estimate that the basic reproduction number for the epidemic in Japan is

R_{0} = 2.6

(

95 %

CI,

2.4

–

2.8

) and the epidemic peak could possibly reach the early-middle summer. In addition, we obtain the following epidemiological insights: (1) the essential epidemic size is less likely to be affected by the rate of identification of the actual infective population; (2) the intervention has a positive effect on the delay of the epidemic peak; (3) intervention over a relatively long period is needed to effectively reduce the final epidemic size. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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15 pages, 1608 KiB

Open AccessArticle

Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

by Mohammed A. A. Al-qaness, Ahmed A. Ewees, Hong Fan and Mohamed Abd El Aziz

J. Clin. Med. 2020, 9(3), 674; https://doi.org/10.3390/jcm9030674 - 2 Mar 2020

Cited by 235 | Viewed by 18363

Abstract

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. [...] Read more.

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination (

R^{2}

), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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9 pages, 618 KiB

Open AccessFeature PaperArticle

Epidemiological Identification of A Novel Pathogen in Real Time: Analysis of the Atypical Pneumonia Outbreak in Wuhan, China, 2019–2020

by Sung-mok Jung, Ryo Kinoshita, Robin N. Thompson, Natalie M. Linton, Yichi Yang, Andrei R. Akhmetzhanov and Hiroshi Nishiura

J. Clin. Med. 2020, 9(3), 637; https://doi.org/10.3390/jcm9030637 - 27 Feb 2020

Cited by 12 | Viewed by 6534

Abstract

Virological tests have now shown conclusively that a novel coronavirus is causing the 2019–2020 atypical pneumonia outbreak in Wuhan, China. We demonstrate that non-virological descriptive characteristics could have determined that the outbreak is caused by a novel pathogen in advance of virological testing. [...] Read more.

Virological tests have now shown conclusively that a novel coronavirus is causing the 2019–2020 atypical pneumonia outbreak in Wuhan, China. We demonstrate that non-virological descriptive characteristics could have determined that the outbreak is caused by a novel pathogen in advance of virological testing. Characteristics of the ongoing outbreak were collected in real time from two medical social media sites. These were compared against characteristics of eleven pathogens that have previously caused cases of atypical pneumonia. The probability that the current outbreak is due to “Disease X” (i.e., previously unknown etiology) as opposed to one of the known pathogens was inferred, and this estimate was updated as the outbreak continued. The probability (expressed as a percentage) that Disease X is driving the outbreak was assessed as over 29% on 31 December 2019, one week before virus identification. After some specific pathogens were ruled out by laboratory tests on 5 January 2020, the inferred probability of Disease X was over 49%. We showed quantitatively that the emerging outbreak of atypical pneumonia cases is consistent with causation by a novel pathogen. The proposed approach, which uses only routinely observed non-virological data, can aid ongoing risk assessments in advance of virological test results becoming available. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Figure 1
Real-time estimation of the probability that the ongoing pneumonia outbreak is driven by each candidate pathogen, given available information on different days. The probability that the outbreak is due to an unknown pathogen (Disease X) increases as more information becomes available, for two reasons: (i) the current outbreak can be seen to exhibit characteristics that are not similar to those observed in previous outbreaks, and; (ii) previously observed pathogens are ruled out by laboratory test results. Arrows indicate new information available on each date. Results are shown for different metrics describing the distance between characteristics of the ongoing outbreak and each candidate pathogen, and either including or excluding initial exposure information for the current outbreak (i.e., worked at/visited a wet market), specifically: (A) Hamming distance (the sum of squares difference between the entries in the columns of <a href="#jcm-09-00637-t001" class="html-table">Table 1</a> corresponding to the ongoing outbreak and the candidate pathogen considered) with wet market exposure; (B) Euclidean distance (the square root of the Hamming distance) with wet market exposure; (C) Hamming distance without wet market exposure; (D) Euclidean distance without wet market exposure. Dashed grey lines show the probability for every pathogen (including Disease X) if the only information included is the ruling out of different pathogens through laboratory tests (i.e., a probability of 1/(1 + number of candidate pathogens remaining on that day)). Note that the probability corresponding to different pathogens can be identical, for example, severe acute respiratory syndrome (SARS) and Mycoplasma pneumoniae were assessing as being equally likely as the causative pathogen from 30 December to 4 January, and Legionellosis and Chlamydia pneumoniae had equal probability from 30 December to 12 January (Details in <a href="#app1-jcm-09-00637" class="html-app">Supplementary Materials Table S1</a>). Full article ">

9 pages, 1402 KiB

Open AccessArticle

Assessing the Impact of Reduced Travel on Exportation Dynamics of Novel Coronavirus Infection (COVID-19)

by Asami Anzai, Tetsuro Kobayashi, Natalie M. Linton, Ryo Kinoshita, Katsuma Hayashi, Ayako Suzuki, Yichi Yang, Sung-mok Jung, Takeshi Miyama, Andrei R. Akhmetzhanov and Hiroshi Nishiura

J. Clin. Med. 2020, 9(2), 601; https://doi.org/10.3390/jcm9020601 - 24 Feb 2020

Cited by 132 | Viewed by 23531

Abstract

The impact of the drastic reduction in travel volume within mainland China in January and February 2020 was quantified with respect to reports of novel coronavirus (COVID-19) infections outside China. Data on confirmed cases diagnosed outside China were analyzed using statistical models to [...] Read more.

The impact of the drastic reduction in travel volume within mainland China in January and February 2020 was quantified with respect to reports of novel coronavirus (COVID-19) infections outside China. Data on confirmed cases diagnosed outside China were analyzed using statistical models to estimate the impact of travel reduction on three epidemiological outcome measures: (i) the number of exported cases, (ii) the probability of a major epidemic, and (iii) the time delay to a major epidemic. From 28 January to 7 February 2020, we estimated that 226 exported cases (95% confidence interval: 86,449) were prevented, corresponding to a 70.4% reduction in incidence compared to the counterfactual scenario. The reduced probability of a major epidemic ranged from 7% to 20% in Japan, which resulted in a median time delay to a major epidemic of two days. Depending on the scenario, the estimated delay may be less than one day. As the delay is small, the decision to control travel volume through restrictions on freedom of movement should be balanced between the resulting estimated epidemiological impact and predicted economic fallout. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Figure 1
Number of confirmed cases outside China by date of report. The bars measure the number of cases reported each day between 13 January and 6 February 2020. The black bars represent infections that are likely to have occurred in China while the grey bars indicate infections that are likely to have occurred outside China. Full article ">Figure 2
Observed and expected number of cases diagnosed outside China by date of report. Observed cases (dots) include those infected in China. An exponential growth curve was fitted to the observed data from 27 January 2020. The dashed lines represent the 95% confidence interval on and after 28 January 2020. Full article ">Figure 3
Probability of a major epidemic with various levels of transmissibility and traced contact. (A) The solid lines represent the probability of a major epidemic in the counterfactual scenario, i.e., based on the expected number of cases diagnosed in Japan. Dashed lines represent the probability of a major epidemic in the presence of travel volume reductions, calculated using the number of traced and untraced cases was 6 in total in Japan from Day 58 to Day 67. Contact tracing leading to isolation was assumed at three different levels: 10%, 30%, and 50%. (B) The vertical axis represents the reduced probability of a major epidemic due to travel volume reduction. The horizontal axis shows the proportion of cases traced, adopting the same scenarios as panel A. Full article ">Figure 4
Delay in the time to a major epidemic gained by travel volume reduction. The median delay is shown for Japan, using relative reduction in the probability of a major epidemic. The vertical axis represents the time delay to a major epidemic (in days), and the horizontal axis represents the proportion of contacts traced. Each shaped dot represents different values of the basic reproduction number. Full article ">

9 pages, 1630 KiB

Open AccessArticle

Short-term Forecasts of the COVID-19 Epidemic in Guangdong and Zhejiang, China: February 13–23, 2020

by Kimberlyn Roosa, Yiseul Lee, Ruiyan Luo, Alexander Kirpich, Richard Rothenberg, James M. Hyman, Ping Yan and Gerardo Chowell

J. Clin. Med. 2020, 9(2), 596; https://doi.org/10.3390/jcm9020596 - 22 Feb 2020

Cited by 152 | Viewed by 18980

Abstract

The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of [...] Read more.

The ongoing COVID-19 epidemic continues to spread within and outside of China, despite several social distancing measures implemented by the Chinese government. Limited epidemiological data are available, and recent changes in case definition and reporting further complicate our understanding of the impact of the epidemic, particularly in the epidemic’s epicenter. Here we use previously validated phenomenological models to generate short-term forecasts of cumulative reported cases in Guangdong and Zhejiang, China. Using daily reported cumulative case data up until 13 February 2020 from the National Health Commission of China, we report 5- and 10-day ahead forecasts of cumulative case reports. Specifically, we generate forecasts using a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model, which have each been previously used to forecast outbreaks due to different infectious diseases. Forecasts from each of the models suggest the outbreaks may be nearing extinction in both Guangdong and Zhejiang; however, the sub-epidemic model predictions also include the potential for further sustained transmission, particularly in Zhejiang. Our 10-day forecasts across the three models predict an additional 65–81 cases (upper bounds: 169–507) in Guangdong and an additional 44–354 (upper bounds: 141–875) cases in Zhejiang by February 23, 2020. In the best-case scenario, current data suggest that transmission in both provinces is slowing down. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Figure 1
Forecasting results of 5- and 10-day ahead estimates of cumulative reported case counts for Guangdong, China, generated on 13 February 2020. The dots are the mean estimates for each model, and the hinge lines represent the 95% prediction intervals. Full article ">Figure 2
Forecasting results of 5- and 10-day ahead estimates of cumulative reported case counts for Zhejiang, China, generated on 13 February 2020. The dots are the mean estimates for each model, and the hinge lines represent the 95% prediction intervals. Full article ">Figure 3
Ten-day ahead logistic growth model forecasts of cumulative reported COVID-19 cases in Guangdong and Zhejiang, China, generated on 13 February 2020. The blue circles correspond to the cumulative cases reported up until 13 February 2020; the solid red lines correspond to the mean model solution; the dashed red lines depict the 95% prediction intervals; and the black vertical dashed line separates the calibration and forecasting periods. Full article ">Figure 4
Ten-day ahead Richards model forecasts of cumulative reported COVID-19 cases in Guangdong and Zhejiang, China, generated on 13 February 2020. The blue circles correspond to the cumulative cases reported up until 13 February 2020; the solid red lines correspond to the mean model solution; the dashed red lines depict the 95% prediction intervals; and the black vertical dashed line separates the calibration and forecasting periods. Full article ">Figure 5
Ten-day ahead sub-epidemic model forecasts of cumulative reported COVID-19 cases in Guangdong and Zhejiang, China, generated on 13 February 2020. The blue circles correspond to the cumulative cases reported up until 13 February 2020; the solid red lines correspond to the mean model solution; the dashed red lines depict the 95% prediction intervals; and the black vertical dashed line separates the calibration and forecasting periods. Full article ">

12 pages, 1235 KiB

Open AccessArticle

Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China

by Péter Boldog, Tamás Tekeli, Zsolt Vizi, Attila Dénes, Ferenc A. Bartha and Gergely Röst

J. Clin. Med. 2020, 9(2), 571; https://doi.org/10.3390/jcm9020571 - 19 Feb 2020

Cited by 203 | Viewed by 50754

Abstract

We developed a computational tool to assess the risks of novel coronavirus outbreaks outside of China. We estimate the dependence of the risk of a major outbreak in a country from imported cases on key parameters such as: (i) the evolution of the [...] Read more.

We developed a computational tool to assess the risks of novel coronavirus outbreaks outside of China. We estimate the dependence of the risk of a major outbreak in a country from imported cases on key parameters such as: (i) the evolution of the cumulative number of cases in mainland China outside the closed areas; (ii) the connectivity of the destination country with China, including baseline travel frequencies, the effect of travel restrictions, and the efficacy of entry screening at destination; and (iii) the efficacy of control measures in the destination country (expressed by the local reproduction number

R_{loc}

). We found that in countries with low connectivity to China but with relatively high

R_{loc}

, the most beneficial control measure to reduce the risk of outbreaks is a further reduction in their importation number either by entry screening or travel restrictions. Countries with high connectivity but low

R_{loc}

benefit the most from policies that further reduce

R_{loc}

. Countries in the middle should consider a combination of such policies. Risk assessments were illustrated for selected groups of countries from America, Asia, and Europe. We investigated how their risks depend on those parameters, and how the risk is increasing in time as the number of cases in China is growing. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Figure 1
Final epidemic sizes in China, outside Hubei, with <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>2.1</mn> <mo>,</mo> <mn>2.6</mn> <mo>,</mo> <mn>3.1</mn> </mrow> </semantics></math>, as a function of the time when the control function <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> reaches its maximum (in days after 23 January). Rapid implementation of the control generates much smaller case numbers. The inset shows the estimations of the ascertainment rate for the week 25–31, with average <math display="inline"><semantics> <mrow> <mn>0.063</mn> </mrow> </semantics></math>, based on the ratio of confirmed cases and the maximum likelihood estimates of the case numbers from exportation. Full article ">Figure 2
(Left) Risk of major outbreaks as a function of cumulative number of cases in selected countries, assuming <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>loc</mi> </msub> <mo>=</mo> <mn>1.6</mn> </mrow> </semantics></math> and baseline connectivity to China. Other countries in South America, including Mexico, are inside the green shaded area. (Right) The effects of reductions of imported case numbers (either by travel restriction or entry screening) in the USA and Canada, assuming <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>loc</mi> </msub> <mo>=</mo> <mn>1.4</mn> </mrow> </semantics></math>. Full article ">Figure 3
Outbreak risks for highly connected countries in Asia. Thailand and the Republic of Korea are plotted; the curves for Japan and Taiwan are in between them. (Left) We plot the risk vs. the efficacy of prevented importations when the cumulative number of cases reaches 150,000. (Right) C = 600,000. Black parts of the curves represent situations when the four countries are indistinguishable. Full article ">Figure 4
Selected European countries with high, medium, and low connectivity to China. (Left) The outbreak risk is plotted assuming their baseline connectivity <math display="inline"><semantics> <mi>θ</mi> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>loc</mi> </msub> <mo>=</mo> <mn>1.4</mn> </mrow> </semantics></math> for each country, as the cumulative number of cases is increasing. A significant reduction in the risks can be observed (Right), where we reduced <math display="inline"><semantics> <msub> <mi>R</mi> <mi>loc</mi> </msub> </semantics></math> to 1.1 and assumed a 50% reduction in importations. Full article ">Figure 5
Heatmap of the outbreak risks as functions of <math display="inline"><semantics> <mi>θ</mi> </semantics></math> and <math display="inline"><semantics> <msub> <mi>R</mi> <mi>loc</mi> </msub> </semantics></math>, when C = 200,000. The arrows show the directions corresponding to the largest reductions in the risk. Full article ">

9 pages, 931 KiB

Open AccessArticle

Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data

by Natalie M. Linton, Tetsuro Kobayashi, Yichi Yang, Katsuma Hayashi, Andrei R. Akhmetzhanov, Sung-mok Jung, Baoyin Yuan, Ryo Kinoshita and Hiroshi Nishiura

J. Clin. Med. 2020, 9(2), 538; https://doi.org/10.3390/jcm9020538 - 17 Feb 2020

Cited by 848 | Viewed by 74294

Abstract

The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the [...] Read more.

The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2–14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3–4 days without truncation and at 5–9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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10 pages, 969 KiB

Open AccessFeature PaperArticle

Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases

by Sung-mok Jung, Andrei R. Akhmetzhanov, Katsuma Hayashi, Natalie M. Linton, Yichi Yang, Baoyin Yuan, Tetsuro Kobayashi, Ryo Kinoshita and Hiroshi Nishiura

J. Clin. Med. 2020, 9(2), 523; https://doi.org/10.3390/jcm9020523 - 14 Feb 2020

Cited by 266 | Viewed by 61218

Abstract

The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand [...] Read more.

The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Figure 1
Estimates of the mean and standard deviation (SD) of the time from illness onset to reporting and death cases, accounting for right truncation, with novel coronavirus (COVID-19) infection in China, 2020. Inference of A and B was conducted among (A) exported cases observed in other countries and (B) deceased cases in China. (A) Frequency distribution of the time from illness onset to reporting among exported cases employing a gamma distribution with a mean of 7.1 days (95% confidence interval [CI]: 5.9, 8.4) and SD of 4.4 days (95% CI: 3.5, 5.7). (B) Frequency distribution of the time from illness onset to death with a mean of 19.9 days (95% CI: 14.9, 29.0) (shown in black) and SD of 11.4 days (95% CI: 6.5, 21.6) employing a lognormal distribution and accounting for right truncation. For reference, the estimate of the mean and its 95% credible intervals without accounting for right truncation are shown in grey. The values for distribution of time from illness onset to death are adopted from an earlier study [<a href="#B14-jcm-09-00523" class="html-bibr">14</a>]. The blue bars show empirically observed data collected from governmental reports (as of 24 January 2020). Full article ">Figure 2
Cumulative incidence and the confirmed case fatality risk of the novel coronavirus (COVID-19) outbreak in China, 2020. (A,B) Observed and estimated cumulative number of cases in China by the date of report. An exponential growth curve was extrapolated using the exported case data. Scenario 1 extrapolated the exponential growth from December to first case on 8 December 2019, while Scenario 2 started the estimation of the exponential growth only from 13 January 2020. The black line and shaded area represent median and 95% credible interval of the cumulative incidence in China, respectively. The blue bars show the cumulative number of reported cases from the government of mainland China. The cumulative number of reported cases was not used for fitting, but it was shown for comparison between the cumulative number of reported and estimated cases in China. There is a decrease in the cumulative number of reported cases in early January, because only 41 cases tested positive for the novel coronavirus among the reported 59 cases on 10 January 2020. Left-top panels on both A and B show the cumulative numbers of exported cases observed in other countries and the cumulative number of deaths in China, represented by dark and light green bars, respectively. (C,D) Confirmed case fatality risk (cCFR) by the date of reporting. Each value of cCFR was estimated as the ratio of cumulative number of estimated incidence to death at time t. The points and error bars represent the median and its 95% credible interval of the cCFR. All 95% credible intervals were derived from Markov chain Monte Carlo simulations. Full article ">Figure 3
Basic reproduction number of novel coronavirus (COVID-19) infections in China, 2020. Black lines and grey shades represent the median and 95% credible intervals of the basic reproduction number. Panel A shows the result of Scenario 1, in which an exponential growth started from the assumed illness onset date of index case, while Panel B shows the result from exponential growth from the first exported case (Scenario 2). The 95% credible intervals were derived from Markov chain Monte Carlo method. Full article ">

8 pages, 694 KiB

Open AccessFeature PaperArticle

Novel Coronavirus Outbreak in Wuhan, China, 2020: Intense Surveillance Is Vital for Preventing Sustained Transmission in New Locations

by Robin N. Thompson

J. Clin. Med. 2020, 9(2), 498; https://doi.org/10.3390/jcm9020498 - 11 Feb 2020

Cited by 129 | Viewed by 17320

Abstract

The outbreak of pneumonia originating in Wuhan, China, has generated 24,500 confirmed cases, including 492 deaths, as of 5 February 2020. The virus (2019-nCoV) has spread elsewhere in China and to 24 countries, including South Korea, Thailand, Japan and USA. Fortunately, there has [...] Read more.

The outbreak of pneumonia originating in Wuhan, China, has generated 24,500 confirmed cases, including 492 deaths, as of 5 February 2020. The virus (2019-nCoV) has spread elsewhere in China and to 24 countries, including South Korea, Thailand, Japan and USA. Fortunately, there has only been limited human-to-human transmission outside of China. Here, we assess the risk of sustained transmission whenever the coronavirus arrives in other countries. Data describing the times from symptom onset to hospitalisation for 47 patients infected early in the current outbreak are used to generate an estimate for the probability that an imported case is followed by sustained human-to-human transmission. Under the assumptions that the imported case is representative of the patients in China, and that the 2019-nCoV is similarly transmissible to the SARS coronavirus, the probability that an imported case is followed by sustained human-to-human transmission is 0.41 (credible interval [0.27, 0.55]). However, if the mean time from symptom onset to hospitalisation can be halved by intense surveillance, then the probability that an imported case leads to sustained transmission is only 0.012 (credible interval [0, 0.099]). This emphasises the importance of current surveillance efforts in countries around the world, to ensure that the ongoing outbreak will not become a global pandemic. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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The probability of a self-sustaining outbreak driven by human-to-human transmission arising following the importation of one infected individual. (A) Data describing the number of days between symptom onset and hospitalisation for 47 patients in the ongoing outbreak [<a href="#B15-jcm-09-00498" class="html-bibr">15</a>]. (B) The estimated distribution of times between symptom onset and hospitalisation, obtained by fitting to the data shown in panel A. Blue lines show a range of equally possible distributions (see Methods; 50 distributions are shown here, selected at random from the n = 100,000 distributions considered), and the red line shows the average of the n = 100,000 distributions. (C) The probability of sustained transmission for each possible distribution of times from symptom onset to hospitalisation (Equation (1); blue histogram) and the probability of sustained transmission obtained by integrating over the possible distributions (Equation (2); red line). (D) The probability that a single imported case leads to sustained transmission in a new location, for different surveillance levels. The red line shows the mean estimates (obtained using Equation (2) but extended to account for intensified surveillance), and the blue dotted lines show the 5th and 95th percentile estimates (obtained when Equation (1) is applied, but extended to account for intensified surveillance). Full article ">Figure 2
The probability of a self-sustaining outbreak driven by human-to-human transmission arising from multiple independent cases imported to a new location, under different surveillance levels. (A) No intensification of surveillance (<math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>). (B) Medium level of surveillance intensification (<math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math> ). (C) High level of surveillance intensification (<math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> ). The grey bars and red dots show the mean estimates (obtained using Equation (4)), and the error bars indicate the 5th and 95th percentile estimates obtained when Equation (3) is applied. Full article ">

13 pages, 3442 KiB

Open AccessFeature PaperArticle

Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions

by Biao Tang, Xia Wang, Qian Li, Nicola Luigi Bragazzi, Sanyi Tang, Yanni Xiao and Jianhong Wu

J. Clin. Med. 2020, 9(2), 462; https://doi.org/10.3390/jcm9020462 - 7 Feb 2020

Cited by 1058 | Viewed by 41583

Abstract

Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the [...] Read more.

Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71–7.23). Sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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(A) Cumulative diagnoses and revised case data (dataRev1) in mainland China, the blue curve is the best fitting curve of model (1) to dataRev1. (B) Data information of cumulative quarantined/released population. Full article ">Figure 2
Diagram of the model adopted in the study for simulating the novel coronavirus (2019-nCoV) infection. Interventions including intensive contact tracing followed by quarantine and isolation are indicated. Full article ">Figure 3
Sensitivity analyses with respect to contact rate, c (A,B), and quarantine rate, q (C,D), on the log number of infected individuals and cumulative reported cases. Full article ">Figure 4
Contour plot of R_c, with the parameter of baseline transmission probability and the contact rate, c (A), or the quarantine rate, q (B). (B) shows that a higher transmission probability of the virus will significantly increase the basic reproduction number. Full article ">Figure 5
Heat-map showing the spreading of the Coronavirus infection. Full article ">Figure 6
The effects of no travel restrictions (A) versus travel restriction (B) in the Hubei Province on the Coronavirus disease in Beijing city. Full article ">

6 pages, 518 KiB

Open AccessFeature PaperArticle

Estimating the Unreported Number of Novel Coronavirus (2019-nCoV) Cases in China in the First Half of January 2020: A Data-Driven Modelling Analysis of the Early Outbreak

by Shi Zhao, Salihu S. Musa, Qianying Lin, Jinjun Ran, Guangpu Yang, Weiming Wang, Yijun Lou, Lin Yang, Daozhou Gao, Daihai He and Maggie H. Wang

J. Clin. Med. 2020, 9(2), 388; https://doi.org/10.3390/jcm9020388 - 1 Feb 2020

Cited by 330 | Viewed by 33520

Abstract

Background: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from [...] Read more.

Background: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R₀, of 2019-nCoV. Methods: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R₀. Results: We confirmed that the initial growth phase followed an exponential growth pattern. The under-reporting was likely to have resulted in 469 (95% CI: 403–540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18–25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R₀ of 2019-nCoV at 2.56 (95% CI: 2.49–2.63). Conclusion: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Review

Jump to: Editorial, Research

12 pages, 447 KiB

Open AccessReview

Frailty and COVID-19: A Systematic Scoping Review

by Giuseppe Maltese, Andrea Corsonello, Mirko Di Rosa, Luca Soraci, Cristiana Vitale, Francesco Corica and Fabrizia Lattanzio

J. Clin. Med. 2020, 9(7), 2106; https://doi.org/10.3390/jcm9072106 - 4 Jul 2020

Cited by 88 | Viewed by 11534

Abstract

Older people have paid a huge toll in terms of mortality during the coronavirus disease-19 (COVID-19) pandemic. Frailty may have contributed to the vulnerability of older people to more severe clinical presentation. We aimed at reviewing available evidence about frailty and COVID-19. We [...] Read more.

Older people have paid a huge toll in terms of mortality during the coronavirus disease-19 (COVID-19) pandemic. Frailty may have contributed to the vulnerability of older people to more severe clinical presentation. We aimed at reviewing available evidence about frailty and COVID-19. We searched PUBMED, Web of Science, and EMBASE from 1 December 2019 to 29 May 2020. Study selection and data extraction were performed by three independent reviewers. Qualitative synthesis was conducted and quantitative data extracted when available. Forty papers were included: 13 editorials, 15 recommendations/guidelines, 3 reviews, 1 clinical trial, 6 observational studies, 2 case reports. Editorials and reviews underlined the potential clinical relevance of assessing frailty among older patients with COVID-19. However, frailty was only investigated in regards to its association with overall mortality, hospital contagion, intensive care unit admission rates, and disease phenotypes in the few observational studies retrieved. Specific interventions in relation to frailty or its impact on COVID-19 treatments have not been evaluated yet. Even with such limited evidence, clinical recommendations on the use of frailty tools have been proposed to support decision making about escalation plan. Ongoing initiatives are expected to improve knowledge of COVID-19 interaction with frailty and to promote patient-centered approaches. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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19 pages, 1352 KiB

Open AccessReview

No Autopsies on COVID-19 Deaths: A Missed Opportunity and the Lockdown of Science

by Monica Salerno, Francesco Sessa, Amalia Piscopo, Angelo Montana, Marco Torrisi, Federico Patanè, Paolo Murabito, Giovanni Li Volti and Cristoforo Pomara

J. Clin. Med. 2020, 9(5), 1472; https://doi.org/10.3390/jcm9051472 - 14 May 2020

Cited by 92 | Viewed by 14389

Abstract

Background: The current outbreak of COVID-19 infection, which started in Wuhan, Hubei province, China, in December 2019, is an ongoing challenge and a significant threat to public health requiring surveillance, prompt diagnosis, and research efforts to understand a new, emergent, and unknown pathogen [...] Read more.

Background: The current outbreak of COVID-19 infection, which started in Wuhan, Hubei province, China, in December 2019, is an ongoing challenge and a significant threat to public health requiring surveillance, prompt diagnosis, and research efforts to understand a new, emergent, and unknown pathogen and to develop effective therapies. Despite the increasing number of published studies on COVID-19, in all the examined studies the lack of a well-defined pathophysiology of death among patients who died following COVID-19 infection is evident. Autopsy should be considered mandatory to define the exact cause of death, thus providing useful clinical and epidemiologic information as well as pathophysiological insights to further provide therapeutic tools. Methods: A literature review was performed on PubMed database, using the key terms: “COVID-19”, “nCov 19”, and “Sars Cov 2”. 9709 articles were retrieved; by excluding all duplicated articles, additional criteria were then applied: articles or abstracts in English and articles containing one of the following words: “death”, “died”, “comorbidity”, “cause of death”, “biopsy”, “autopsy”, or “pathological”. Results: A total of 50 articles met the inclusion criteria. However, only 7 of these studies reported autopsy-based data. Discussion: The analysis of the main data from the selected studies concerns the complete analysis of 12,954 patients, of whom 2269 died (with a mortality rate of 17.52%). Laboratory confirmation of COVID-19 infection was obtained in all cases and comorbidities were fully reported in 46 studies. The most common comorbidities were: cardiovascular diseases (hypertension and coronary artery disease), metabolic disorders (diabetes, overweight, or obesity), respiratory disorders (chronic obstructive pulmonary disease), and cancer. The most common reported complications were: acute respiratory distress syndrome (ARDS), acute kidney injury, cardiac injury, liver insufficiency, and septic shock. Only 7 papers reported histological investigations. Nevertheless, only two complete autopsies are described and the cause of death was listed as COVID-19 in only one of them. The lack of postmortem investigation did not allow a definition of the exact cause of death to determine the pathways of this infection. Based on the few histopathological findings reported in the analyzed studies, it seems to be a clear alteration of the coagulation system: frequently prothrombotic activity with consequent thromboembolism was described in COVID-19 patients. As a scientific community, we are called on to face this global threat, and to defeat it with all the available tools necessary. Despite the improvement and reinforcement of any method of study in every field of medicine and science, encouraging the autopsy practice as a tool of investigation could also therefore, help physicians to define an effective treatment to reduce mortality. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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29 pages, 2387 KiB

Open AccessReview

The COVID-19 Pandemic: A Comprehensive Review of Taxonomy, Genetics, Epidemiology, Diagnosis, Treatment, and Control

by Yosra A. Helmy, Mohamed Fawzy, Ahmed Elaswad, Ahmed Sobieh, Scott P. Kenney and Awad A. Shehata

J. Clin. Med. 2020, 9(4), 1225; https://doi.org/10.3390/jcm9041225 - 24 Apr 2020

Cited by 461 | Viewed by 50740

Abstract

A pneumonia outbreak with unknown etiology was reported in Wuhan, Hubei province, China, in December 2019, associated with the Huanan Seafood Wholesale Market. The causative agent of the outbreak was identified by the WHO as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), producing [...] Read more.

A pneumonia outbreak with unknown etiology was reported in Wuhan, Hubei province, China, in December 2019, associated with the Huanan Seafood Wholesale Market. The causative agent of the outbreak was identified by the WHO as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), producing the disease named coronavirus disease-2019 (COVID-19). The virus is closely related (96.3%) to bat coronavirus RaTG13, based on phylogenetic analysis. Human-to-human transmission has been confirmed even from asymptomatic carriers. The virus has spread to at least 200 countries, and more than 1,700,000 confirmed cases and 111,600 deaths have been recorded, with massive global increases in the number of cases daily. Therefore, the WHO has declared COVID-19 a pandemic. The disease is characterized by fever, dry cough, and chest pain with pneumonia in severe cases. In the beginning, the world public health authorities tried to eradicate the disease in China through quarantine but are now transitioning to prevention strategies worldwide to delay its spread. To date, there are no available vaccines or specific therapeutic drugs to treat the virus. There are many knowledge gaps about the newly emerged SARS-CoV-2, leading to misinformation. Therefore, in this review, we provide recent information about the COVID-19 pandemic. This review also provides insights for the control of pathogenic infections in humans such as SARS-CoV-2 infection and future spillovers. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Genome organization of SARS CoV-2 and its encoded proteins. The orf1ab gene constitutes two-thirds of the genome, encodes a total of 16 non-structural proteins (NSPs) within the pp1ab gene, as shown in yellow, which are nsp1 (180 aa), nsp2 (638 aa), nsp3 (1945 aa), nsp4 (500 aa), nsp5 (306 aa), nsp6 (290 aa), nsp7 (83 aa), nsp8 (198 aa), nsp9 (113 aa), nsp10 (139 aa), nsp11 (13 aa), nsp12 (932 aa), nsp13 (601 aa), nsp14 (527 aa), nsp15 (346 aa), and nsp16 (298 aa). The other third of SARS CoV-2 includes four genes (in green) that encode four structural proteins (S, M, E, N), and six accessory genes (in blue) that encode six accessory proteins (orf3a, orf6, orf7a, orf7b, orf8, and orf10). Full article ">Figure 2
Phylogenetic tree based on the complete genome sequences of 45 selected coronaviruses from 18 countries including the SARS-CoV-2, SARS-CoV, HCoV, bat SARS, SARS-like CoV, and MERS-CoV. The tree was constructed in IQ-TREE using the maximum likelihood method, ModelFinder, and ultrafast bootstrap approximation (1000 replicates). The tree is drawn to scale, with branch lengths (numbers below the branches) measured in the number of substitutions per site. Branch lengths less than 0.3 are not shown. Numbers above the branches represent the percentage of replicate trees in which the associated viruses clustered together in the bootstrap test. The tree is rooted with two human coronavirus species from the genus Alphacoronavirus as an outgroup (HCoV-229E and HCoV-NL63). Full article ">Figure 3
The transmission cycle of coronaviruses including MERS-CoV, SARS-CoV, and SARSCoV-2. The transmission of the virus to humans occurs by direct contact with infected animals. The continuous line represents direct transmission. Full article ">Figure 4
Lung of a 51-year-old male patient with a history of hepatitis C and symptoms of dry cough and shortness of breathing for three weeks. No recent travel or known contacts with infected subjects. Axial (A) and coronal computed tomography (CT) (B) of chest without contrast revealed bilateral peribronchial and subpleural consolidative opacities noted throughout both lungs (green arrow). There were scattered nodular consolidative opacities in a peribronchial distribution (orange arrow). The patient tested positive for SARS-CoV-2 RNA. Full article ">

13 pages, 1043 KiB

Open AccessReview

A Systematic Review of COVID-19 Epidemiology Based on Current Evidence

by Minah Park, Alex R. Cook, Jue Tao Lim, Yinxiaohe Sun and Borame L. Dickens

J. Clin. Med. 2020, 9(4), 967; https://doi.org/10.3390/jcm9040967 - 31 Mar 2020

Cited by 393 | Viewed by 59474

Abstract

As the novel coronavirus (SARS-CoV-2) continues to spread rapidly across the globe, we aimed to identify and summarize the existing evidence on epidemiological characteristics of SARS-CoV-2 and the effectiveness of control measures to inform policymakers and leaders in formulating management guidelines, and to [...] Read more.

As the novel coronavirus (SARS-CoV-2) continues to spread rapidly across the globe, we aimed to identify and summarize the existing evidence on epidemiological characteristics of SARS-CoV-2 and the effectiveness of control measures to inform policymakers and leaders in formulating management guidelines, and to provide directions for future research. We conducted a systematic review of the published literature and preprints on the coronavirus disease (COVID-19) outbreak following predefined eligibility criteria. Of 317 research articles generated from our initial search on PubMed and preprint archives on 21 February 2020, 41 met our inclusion criteria and were included in the review. Current evidence suggests that it takes about 3-7 days for the epidemic to double in size. Of 21 estimates for the basic reproduction number ranging from 1.9 to 6.5, 13 were between 2.0 and 3.0. The incubation period was estimated to be 4-6 days, whereas the serial interval was estimated to be 4-8 days. Though the true case fatality risk is yet unknown, current model-based estimates ranged from 0.3% to 1.4% for outside China. There is an urgent need for rigorous research focusing on the mitigation efforts to minimize the impact on society. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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14 pages, 715 KiB

Open AccessReview

Novel Coronavirus Infection (COVID-19) in Humans: A Scoping Review and Meta-Analysis

by Israel Júnior Borges do Nascimento, Nensi Cacic, Hebatullah Mohamed Abdulazeem, Thilo Caspar von Groote, Umesh Jayarajah, Ishanka Weerasekara, Meisam Abdar Esfahani, Vinicius Tassoni Civile, Ana Marusic, Ana Jeroncic, Nelson Carvas Junior, Tina Poklepovic Pericic, Irena Zakarija-Grkovic, Silvana Mangeon Meirelles Guimarães, Nicola Luigi Bragazzi, Maria Bjorklund, Ahmad Sofi-Mahmudi, Mohammad Altujjar, Maoyi Tian, Diana Maria Cespedes Arcani, Dónal P. O’Mathúna and Milena Soriano Marcolino Show full author list Hide full author list

J. Clin. Med. 2020, 9(4), 941; https://doi.org/10.3390/jcm9040941 - 30 Mar 2020

Cited by 397 | Viewed by 45422

Abstract

A growing body of literature on the 2019 novel coronavirus (SARS-CoV-2) is becoming available, but a synthesis of available data has not been conducted. We performed a scoping review of currently available clinical, epidemiological, laboratory, and chest imaging data related to the SARS-CoV-2 [...] Read more.

A growing body of literature on the 2019 novel coronavirus (SARS-CoV-2) is becoming available, but a synthesis of available data has not been conducted. We performed a scoping review of currently available clinical, epidemiological, laboratory, and chest imaging data related to the SARS-CoV-2 infection. We searched MEDLINE, Cochrane CENTRAL, EMBASE, Scopus and LILACS from 01 January 2019 to 24 February 2020. Study selection, data extraction and risk of bias assessment were performed by two independent reviewers. Qualitative synthesis and meta-analysis were conducted using the clinical and laboratory data, and random-effects models were applied to estimate pooled results. A total of 61 studies were included (59,254 patients). The most common disease-related symptoms were fever (82%, 95% confidence interval (CI) 56%–99%; n = 4410), cough (61%, 95% CI 39%–81%; n = 3985), muscle aches and/or fatigue (36%, 95% CI 18%–55%; n = 3778), dyspnea (26%, 95% CI 12%–41%; n = 3700), headache in 12% (95% CI 4%–23%, n = 3598 patients), sore throat in 10% (95% CI 5%–17%, n = 1387) and gastrointestinal symptoms in 9% (95% CI 3%–17%, n = 1744). Laboratory findings were described in a lower number of patients and revealed lymphopenia (0.93 × 10⁹/L, 95% CI 0.83–1.03 × 10⁹/L, n = 464) and abnormal C-reactive protein (33.72 mg/dL, 95% CI 21.54–45.91 mg/dL; n = 1637). Radiological findings varied, but mostly described ground-glass opacities and consolidation. Data on treatment options were limited. All-cause mortality was 0.3% (95% CI 0.0%–1.0%; n = 53,631). Epidemiological studies showed that mortality was higher in males and elderly patients. The majority of reported clinical symptoms and laboratory findings related to SARS-CoV-2 infection are non-specific. Clinical suspicion, accompanied by a relevant epidemiological history, should be followed by early imaging and virological assay. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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33 pages, 854 KiB

Open AccessReview

Potential Rapid Diagnostics, Vaccine and Therapeutics for 2019 Novel Coronavirus (2019-nCoV): A Systematic Review

by Junxiong Pang, Min Xian Wang, Ian Yi Han Ang, Sharon Hui Xuan Tan, Ruth Frances Lewis, Jacinta I-Pei Chen, Ramona A Gutierrez, Sylvia Xiao Wei Gwee, Pearleen Ee Yong Chua, Qian Yang, Xian Yi Ng, Rowena K.S. Yap, Hao Yi Tan, Yik Ying Teo, Chorh Chuan Tan, Alex R. Cook, Jason Chin-Huat Yap and Li Yang Hsu

J. Clin. Med. 2020, 9(3), 623; https://doi.org/10.3390/jcm9030623 - 26 Feb 2020

Cited by 364 | Viewed by 60975

Abstract

Rapid diagnostics, vaccines and therapeutics are important interventions for the management of the 2019 novel coronavirus (2019-nCoV) outbreak. It is timely to systematically review the potential of these interventions, including those for Middle East respiratory syndrome-Coronavirus (MERS-CoV) and severe acute respiratory syndrome (SARS)-CoV, [...] Read more.

Rapid diagnostics, vaccines and therapeutics are important interventions for the management of the 2019 novel coronavirus (2019-nCoV) outbreak. It is timely to systematically review the potential of these interventions, including those for Middle East respiratory syndrome-Coronavirus (MERS-CoV) and severe acute respiratory syndrome (SARS)-CoV, to guide policymakers globally on their prioritization of resources for research and development. A systematic search was carried out in three major electronic databases (PubMed, Embase and Cochrane Library) to identify published studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Supplementary strategies through Google Search and personal communications were used. A total of 27 studies fulfilled the criteria for review. Several laboratory protocols for confirmation of suspected 2019-nCoV cases using real-time reverse transcription polymerase chain reaction (RT-PCR) have been published. A commercial RT-PCR kit developed by the Beijing Genomic Institute is currently widely used in China and likely in Asia. However, serological assays as well as point-of-care testing kits have not been developed but are likely in the near future. Several vaccine candidates are in the pipeline. The likely earliest Phase 1 vaccine trial is a synthetic DNA-based candidate. A number of novel compounds as well as therapeutics licensed for other conditions appear to have in vitro efficacy against the 2019-nCoV. Some are being tested in clinical trials against MERS-CoV and SARS-CoV, while others have been listed for clinical trials against 2019-nCoV. However, there are currently no effective specific antivirals or drug combinations supported by high-level evidence. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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Characteristics of and Public Health Responses to the Coronavirus Disease 2019 Outbreak in China

by Sheng-Qun Deng and Hong-Juan Peng

J. Clin. Med. 2020, 9(2), 575; https://doi.org/10.3390/jcm9020575 - 20 Feb 2020

Cited by 514 | Viewed by 39276

Abstract

In December 2019, cases of unidentified pneumonia with a history of exposure in the Huanan Seafood Market were reported in Wuhan, Hubei Province. A novel coronavirus, SARS-CoV-2, was identified to be accountable for this disease. Human-to-human transmission is confirmed, and this disease (named [...] Read more.

In December 2019, cases of unidentified pneumonia with a history of exposure in the Huanan Seafood Market were reported in Wuhan, Hubei Province. A novel coronavirus, SARS-CoV-2, was identified to be accountable for this disease. Human-to-human transmission is confirmed, and this disease (named COVID-19 by World Health Organization (WHO)) spread rapidly around the country and the world. As of 18 February 2020, the number of confirmed cases had reached 75,199 with 2009 fatalities. The COVID-19 resulted in a much lower case-fatality rate (about 2.67%) among the confirmed cases, compared with Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). Among the symptom composition of the 45 fatality cases collected from the released official reports, the top four are fever, cough, short of breath, and chest tightness/pain. The major comorbidities of the fatality cases include hypertension, diabetes, coronary heart disease, cerebral infarction, and chronic bronchitis. The source of the virus and the pathogenesis of this disease are still unconfirmed. No specific therapeutic drug has been found. The Chinese Government has initiated a level-1 public health response to prevent the spread of the disease. Meanwhile, it is also crucial to speed up the development of vaccines and drugs for treatment, which will enable us to defeat COVID-19 as soon as possible. Full article

(This article belongs to the Special Issue Real Time Clinical and Epidemiological Investigations on Novel Coronavirus - Part I)

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