8000 GitHub - rkmasiello/covid-resources: Ongoing list of covid-19 models, visualizations
[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to content
10000
/ covid-resources Public
forked from nudro/covid-resources

Ongoing list of covid-19 models, visualizations

0 stars 4 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

rkmasiello/covid-resources

BranchesTags
 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

18 Commits
.ipynb_checkpoints
.ipynb_checkpoints
 
 
imgs
imgs
 
 
README.md
README.md
 
 

Repository files navigation

Introduction

This is an attempt to aggregate as many covid-19 analytical resources online. Ranging from data sources, dashboards, maps, charts, algorithms, and published papers to social media channels and blog posts. If you find a resource not here, please consider contributing by reaching out to Catherine or Michael, or submitting a pull request.

Table of Contents

Datasets

Visualizations

Epi Models

Journals

Social Media

Deep Learning Models

Datasets

  1. Novel Coronavirus (COVID-19) Cases, provided by Johns Hopkins University CSSE https://github.com/CSSEGISandData/COVID-19

  2. Midas Data and Research Portal - https://github.com/midas-network/COVID-19

  1. Chinese nCov Memory - Memory of 2020 nCoV: Media Coverage, Non-fiction Writings, and Individual Narratives (Continuously updating) https://github.com/2019ncovmemory/nCovMemory, About - https://qz.com/1811018/chinese-citizens-use-github-to-save-coronavirus-memories/ here: https://2019ncovmemory.github.io/nCovMemory/

  2. Raw data in Wuhan, Hubei, and Guangzhou for serious COVID-19 cases, and Wuhan hospitalization data - https://github.com/c2-d2/COVID-19-wuhan-guangzhou-data for Ruoran, Li, Caitlin Rivers, Qi Tan, Megan B Murray, Eric Toner, and Marc Lipsitch. The Demand for Inpatient and ICU Beds for COVID-19 in the US: Lessons From Chinese Cities (March 2020). https://dash.harvard.edu/handle/1/42599304; data at https://github.com/c2-d2/COVID-19-wuhan-guangzhou-data

  3. Google Sheets From DXY.cn Google Sheets - https://docs.google.com/spreadsheets/d/1jS24DjSPVWa4iuxuD4OAXrE3QeI8c9BC1hSlqr-NMiU/edit#gid=1187587451

  4. Kaggle Dataset - https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset, Johns Hopkins University has made an excellent dashboard using the affected cases data. Data is extracted from the google sheets associated and made available here.

  5. Nextstrain - https://github.com/nextstrain/ncov - The hCoV-19 / SARS-CoV-2 genomes were generously shared via GISAID. We gratefully acknowledge the Authors, Originating and Submitting laboratories of the genetic sequence and metadata made available through GISAID on which this research is based. For a full list of attributions please see the metadata file.

  6. ECDC Download today’s data on the geographic distribution of COVID-19 cases worldwide - https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide

  7. BNO - https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/

  8. U.S. Centers for Disease Control and Prevention (CDC) - https://www.cdc.gov/media/dpk/diseases-and-conditions/coronavirus/coronavirus-2020.html

  9. Covid19 News Tracker b Scops - https://covid19.scops.ai/superset/dashboard/home/

  10. Chen, Emily, Kristina Lerman, and Emilio Ferrara. "COVID-19: The First Public Coronavirus Twitter Dataset." arXiv preprint arXiv:2003.07372 (2020). https://arxiv.org/pdf/2003.07372.pdf Github https://github.com/echen102/COVID-19-TweetIDs

  11. 2019 new coronavirus epidemic time series data warehouse (Chinese and English) - https://github.com/BlankerL/DXY-COVID-19-Data

  12. The Covid Tracking Project - https://covidtracking.com/, with real-time API: https://covidtracking.com/api/

Visualizations

Maps, Descriptive Charts, Dashboards

  1. Mapping 2019-nCoV - https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30120-1/fulltext (Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis; published online Feb 19. https://doi.org/10.1016/S1473-3099(20)30120-1),

  2. U.S. Centers for Disease Control and Prevention - https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/world-map.html

  1. Covid-19 Dashboards - https://covid19dashboards.com/, Github https://github.com/github/covid19-dashboard

  1. HealthMap alert notifications - https://healthmap.org/wuhan/

  2. HealthMap/John Brownstein Covid-19 Map - https://www.healthmap.org/covid-19/

  3. Covid-19 spread, Chinese Disease Control - http://2019ncov.chinacdc.cn/2019-nCoV/

  4. New York Times/Lai R KK, et al., Coronavirus Map: Tracking the Global Outbreak - https://www.nytimes.com/interactive/2020/world/coronavirus-maps.html

  5. European Centre for Disease Prevention and Control, https://darwinanddavis.github.io/worldmaps/coronavirus.html (Github: https://github.com/darwinanddavis/worldmaps)

  6. University of Virginia - COVID-19 Surveillance Dashboard, http://ncov.bii.virginia.edu/dashboard/

  7. University of Virginia - COVID-19 Cases and Clusters Outside of China, https://datastudio.google.com/u/0/reporting/f6ad0988-f203-45f8-8d18-5d726c1d2d8b/page/MGzDB

  8. University of Washington HGIS Lab - https://hgis.uw.edu/virus/ (Github: https://github.com/jakobzhao/virus)

  9. nCov2019 for studying COVID-19 coronavirus outbreak, Tianzhi Wu, Erqiang Hu, Patrick Tung, Xijin Ge, Guangchuang Yu - nCov2019: An R package with real-time data, historical data and Shiny app (https://guangchuangyu.github.io/nCov2019/)

  10. Dipartimento della Protezione Civile COVID-19 Italia - Monitoraggio della situazione - http://opendatadpc.maps.arcgis.com/apps/opsdashboard/index.html#/b0c68bce2cce478eaac82fe38d4138b1

  11. Esri Story Map Mapping the novel coronavirus outbreak - https://storymaps.arcgis.com/stories/4fdc0d03d3a34aa485de1fb0d2650ee0

  12. World Health Organization. Novel coronavirus (COVID-19) situation (public dashboard) - https://who.maps.arcgis.com/apps/opsdashboard/index.html#/c88e37cfc43b4ed3baf977d77e4a0667

  13. Crowdsourced Google Map by covid-2019 Reddit Map Community - https://www.google.com/maps/d/u/0/viewer?mid=1yCPR-ukAgE55sROnmBUFmtLN6riVLTu3&ll=30.359193252484147%2C0&z=2

  14. COVID19 Infodemics Observatory - https://covid19obs.fbk.eu/, CoMuNe Labs

  15. Bing COVID Tracker - https://www.bing.com/covid

  16. E-Tracking map of the #CoViD19 in Africa - http://umap.openstreetmap.fr/fr/map/e-tracking-map-of-the-covid19-in-africa_411333#3/10.13/45.34

  17. Vox - 11 coronavirus pandemic charts - https://www.vox.com/future-perfect/2020/3/12/21172040/coronavirus-covid-19-virus-charts

  18. Early Alert - https://early-alert.maps.arcgis.com/apps/opsdashboard/index.html#/20bfbf89c8e74c0494c90b1ae0fa7b78

  19. EpiRisk - link here

  20. WorldoMeters- https://www.worldometers.info/coronavirus/

  21. Covid2019app Live Site - https://covid2019app.live/

  22. Here’s how coronavirus spreads on a plane—and the safest place to sit - https://www.nationalgeographic.com/science/2020/01/how-coronavirus-spreads-on-a-plane/

  23. How Much Worse the Coronavirus Could Get, in Charts- https://www.nytimes.com/interactive/2020/03/13/opinion/coronavirus-trump-response.html, By Nicholas Kristof and Stuart A. Thompson

  24. COVID-19 Mobility Monitoring project, ISI Foundation and Cuebiq - https://covid19mm.github.io/in-progress/2020/03/13/first-report-assessment.html

Epi Models

  1. COVID-19 Growth Rate Prediction- https://covid19dashboards.com/growth-bayes/ - We assume a negative binomial likelihood as we are dealing with count data. A Poisson could also be used but the negative binomial allows us to also model the variance separately to give more flexibility. | Thomas Wiecki, @HamelHusain

  2. Estimating The Mortality Rate For COVID-19- https://covid19dashboards.com/covid-19-mortality-estimation/#Interpretation-of-Country-Level-Parameters - Using Country-Level Covariates To Correct For Testing & Reporting Biases And Estimate a True Mortality Rate. (Github model: https://github.com/jwrichar/COVID19-mortality, full analysis https://github.com/jwrichar/COVID19-mortality/blob/master/COVID-19%20Mortality%20Rate.ipynb) | @HamelHusain, @jwrichar

  3. NobBS: Nowcasting by Bayesian Smoothing - https://github.com/sarahhbellum/NobBS - NobBS is Bayesian approach to estimate the number of occurred-but-not-yet-reported cases from incomplete, time-stamped reporting data for disease outbreaks. NobBS learns the reporting delay distribution and the time evolution of the epidemic curve to produce smoothed nowcasts in both stable and time-varying case reporting settings. | sarahhbellum

  4. Scenario analysis for the transmission of COVID-19 in Georgia - http://2019-coronavirus-tracker.com/stochastic-GA.html - The epidemiology of COVID-19 in the United States is poorly understood. To better understand the potential range of epidemic outcomes in the state of Georgia, we developed a model based on data from Hubei Province, China calibrated to regionally specific conditions in Georgia and observations of the number of reported cases in Georgia in early March. Github - https://github.com/CEIDatUGA/ncov-wuhan-stochastic-model | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  5. Probability of widespread transmission - http://2019-coronavirus-tracker.com/final-size.html; Github (private) - https://github.com/CEIDatUGA/ncov-coupled-outbreaks | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  6. Spatial Spread of 2019 novel coronavirus in China - http://2019-coronavirus-tracker.com/spatial-china.html - We developed a gravity-based model to better understand the risk of spatial spread of the 2019-nCov at the prefecture level in China, and to determine the efficacy of quarantines imposed in Wuhan and other prefectures. Github (private) - https://github.com/CEIDatUGA/CoronavirusSpatial | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  7. Effect of early intervention on outbreak size of COVID-19 in China - http://2019-coronavirus-tracker.com/early-intervention.html - The epidemic of COVID-19 reached different areas of China at different times. This means that different locations were at different phases of outbreak at the time of the Wuhan lockdown (23 January) and other provincial and national actions. This provides what is sometimes called a “natural experiment” becuase it is as if replicate epidemics had been induced and then intervened on at different times. By looking at the effect of timing on outbreak size, we can draw conclusions about the effect of delaying intervention, which may be informative to other countries that are considering taking action. Github - https://github.com/CEIDatUGA/ncov-early-intervention | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  8. Effect of mass testing - http://2019-coronavirus-tracker.com/mass_testing.html - A symptom-based mass screening and testing intervention (MSTI) can identify a large fraction of infected individuals during an infectious disease outbreak. China is currently using this strategy for the COVID-19 outbreak. However, MSTI might lead to increased transmission if not properly implemented. We investigate under which conditions MSTI is beneficial. Github (private) - https://github.com/CEIDatUGA/CoV_MassTesting | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  9. Epidemic Data Curves, Maps - http://2019-coronavirus-tracker.com/data.html - Github (private) - https://github.com/CEIDatUGA/ncov-data-summary | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  10. Nowcasting the current size of the COVID-19 outbreak in the United States - http://2019-coronavirus-tracker.com/nowcast.html - At any given time, most COVID-19 cases are circulating in the community and not known to us. We wish to estimate the total current size of the COVID-19 outbreak (the total number of unnotified individuals currently infected with SARS-CoV2). Github (private) https://github.com/CEIDatUGA/ncov-nowcast, Global and US Parameters http://2019-coronavirus-tracker.com/parameters | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  11. Speed of Spread of COVID-19 - http://2019-coronavirus-tracker.com/speed-of-spread.html By US State and Global - Epidemics of COVID-19 are occuring at different times across the United States so it is important to compare the spread of an epidemic in a given state with the appropriate stage in other countries. The following figures show the cumulative number of cases in a state by number of days since the 100th case, number of days since the 1st case, and by calendar date, respectively. Github (private) https://github.com/CEIDatUGA/ncov-data-summary | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  12. COVID-19 in Context - http://2019-coronavirus-tracker.com/context.html - How does the 2019 novel coronavirus disease (COVID-19) epidemic compare in severity to other recent disease outbreaks? We gathered data from existing studies to put COVID-19 into context. Github (private) https://github.com/CEIDatUGA/ncov-context | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  13. Estimating $R_0$ and other parameters for the 2019-nCov epidemic -The epidemiology of the global 2019-nCov is poorly understood. Identifying the key processes that shape transmission and estimating the relevant model parameters is therefore an important task. This document presents arguments and analysis to support the estimation of a number of key quantities - Epidemic curve, Basic reproduction number ($R_0$), Case detection rate (q), Incubation period ($\frac{1}{\sigma}$), Lag between symptom onset and isolation, Transmissibility ($\beta$), Additional parameters; http://2019-coronavirus-tracker.com/parameters-supplement.html | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  14. Estimation of the effective reproduction number of COVID-19 outside China - http://2019-coronavirus-tracker.com/reff-outside.html - What is the average $R_{eff}$ outside of China? | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia; Github https://github.com/CEIDatUGA/ncov-Reff-outside-China

  15. COVID-19 Growth Rate Prediction - http://2019-coronavirus-tracker.com/stochastic.html - We developed a stochastic model to better understand the transmission of 2019-nCov in Hubei (primarily Wuhan). The model includes several features of the Wuhan outbreak that are absent from most compartmental models that otherwise confound the interpretation of data, including time-varying rates of case detection, patient isolation, and case notification. Github - https://github.com/CEIDatUGA/ncov-wuhan-stochastic-model, HTML http://2019-coronavirus-tracker.com/stochastic-model.html | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia

  16. Extended state-space SIR epidemiological models - https://github.com/lilywang1988/eSIR - R package eSIR: extended state-space SIR epidemiological models. The standard SIR model has three components: susceptible, infected, and removed (including the recovery and dead). In the following sections, we will introduce the other extended state-space SIR models and their implementation in the package. The results provided below are based on relatively short chains. | @lilywang1988

  17. JSON time-series of coronavirus cases (confirmed, deaths and recovered) per country - updated daily - https://github.com/pomber/covid19- Transforms the data from CSSEGISandData/COVID-19 into a json file. Available at https://pomber.github.io/covid19/timeseries.json. Updated three times a day using GitHub Actions. | @pomber

  18. Genomic epidemiology of novel coronavirus - https://nextstrain.org/ncov?c=country - Showing 838 of 838 genomes sampled between Dec 2019 and Mar 2020 | the NextStrain Team | nextstrain

  19. Phylodynamic Analysis - http://virological.org/ - Novel 2019 coronavirus category| virological

  20. Genomic epidemiology of hCoV-19 - https://www.gisaid.org/epiflu-applications/next-hcov-19-app/ - Showing 838 of 838 genomes sampled between Dec 2019 and Mar 2020.| GISAID

  21. Don’t “Flatten the Curve,” squash it!, with simulations Modeling COVID-19 Spread vs Healthcare Capacity - https://alhill.shinyapps.io/COVID19seir/?fbclid=IwAR2aXJT79M2AmZxMdy8jsiEuSC4i7ijU8Av6oB4dmlZIeJ2VQgL7Tt3QGxA - The graph shows the expected numbers of individuals over time who are infected, recovered, susceptible, or dead over time. Infected individuals first pass through an exposed/incubation phase where they are asymptomatic and not infectious, and then move into a symptomatic and infections stage classified by the clinical status of infection (mild, severe, or critical). | Alison Hill, Joscha Bach

  22. Ferguson, Neil M., et al. "Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand." Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand - https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf - Here we present the results of epidemiological modelling which has informed policymaking in the UK and other countries in recent weeks. | Imperial College COVID-19 Response Team, Neil M Ferguson et al.

  23. Li, Ruiyun, et al. "Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)." Science (2020) https://science.sciencemag.org/content/early/2020/03/13/science.abb3221 - Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. | Li, Ruiyun, et al

  24. Chan, Jasper Fuk-Woo, et al. "A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster." The Lancet 395.10223 (2020): 514-523. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30154-9/fulltext?fbclid=IwAR1YTPBtlNUrZRvcE9sSBnOzJTOUR8sVK4nc54le5k4xXF3_WvjSuKW5BBU - In this study, we report the epidemiological, clinical, laboratory, radiological, and microbiological findings of five patients in a family cluster who presented with unexplained pneumonia after returning to Shenzhen, Guangdong province, China, after a visit to Wuhan, and an additional family member who did not travel to Wuhan. | Chan, et al.

  25. R library (coronavirus) https://ramikrispin.github.io/coronavirus/ - Github repo is here https://github.com/RamiKrispin/coronavirus | @RamiKrispin

  26. Innophore protein modeling https://innophore.com/2019-ncov/ - Validating the protease sequence | Innophore

  27. Wuhan coronavirus 2019-nCoV protease homology model - https://3dprint.nih.gov/discover/3DPX-012867| - Ho 9E81 molgy model by Phyre2 of the Wuhan coronavirus 2019-nCoV protease, https://innophore.com/2019-ncov From a PDB file in the PyMol session linked in that article.| NIH

  28. Coronavirus Simulator - https://www.washingtonpost.com/graphics/2020/world/corona-simulator/, Harry Stevens, Washington Post

  29. Chinazzi, Matteo, et al. "The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak." Science (2020). - https://science.sciencemag.org/content/early/2020/03/05/science.aba9757 - Motivated by the rapid spread of COVID-19 in Mainland China, we use a global metapopulation disease transmission model to project the impact of travel limitations on the national and international spread of the epidemic. | Chinazzi, et al

  30. Li, Qun, et al. "Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia." New England Journal of Medicine (2020). - https://www.nejm.org/doi/full/10.1056/NEJMoa2001316 - We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. | Qun Li, et al

  31. Li, Ruiyun, et al. "Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19)." medRxiv (2020). - https://www.medrxiv.org/content/medrxiv/early/2020/02/17/2020.02.14.20023127.full.pdf - Estimation of the fraction and contagiousness of undocumented novel coronavirus (COVID-19) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Github - https://github.com/SenPei-CU/COVID-19 | Li, Pen, et al.

  32. Fighting Fatal Coronavirus Using Knowledge Graph - https://community.neo4j.com/t/fighting-fatal-coronavirus-using-knowledge-graph/14634 - uses neo4j http://v.we-yun.com:2020/browser/ | Zhi Zhang from we-yun.com

  33. Using Nebula graph - Detect Corona Virus Spreading With Graph Database Based on a Real Case https://nebula-graph.io/en/posts/detect-corona-virus-spreading-with-graph-database/

  34. Maier, Benjamin F., and Dirk Brockmann. "Effective containment explains sub-exponential growth in confirmed cases of recent COVID-19 outbreak in Mainland China." arXiv preprint arXiv:2002.07572 (2020). - https://arxiv.org/pdf/2002.07572.pdf

  35. Mesa: Agent-based modeling in Python 3+ https://github.com/projectmesa/mesa

  36. Excess cases of Influenza like illnesses in France synchronous with COVID19 invasion. Pierre-Yves Boëlle1 and the Sentinelles syndromic and viral surveillance group, Sorbonne Université, Institut Pierre Louis d’Epidemiologie et de Santé Publique, Paris, France - https://www.epicx-lab.com/uploads/9/6/9/4/9694133/sentinelles-2020-03-11.pdf

  37. Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) — United States, February 12–March 16, 2020 - https://www.cdc.gov/mmwr/volumes/69/wr/mm6912e2.htm?s_cid=mm6912e2_w - This first preliminary description of outcomes among patients with COVID-19 in the United States indicates that fatality was highest in persons aged ≥85, ranging from 10% to 27%, followed by 3% to 11% among persons aged 65–84 years, 1% to 3% among persons aged 55-64 years, <1% among persons aged 20–54 years, and no fatalities among persons aged ≤19 years.

  38. Remuzzi, Andrea, and Giuseppe Remuzzi. "COVID-19 and Italy: what next?." The Lancet (2020). - https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30627-9/fulltext?fbclid=IwAR3ke0W7zk58fdCwz_FJGw8VzAiVUveYng6mZmeHPsfBVW5814xlDd_yNgE - The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. A global response to prepare health systems worldwide is imperative. Although containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, and Italy has been particularly affected.

  39. Wu, Ke, et al. "Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world." arXiv preprint arXiv:2003.05681 (2020). - https://arxiv.org/pdf/2003.05681.pdf - Background: the COVID-19 has been successfully contained in China but is spreading all over the world. We use phenomenological models to dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We use the experience from China to analyze the calibration results on Japan, South Korea, Iran, Italy and Europe, and make future scenario projections. The datasets generated and analysed during the current study are available in the Github repository, https://github.com/kezida/covid-19-logistic-paper

  40. Visual Data Analysis and Simulation Prediction for COVID-19 - Baoquan Chen, Mingyi Shi, Xingyu Ni, Liangwang Ruan, Hongda Jiang, Heyuan Yao, Mengdi Wang, Zhenhua Song, Qiang Zhou, Tong Ge - In this study, we seek to answer a few questions: How did the virus get spread from the epicenter Wuhan city to the rest of the country? To what extent did the measures, such as, city closure and community quarantine, help controlling the situation? More importantly, can we forecast any significant future development of the event had some of the conditions changed? By collecting and visualizing publicly available data, we first show patterns and characteristics of the epidemic development; we then employ a mathematical model of disease transmission dynamics to evaluate the effectiveness of some epidemic control measures, and more importantly, to offer a few tips on preventive measures. - https://arxiv.org/abs/2002.07096, with code https://github.com/NCP-VIS

  41. Peng, Liangrong, et al. "Epidemic analysis of COVID-19 in China by dynamical modeling." arXiv preprint arXiv:2002.06563 (2020). - https://arxiv.org/pdf/2002.06563.pdf Based on the public data of National Health Commission of China from Jan. 20th to Feb. 9th, 2020, we reliably estimate key epidemic parameters and make predictions on the inflection point and possible ending time for 5 different regions.

  42. Li, Ming, Jie Chen, and Youjin Deng. "Scaling features in the spreading of COVID-19." arXiv preprint arXiv:2002.09199 (2020). - https://arxiv.org/pdf/2002.09199.pdf Since the outbreak of COVID-19, many data analysis have been done. Some of them are based on the classical epidemiological approach that assumes an exponential growth, but a few studies report that a power-law scaling may provide a better fitting to the currently available data. Hereby, we examine the epidemic data in China mainland (01/20/2020–02/24/2020) in a log-log scale, and indeed find that the growth closely follows a power-law kinetics over a significantly wide time period.

  43. Biswas, Kathakali, Abdul Khaleque, and Parongama Sen. "Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network." arXiv preprint arXiv:2003.07063 (2020). - https://arxiv.org/pdf/2003.07063.pdf We study the data for the cumulative as well as daily number of cases in the Covid-19 outbreak in China. The cumulative data can be fit to an empirical form obtained from a Susceptible-InfectedRemoved (SIR) model studied on an Euclidean network previously. Plotting the number of cases against the distance from the epicenter for both China and Italy, we find an approximate power law variation with an exponent ∼ 1.85 showing strongly that the spatial dependence plays a key role, a factor included in the model.

  44. COVID-2020 SIR - https://github.com/amita-kapoor/COVID-2020 @amita-kapoor

  45. Evidation Health - https://evidation.com/news/covid-19-pulse-first-data-evidation/ - To understand how Americans are coping with the spread of COVID-19, Evidation Health, the health and measurement company, has launched a nationwide initiative tracking people’s attitudes toward and experiences during the pandemic, alongside their health. Over 140,000 (as of March 22) people from across all 50 states and the District of Columbia have agreed to participate, recruited in less than seven days from the nearly 4 million people who use Evidation’s Achievement app—the largest, most diverse virtual research site in the U.S.

  46. CoronaTracker: World-wide COVID-19 Outbreak Data Analysis and Prediction CoronaTracker Community Research Group - https://www.who.int/bulletin/online_first/20-255695.pdf CoronaTracker was born as the online platform that provides latest and reliable news development, as well as statistics and analysis on COVID-19. This paper is done by the research team in the CoronaTracker community and aims to predict and forecast COVID19 cases, deaths, and recoveries through predictive modelling. The model helps to interpret patterns of public sentiment on disseminating related health information, and assess political and economic influence of the spread of the virus.

  47. Array Advisors’ Model Validates Fears of ICU Bed Shortage Due to Coronavirus Pandemic - https://array-architects.com/press-release/array-advisors-model-validates-fears-of-icu-bed-shortage-due-to-coronavirus-pandemic/ Array Advisors has built a model that projects the availability of U.S. hospital beds as the coronavirus pandemic grows. The model validates fears that a shortage of beds may occur unless efforts to expand hospital capacity are implemented immediately. Runnable model in Excel.

Link to Journals via Google Scholar

There are a variety of papers being published every day. Check out the below to keep up to date with the latest articles.

Channels and Social Media

  1. Covid-2019 Reddit Map Community - https://www.reddit.com/r/CovidMapping/

  2. Coronavirus: Why You Must Act Now - https://medium.com/@tomaspueyo/coronavirus-act-today-or-people-will-die-f4d3d9cd99ca

  3. Estimating the Number of Future Coronavirus Cases in the United States - https://towardsdatascience.com/estimating-the-number-of-future-coronavirus-cases-in-the-united-states-a0ce17df029a

  4. https://threadreaderapp.com/thread/1237347774951305216.html, @mlipsitch, with https://github.com/c2-d2/COVID-19-wuhan-guangzhou-data

  5. https://threadreaderapp.com/thread/1238972082756648960.html, @@davidasinclair

  6. Modelling the coronavirus epidemic in a city with Python - https://towardsdatascience.com/modelling-the-coronavirus-epidemic-spreading-in-a-city-with-python-babd14d82fa2

  7. Top 15 R resources on Novel COVID-19 Coronavirus - https://towardsdatascience.com/top-5-r-resources-on-covid-19-coronavirus-1d4c8df6d85f

Deep Learning Models

  1. Wang, Yunlu, et al. "Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner." arXiv preprint arXiv:2002.05534 (2020). - https://arxiv.org/pdf/2002.05534.pdf

  2. Xu, Xiaowei, et al. "Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia." arXiv preprint arXiv:2002.09334 (2020). - https://arxiv.org/pdf/2002.09334.pdf

  3. Chen, Jun, et al. "Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study." medRxiv (2020). - https://www.medrxiv.org/content/medrxiv/early/2020/02/26/2020.02.25.20021568.full.pdf

Thanks:

  • Badges made using https://shields.io/
  • References: Google Scholar, JHU, Midas, Multiple Twitter Accounts

About

Ongoing list of covid-19 models, visualizations

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 100.0%
0