Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

2.1. ML and AI technology in SARS-CoV-2 screening and treatment

Early detection of any disease, be it infectious and non-infectious, is critically an important task for early treatment to save more lives [26,27]. Fast diagnosis and screening process helps prevent the spread of pandemic diseases like SARS-CoV-2, cost-effective, and speed up the related diagnosis. The development of an expert system for health care assists in the new order of identification screening and management of SARS-CoV-2 carrier by more cost-effective compared to the traditional method. ML and AI are used to augment the diagnosis and screening process of the identified patient with radio imaging technology akin to Computed Tomography (CT), X-Ray, and Clinical blood sample data. In this regard, Table 1 shows selective information on diagnosis and screening proposed for the Coronavirus disease. The healthcare expert can use radiology images like X-ray and CT scans as routine tools to augment traditional diagnosis and screening. Unfortunately, the performance of such devices is moderate during the high outburst of the SARS-CoV-2 pandemic. In this regard, studies [28] show the potential of AI and ML tools by suggesting a new model that comes with rapid and valid method SARS-CoV-2 diagnosis using Deep Convolutional Network. The study shows that diagnosis utilizing an expert system employing AI and ML on 1020 CT images of 108 Covid-19 infected patients along with viral pneumonia of 86 patients, the remarkable performance suggests the use of the convolutional neural network (Resnet-101) as an adjuvant tool for radiologist resulting 86.27%, 83.33% of accuracy and specificity respectively.

Recent studies design an auxiliary tool to increase the accuracy of Covid-19 diagnosis with new model Automatic COVID-19 detection based on deep learning algorithm [29]. The developed model uses raw chest X-ray images of 127 infected patients with 500 no-findings and pneumonia cases of 500 records. With remarkable performance accuracy, binary class of 98.08%, and multi-class with 87.02%. Multi-classes proved the applicability of the expert system to assist radiology in validating in screening process rapidly and accurately.

Furthermore, researchers have found four important medical features combinations of clinical, laboratory features, and demographic information using GHS, CD3 percentage, total protein, and patient age employing Support Vector Machine as the primary feature classification model [30]. The new model is effective and robust in predicting patients in critical/severe conditions, and the empirical results show that a combination of the four-feature results an AUROC of 0.9996 and 0.9757 in training and testing datasets respectively. The survival and the cox-multivariant regression analysis revealed the model's significance towards and auxiliary tools for the healthcare expert.

After evaluating 253 clinical blood samples from Wuhan, researchers found eleven (bilirubin total, creatine kinase isoenzyme, GLU, creatinine, kalium, lactate dehydrogenase, platelet distribution width, calcium, basophil, total protein, and magnesium) key relevant indices which can assist as a discrimination tool of Covid-19 for healthcare expert toward rapid diagnosis [31]. The studies show that 11 relevant indices are extracted after employing the Random Forest algorithm with an overall accuracy of 95.95% and 96.97% specificity respectively. Furthermore, the authors published that the tools were deployed and are available on web-server at http://lishuyan.lzu.edu.cn/COVID2019_2/ to assist healthcare experts.

The above studies give the evidence of an application of the expert system; designing rapid diagnosis was the main objective along with augmentation of accuracy. Prompt and early detection reduce the spread of the disease and reserve more time to the healthcare expert to correspond to the next diagnosis to save more lives, resulting in low-cost medical expenditure. However, majority of the studied paper employed a single classification algorithm on individual data or more. Therefore it is suggested to come up with a hybrid classification method applying more potential algorithm on multi-database or hybrid-database consisting of clinical, mammographic, and demographic data, as each type of data has a significant factor that could represent the true identity of the infected patients and deployment of the application in the real world.

2.2. ML and AI technology in SARS-Cov-2 contact tracing

If a person diagnoses and is confirmed with Covid-19, the next important step is contact tracing prevention of the wider spread of the disease. According to WHO, the infection spreads from person-to-person primarily through saliva, droplets, or discharges from the nose through contact transmission [32]. To take control on the spread of SARS-Cov-2, contact tracing is an essential public health tool used to break the chain of virus transmission [33]. The process of contact tracing is to identify and manage people who are recently exposed to an infected Covid-19 patient to avoid further spread. Generally, the process identifies the infected person with a follow-up for 14 days since the exposure. If employed thoroughly, this process can break the transmission chain of the current novel coronavirus and suppress the outbreak by giving a higher chance of adequate controls and helping reduce the magnitude of the recent pandemic. In this regard, various infected countries come up with a digital contact tracing process with the mobile application, utilizing different technologies like Bluetooth, Global Positioning System (GPS), Social graph, contact details, network-based API, mobile tracking data, card transaction data, and system physical address. The digital contact tracing process can perform virtually real-time and much faster compared to the non-digital system. All these digital apps are designed to collect individual personal data, which will be analyzed by ML and AI tools to trace a person who is vulnerable to the novel virus due to their recent contacted chain.

As shown in Table 2 , articles [34,35] listed various countries competent with such ML and AL-based contact tracing applications. Studies show that over 36 Countries successfully employed digital contact tracing use following centralized, decentralized, or hybrid of both techniques were proposed to lessen the effort and augment the effectiveness of the traditional healthcare diagnosis processes.

Concerning contact tracing, studies have proven the use of ML and AI in augmentation of contact tracing process against infectious Chronic Wasting disease [36]. After applying Graph theory on infectious animal disease epidemics data, mainly shipment data between each farm, the resultant graph properties generated by the proposed model can be used to exploit to augment contact tracing more efficiently. Moreover, the generated graphs have a potential prediction impact on the number of infections that can take place. However, there are still limitations in addressing the scenario, privacy, control over the data, and even data security breach. Countries are working to overcome the challenges; some countries like Israel “passed an emergency law to use mobile phone data” to tackle the current pandemic [37]. Among the world contact tracing apps, some countries app violated privacy law and reported unsafe [35] so far they do the job acceptably by supplement the manual tracing process. However, virtually every country has their contact tracing application individually, as the outbreak continues to spread across the world, it becomes a global health emergency. To fight against the Covid-19 as one, one should provide a standard de-facto centralized contact tracing application to trace every human being all around the world. Also, it is reported that some specific query needs to address: “Is it mandatory or voluntary?” “Is the attempt clear or translucent?” “Is information gathering lessened?” “Will collected information be demolished as declared?”, “Is the data safe with the host” and “Are there any restrictions or control on utilizing the information?”.

2.3. ML and AI technology in SARS-CoV-2 prediction and forecasting

Selective information shown in Table 3 indicates the applications of ML and AI in forecasting and predicting the novel pandemic. A new novel model, that forecast and predicting 1-3 to 6 days ahead of total Covid-19 patient of 10 Brazilian states, using stacking-ensemble with support vector regression algorithm on the cumulative positive Covid-19 cases of Brazilian data was proposed, thus augmenting the short-term forecasting process to alert the healthcare expert and the government to tackle the pandemic [38]. Recent studies suggested a novel model using a supervised multi-layered recursive classifier called XGBoost on clinical and mammographic factor datasets. After applying the model, researchers found out those three significant key features (high-sensitivity C-reactive protein, lymphocyte and lactic dehydrogenase (LDH)) of the 75 features clinical and blood test samples result to be the highest rank of 90% accuracy in predicting and assessing Covid-19 patient into general, severe and mortality rate [39]. Furthermore, comparatively higher value in single lactic dehydrogenase appears to be a significant factor in classifying most patients in need of intensive medical care, as LDH degree related to various respiratory disorder diseases, namely asthma and bronchitis, and pneumonia. The forecast model employed decision rule to forecast rapidly and predict infected individuals at the highest risk, authorized patients to be manageable for intensive care, and possibly lessen the transience rate. A Canadian based forecasting model using time-series was developed employing Deep learning algorithm for the long-short-term-memory network, the studies found out a key factor intended for predicting the course with an ending point estimation of the current SARS-CoV-2 epidemic in Canada and all over the globe [40]. The suggested model forecast ending point of this SARS-CoV-2 outbreak in Canada will be around June 2020. Based on the data collected from John Hopkins University [3], the prediction was likely to be accurate as newly infected cases have dropped rapidly and proven the applicability of the expert system in predicting and forecasting for the current pandemic outbreak by revealing key significant features. The real-time forecasting model was proposed combining the goodness of the wavelet-based forecasting model and autoregressive integrated moving average based time-series model [41]. The model solves the problem by generating short-term forecasts of the SARS-CoV-2 for various countries (India, United Kingdom, Canada, South Korea, and France) to assist healthcare experts and policymakers as a preliminary cautioning module for each target country.

2.4. ML and AI technology in SARS-CoV-2 drugs and vaccination

Since the coronavirus epidemic fury, researchers and healthcare experts around the globe ubiquitously urged to develop a possible choice to tackle the development of drug and vaccine for the SARS-CoV-2 pandemic, and ML/AI technology constitutes to be an enthralling road. Concerning the possibility of drug choice for infected patient's treatment, instant testing on the existing old marketable medicines for novel SARS-CoV-2 carrier in a human being is essential.

Researchers from Taiwan are building a new model to augment the development of a novel drug [42]. After applying the ML and AI technology-based model on two datasets (one using the 3C-like protease constraint and other data-holding records of infected SARS-CoV, SARS-Cov-2, influenza, and human immunodeficiency virus (HIV)) using Deep Neural Network on the eighty old drugs with potential for Covid-19 treatment, the study suggested eight drugs, i.e., vismodegib, gemcitabine, clofazimine, celecoxib, brequinar, conivaptan, bedaquiline and tolcapone are found virtually effective against feline infectious peritonitis coronavirus. Furthermore, other five drugs like homoharringtonine, salinomycin, boceprevir, tilorone and chloroquine are also found operational during AI experimental environment.

A novel molecule transformer-drug target interaction model was proposed jointly by researchers from the US and Korea to tackle the need for an antiviral drug that can treat the Covid-19 virus [43]. The study compares the accessible virtual screening and molecular docking application called AutoDock Vina with the proposed model employing a deep learning algorithm on 3C-like proteinase of Covid-19 and FDA approved 3,410 existing drugs available in the market. The result revealed that a popular antiretroviral drug used to treat HIV called Antazanavir (Kd of 94.94 nM) is the best drug for Covid-19 medication, followed by Remdisivir (Kd of 113.13 nM). Furthermore, results revealed that some medications like darunavir, ritonavir, and lopinavir were outlined to tackle viral proteinases. It was also found that various antiviral compounds like Kaletra might be utilized for the medicine of Covid-19 human patients.

A group of researchers from the USA discovered an antiviral drug for treating the Ebola virus. The discovery was first active in the year 2014 [44], starting with ML and AI-based pharmacophore computational analyzing on a limited size of in vitro infected carriers of the Ebola virus. The study proposed an amodiaquine and chloroquine compound popularly used to treat the malaria virus. Furthermore, after uncovering a decade of drug development based on ML and AI technology, a fusion of computational screening method with docking application and machine learning for choosing supplementary medication to investigate on SARS-CoV-2 was proposed [45]. Researchers refer to the successful discovery of Ebola [44], and the Zika virus [46] experience gain belief that the same model could be repeatedly utilized for drug discovery on Covid-19 and future virus pandemic ahead.

The selected review paper adopted various methodologies and technologies addressing the classical method of classification based on statistics to an advanced modern AI and ML algorithm. The use of computational tools, combined with docking application, was found to be more active in predicting the reusability of an existing old drug on Covid-19 medication and dramatically minimize the level of a risk factor in the development of medicine more cost-effective process. During this urgency, the use of ML and AI can augment the drug development process by lessening the time slot on discovering a supplementary treatment and medication for the carrier by drawing a vast probability over security, manageability, and clinical information on the existing drug compound. Issues and challenges found in this area were the limited resource of comprehensive hybrid data and real-life deployment of the application.