








key: cord-316705-3wzurnfp
authors: Lalmuanawma, Samuel; Hussain, Jamal; Chhakchhuak, Lalrinfela
title: Applications of Machine Learning and Artificial Intelligence for Covid-19 (SARS-CoV-2) pandemic: A review
date: 2020-06-25
journal: Chaos Solitons Fractals
DOI: 10.1016/j.chaos.2020.110059
sha: 
doc_id: 316705
cord_uid: 3wzurnfp

BACKGROUND AND OBJECTIVE: : During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic. METHOD: A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic. RESULT: This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead. CONCLUSION: The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic.

There are several disease outbreaks that invaded humanity in World history. World Health Organization (WHO), its co-operating clinicians and various national authorities around the globe fight against these pandemics to date. Since the first Covid-19 (Coronavirus) disease case confirmed in China December 2019 Wuhan District, the outbreak continues to spread all across the world, and on 30th January 2020 WHO declared the pandemic as an 1 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. 

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?".

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. 

Real-time forecast and 10 days ahead, Observed seven key features associated with dead rate.

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] . [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.

Since the outbreak of the novel SARS-CoV-2, scientists and medical industries around the globe ubiquitously urged to fight against the pandemic, searching alternative method of rapid screening and prediction process, contact tracing, forecasting, and development of vaccine or drugs with the more accurate and reliable operation. Machine Learning and Artificial Intelligence are such promising methods employed by various healthcare providers. This paper addresses on recent studies that apply such advance technology in augmenting the researchers in multiple angles, addressing the troubles and challenges while using such algorithm in assisting medical expert in real-world problems. This paper also discusses suggestions conveying researchers on AI/ML-based model design, medical experts, and policymakers on few errors encountered in the current situation while tackling the current pandemic. This review shows that the use of modern technology with AI and ML dramatically improves the screening, prediction, contact tracing, forecasting, and drug/vaccine development with extreme reliability. Majority of the paper employed deep learning algorithms and is found to have more potential, robust, and advance among the other learning algorithms. However, the current urgency requires an improved model with high-end performance accuracy in screening and predicting the SARS-CoV-2 with a different kind of related disease by analyzing the clinical, mammographic, and demographic information of the suspects and infected patients. Finally, it is evident that AI and ML can significantly improve treatment, medication, screening & prediction, forecasting, contact tracing, and drug/vaccine development for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the pandemic.

None.

World Health Organization declares Global Emergency: A review of the 2019 Novel Coronavirus (COVID-19)

Coronavirus disease (COVID-2019) situation Reports

COVID-19 Dashboard by the

The potential for artificial intelligence in healthcare

Intelligent Decision Making: An AI-Based Approach

Use of artificial intelligence in infectious diseases. Artificial Intelligence in Precision Health

Computer-Based Medical Consultations: Mycin

Machine learning for clinical decision support in infectious diseases: A narrative review of current applications

An adaptive machine learning approach to improve automatic iceberg detection from SAR images

Deep nonsmooth nonnegative matrix factorization network factorization network with semi-supervised learning for SAR image change detection

Change detection in SAR images by artificial immune multi-objective clustering

A novel target detection method for SAR images based on shadow proposal and saliency analysis

Machine-learning Prognostic Models from the 2014-16 Ebola Outbreak: Data-harmonization Challenges, Validation Strategies, and mHealth Applications

Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks

Two-steps learning of Fuzzy Cognitive Maps for prediction and knowledge discovery on the HIV-1 drug resistance

Automated diagnosis of HIV-associated neurocognitive disorders using large-scale Granger causality analysis of resting-state functional MRI

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Breast cancer detection by leveraging Machine Learning. ICT Express

Machine Learning Methods for Computer-Aided Breast Cancer Diagnosis Using Histopathology: A Narrative Review

Diabetic Retinopathy Detection through Novel Tetragonal Local Octa Patterns and Extreme Learning Machines

Machine Learning and Data Mining Methods in Diabetes Research

Artificial Plant Optimization Algorithm to detect Heart Rate & Presence of Heart Disease using Machine Learning

Classification models for heart disease prediction using feature selection and PCA. Informatics in Medicine Unlocked

A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset

Deep learning IoT system for online stroke detection in skull computed tomography images

Artificial Intelligence (AI) applications for COVID-19 pandemic

Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks

Automated detection of COVID-19 cases using deep neural networks with X-ray images

Combination of four clinical indicators predicts the severe/critical symptom of patients infected COVID-19

Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results

WHO: World Health Organization, 2020. Health Topic, Coronavirus disease overview

Contact tracing in the context of COVID-19

Wikipedia: Covid-19 apps

MIT: Covid Tracing Tracker -A flood of coronavirus apps are tracking us. Now it's time to keep track of them

Contact tracing for the control of infectious disease epidemics: Chronic Wasting Disease in deer farms

BBC: Coronavirus: Israel enables emergency spy powers

Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil

An interpretable mortality prediction model for COVID-19 patients

Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks

Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

Artificial intelligence approach fighting COVID-19 with repurposing drugs

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus

DÃ©jÃ  vu: stimulating open drug discovery for SARS-CoV-2. Drug Discovery Today

Open drug discovery for the Zika virus



