Machine Learning Research Towards Combating COVID-19: Virus Detection, Spread Prevention, and Medical Assistance

TL;DR

This paper surveys how machine learning has been utilized in COVID-19 detection, spread prevention, and medical assistance, highlighting its role in screening, forecasting, and vaccine development.

Contribution

It provides a comprehensive overview of ML algorithms and models applied to COVID-19, emphasizing their contributions in diagnosis, prediction, and vaccine research.

Findings

ML methods aid in COVID-19 screening and diagnosis

Forecasting models help predict virus spread patterns

ML accelerates vaccine development processes

Abstract

COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, and predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspectives. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.