Data science approaches to confronting the COVID-19 pandemic: a narrative review

Abstract

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale ‘big data’ generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

A data-driven hybrid ensemble AI model for COVID-19 infection forecast using multiple neural networks and reinforced learning

“…Second, integration with other modeling approaches (e.g., time series models such as ARIMA, STAR models, dynamic models such as SAHQD model [ 19 ], SEIAIR model [ 28 ], even the SCUAQIHMRD model [ 31 ]) can also be performed, where reinforcement learning can still provide an integration framework. Third, the data of other modalities, such as Mobility Report released by Google [ 78 ], search interest [ 51 ], local weather data, human contact data [ 33 ], etc., could also considered as meaningful input into the model [ 79 ], although the inaccessibility and incompleteness of some data modalities may limit the power and generalizability of the model with integrated multi-modalities. Additionally, as a deep learning model, there are some inherent drawbacks.…”

A COVID-19 Search Engine (CO-SE) with Transformer-based architecture

“…Our goal is to propose a scalable in their search and discovery for answers to high priority scientific queries. Some recent works [33] , [34] , [35] , [36] , [37] on COVID-19 literature and pandemic response are also the motivation for the current study.…”

Data science approaches to infectious disease surveillance

“…notably the ongoing COVID-19 pandemic, data science approaches have emerged to become widely adopted in understanding and combating infectious disease surveillance [1].…”

“…Infectious disease epidemics have generated massive data on human behaviours, including human movement, contact tracing, clinical records, virology, pharmacy, scientific literature and so on. With the ever-increasing availability of data and the urgent need for data-driven insights to combat various infectious disease epidemics, notably the ongoing COVID-19 pandemic, data science approaches have emerged to become widely adopted in understanding and combating infectious disease surveillance [ 1 ].…”

“…There are a number of important topics that were not covered by the papers in this theme issue, including assessing the economic impact, mining patients' data, drug re-purposing and development, mining scientific literature and so on. Interested readers can refer to [ 1 ], which reviews the newly-born data science approaches to addressing these problems in the battle against COVID-19, and discusses the opportunities and challenges in this emerging field.…”