Methods, Challenges, and Practical Issues of COVID-19 Projection: A Data Science Perspective

Kim, Myungjin; Gu, Zhiling; Yu, Shan; Wang, Guannan; Wang, Li

doi:10.6339/21-JDS1013

Journal of Data Science

Methods, Challenges, and Practical Issues of COVID-19 Projection: A Data Science Perspective

Volume 19, Issue 2 (2021), pp. 219–242

Myungjin Kim Zhiling Gu Shan Yu All authors (5)

https://doi.org/10.6339/21-JDS1013

Pub. online: 27 April 2021 Type: Philosophies Of Data Science

Received
21 April 2021

Accepted
22 April 2021

Published
27 April 2021

Abstract

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed epidemic modeling at the center of attention of public policymaking. Predicting the severity and speed of transmission of COVID-19 is crucial to resource management and developing strategies to deal with this epidemic. Based on the available data from current and previous outbreaks, many efforts have been made to develop epidemiological models, including statistical models, computer simulations, mathematical representations of the virus and its impacts, and many more. Despite their usefulness, modeling and forecasting the spread of COVID-19 remains a challenge. In this article, we give an overview of the unique features and issues of COVID-19 data and how they impact epidemic modeling and projection. In addition, we illustrate how various models could be connected to each other. Moreover, we provide new data science perspectives on the challenges of COVID-19 forecasting, from data collection, curation, and validation to the limitations of models, as well as the uncertainty of the forecast. Finally, we discuss some data science practices that are crucial to more robust and accurate epidemic forecasting.

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COVID-19 disease spread epidemic models forecast uncertainty

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