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

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.

Simultaneous Confidence Band Approach for Comparison of COVID-19 Case Counts

“…The State of Ohio established a website 1 which provides the public with information and data about COVID-19 in the state. The daily case count data of male and female seniors (at least 60 years old) from April 1, 2020 to March 31, 2022, inclusive, were downloaded from the website.…”

“…It is the first such pandemic outbreak since the 1918 influenza, commonly known by the misnomer “Spanish flu”. It is the first time in human history that a huge amount of data related to a pandemic has been recorded, which makes it possible to analyze the biological, medical and social aspects of COVID-19, and which provides statisticians and data scientists with tremendous yet challenging opportunities for COVID-19 studies [ 1 ].…”

An ensemble learning strategy for panel time series forecasting of excess mortality during the COVID-19 pandemic

“…However, many SARIMA Electronic copy available at: https://ssrn.com/abstract=4057314 models have no exponential smoothing counterparts [44], and the robust univariate forecasting models such as Holt-Winters' multiplicative method (HWM) and the Exponential Smoothing State Space Model (ETS) could be considered as a good complimentary for SARIMA models in our final Ensemble. All ETS models are nonstationary, while some SARIMA models are stationary [45]. ETS follows the last trend of the time series and it is appropriate for the Ensemble model to empower the trend parameter in the final predictions.…”