Forecasting COVID-19 and Analyzing the Effect of Government Interventions

Michael Lingzhi Li (), Hamza Tazi Bouardi (), Omar Skali Lami (), Thomas A. Trikalinos (), Nikolaos Trichakis () and Dimitris Bertsimas ()
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Michael Lingzhi Li: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Hamza Tazi Bouardi: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Omar Skali Lami: Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Thomas A. Trikalinos: Center for Evidence Synthesis in Health, Brown University, Providence, Rhode Island 02912
Nikolaos Trichakis: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
Dimitris Bertsimas: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142

Operations Research, 2023, vol. 71, issue 1, 184-201

Abstract: We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more than 200 geographical areas since early April 2020 and recorded 6% and 11% two-week, out-of-sample median mean absolute percentage error on predicting cases and deaths, respectively. DELPHI compares favorably with other top COVID-19 epidemiological models and predicted in 2020 the large-scale epidemics in many areas, including the United States, United Kingdom, and Russia, months in advance. We further illustrate two downstream applications of DELPHI, enabled by the model’s flexible parametric formulation of the effect of government interventions. First, we quantify the impact of government interventions on the pandemic’s spread. We predict, that in the absence of any interventions, more than 14 million individuals would have perished by May 17, 2020, whereas 280,000 deaths could have been avoided if interventions around the world had started one week earlier. Furthermore, we find that mass gathering restrictions and school closings were associated with the largest average reductions in infection rates at 29.9 ± 6.9 % and 17.3 ± 6.7 % , respectively. The most stringent policy, stay at home, was associated with an average reduction in infection rate by 74.4 ± 3.7 % from baseline across countries that implemented it. In the second application, we demonstrate how DELPHI can predict future COVID-19 incidence under alternative governmental policies and discuss how Janssen Pharmaceuticals used such analyses to select the locations of its Phase III trial for its leading single-dose vaccine candidate Ad26.Cov2.S.

Keywords: Machine Learning and Data Science; epidemiology; compartmental modeling; infectious diseases; partial identifiability (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:71:y:2023:i:1:p:184-201

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