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During the COVID-19 pandemic, different countries, regions, and communitiesconstructed various epidemic models to evaluate spreading behaviors and assistin making mitigation policies. Model uncertainties, introduced by complextransmission behaviors, contact-tracing networks, time-varying spreadingparameters, and human factors, as well as insufficient data, have posed arduouschallenges for model-based approaches. To address these challenges, we proposea novel framework for data-driven counterfactual analysis, strategy evaluation,and feedback control of epidemics, which leverages statistical information fromepidemic testing data instead of constructing a specific model. Through reverseengineering the reproduction number by quantifying the impact of theintervention strategy, this framework tackles three primary problems: 1) Howsevere would an outbreak have been without the implemented interventionstrategies? 2) What impact would varying the intervention strength have had onan outbreak? 3) How can we adjust the intervention intensity based on thecurrent state of an outbreak? Specifically, we consider the epidemicintervention policies such as the testing-for-isolation strategy as an example,which was successfully implemented by the University of IllinoisUrbana-Champaign (UIUC) and Purdue University (Purdue) during the COVID-19pandemic. By leveraging data collected by UIUC and Purdue, we validate theeffectiveness of the proposed data-driven framework.

Reverse Engineering the Reproduction Number: A Framework for Data-Driven  Counterfactual Analysis, Strategy Evaluation, and Feedback Control of  Epidemics