  During the COVID-19 pandemic, different countries, regions, and communities
constructed various epidemic models to evaluate spreading behaviors and assist
in making mitigation policies. Model uncertainties, introduced by complex
transmission behaviors, contact-tracing networks, time-varying spreading
parameters, and human factors, as well as insufficient data, have posed arduous
challenges for model-based approaches. To address these challenges, we propose
a novel framework for data-driven counterfactual analysis, strategy evaluation,
and feedback control of epidemics, which leverages statistical information from
epidemic testing data instead of constructing a specific model. Through reverse
engineering the reproduction number by quantifying the impact of the
intervention strategy, this framework tackles three primary problems: 1) How
severe would an outbreak have been without the implemented intervention
strategies? 2) What impact would varying the intervention strength have had on
an outbreak? 3) How can we adjust the intervention intensity based on the
current state of an outbreak? Specifically, we consider the epidemic
intervention policies such as the testing-for-isolation strategy as an example,
which was successfully implemented by the University of Illinois
Urbana-Champaign (UIUC) and Purdue University (Purdue) during the COVID-19
pandemic. By leveraging data collected by UIUC and Purdue, we validate the
effectiveness of the proposed data-driven framework.
