A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates.

ABSTRACT: During pandemics, countries, regions, and communities develop various epidemic models to evaluate spread and guide mitigation policies. However, model uncertainties caused by complex transmission behaviors, contact-tracing networks, time-varying parameters, human factors, and limited data present significant challenges to model-based approaches. To address these issues, we propose a novel framework that centers around reproduction number estimates to perform counterfactual analysis, strategy evaluation, and feedback control of epidemics. The framework 1) introduces a mechanism to quantify the impact of the testing-for-isolation intervention strategy on the basic reproduction number. Building on this mechanism, the framework 2) proposes a method to reverse engineer the effective reproductio

SUBMITTER: She B 

PROVIDER: S-EPMC11616887 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature