Work

Estimating time-varying epidemic severity rates with adaptive deconvolution

Year: 2026

Type: article

Abstract: Several key metrics in public health convey the probability that a primary event will lead to a more serious secondary event in the future. These “severity rates” can change over the course of an epidemic in response to shifting conditions, like new therapeutics, variants, or public health interventions. In practice, time-varying parameters, such as the case-fatality rate, are typically estimated from aggregate count data. Prior work has demonstrated that commonly-used ratio-based estimators can be highly biased, motivating the development of new methods. In this paper we develop an adaptive deconvolution approach based on approximating a Poisson-binomial model for secondary events, and we regularize the maximum likelihood solution in this model with a trend filtering penalty to produce smooth but locally adaptive estimates of severity rates over time. This enables us to compute severity rates both retrospectively and in real time. Experiments based on COVID-19 death and hospitalization data show that our deconvolution estimator is generally more accurate than the standard ratio-based methods and displays reasonable robustness to model misspecification.

Source: The Annals of Applied Statistics

DOI: 10.1214/26-aoas2216

Authors: Jeremy Goldwasser, Addison J. Hu, Alyssa Bilinski, Daniel J. McDonald, Ryan J. Tibshirani

Institutions: University of California, Berkeley, Carnegie Mellon University, Brown University, University of British Columbia

Topic: COVID-19 epidemiological studies

Subfield: Modeling and Simulation

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Council of State and Territorial Epidemiologists, Centers for Disease Control and Prevention

OpenAlex ID: W4415950404

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