Bayesian calendar-time survival analysis with epidemic curve priors and variant-specific infection hazards

TL;DR

This paper introduces a Bayesian survival analysis model tailored for infectious disease studies during epidemics, incorporating epidemic curve priors, variant-specific hazards, and biomarker thresholds, demonstrated through simulations and COVID-19 data.

Contribution

It presents a novel Bayesian model that integrates epidemic curve priors and variant-specific hazards for improved infection risk estimation during epidemics.

Findings

Effective incorporation of epidemic curve priors into hazard estimation

Ability to estimate biomarker protection thresholds

Successful application to COVID-19 vaccine data

Abstract

In this paper, we develop a Bayesian calendar-time survival model motivated by infectious disease prevention studies occurring during an epidemic, when the risk of infection can change rapidly as the epidemic curve shifts. For studies in which a biomarker is the predictor of interest, we include the option to estimate a threshold of protection for the biomarker. If the intervention is hypothesized to have different associations with several circulating viral variants, or if the infectiousness of the dominant variant(s) changes over the course of the study, we treat infection from different variants as competing risks. We also introduce a novel method for incorporating existing epidemic curve estimates into an informative prior for the baseline hazard function, enabling estimation of the intervention's association with infection risk during periods of calendar time with minimal follow-up in one or more comparator groups. We demonstrate the strengths of this method via simulations, and we apply it to data from an observational COVID-19 vaccine study.