Alejandra D Herrera Reyes - Uncertainty and error in SARS-CoV-2 epidemiological parameters inferr...
Channel: BioInference
Date: 2022-07-16

Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models

Alejandra D Herrera Reyes
University of Nottingham

BioInference 2022
https://bioinference.github.io/

00:00 Talk
19:31 Q&A

Abstract:
During the SARS-CoV2 pandemic, epidemic models have been central to policy-making. Public health responses have been shaped by model-based projections and inferences, especially related to the impact of various non-pharmaceutical interventions. Accompanying this has been increased scrutiny over model performance, model assumptions, and the way that uncertainty is incorporated and presented. Within the context of population-level modelling, in this paper we focus on the particular impact of how the timecourse of host infectiousness and the infection-to-death times are modelled. We introduce an SIR-type model with the infected population structured by `infected age', i.e. the number of days since first being infected, a formulation that enables distributions to be incorporated which are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly misspecify these distributions, is inaccurate for quantities relevant to policy-making, such as the reproduction number and the impact of interventions. We consider uncertainty quantification via a Bayesian approach, implementing this for both synthetic and real data focusing on UK data in the period 15 Feb--14 Jul 2020, and emphasising circumstances where it is misleading to neglect uncertainty.