This article is a Preprint
Preprints are preliminary research reports that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Preprints posted online allow authors to receive rapid feedback and the entire scientific community can appraise the work for themselves and respond appropriately. Those comments are posted alongside the preprints for anyone to read them and serve as a post publication assessment.
Uncertainty and error in SARS-CoV-2 epidemiological parameters inferred from population-level epidemic models (preprint)
medrxiv; 2022.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2022.07.01.22277134
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. Here we consider a population-level model, focusing on how distributions representing host infectiousness and the infection-to-death times are modelled, and particularly on the impact of inferred epidemic characteristics if these distributions are misspecified. 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 that are consistent with clinical data. We show that inference based on simpler models without infected age, which implicitly misspecify these distributions, leads to substantial errors in inferred 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.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
Encephalitis, Arbovirus
Language:
English
Year:
2022
Document Type:
Preprint
Similar
MEDLINE
...
LILACS
LIS