Within-host immunology to age-of-infection epidemiology via a virtual cohort

TL;DR

This paper introduces a methodology linking individual immune responses to population disease spread, exemplified with SARS-CoV-2, by generating synthetic cohorts and parametrizing epidemiological models.

Contribution

It presents a novel approach to connect within-host immunology with population-level epidemiology through a synthetic cohort framework.

Findings

Identified key immunological parameters affecting disease outcomes.

Generated a synthetic population with varied immune response profiles.

Parametrized an age-of-infection epidemiological model using within-host data.

Abstract

We present a methodology providing a one-directional link from within-host individual heterogeneity to population-level disease transmission dynamics. The methodology works in several steps. A within-host model is investigated numerically to determine pathogen and immunological parameters leading to the largest variation of model responses. These key parameters are used to generate a synthetic population of individuals whose temporal immunological response profiles are recorded. These responses are ranked in terms of the severity of experienced outcomes, from mild infections to death, as a function of time since infection. This is used to parametrise an age-of-infection structured epidemiological model to study the transmission dynamics of the disease at the population level. The approach is illustrated using a within-host model describing SARS-CoV-2 infection and an SIR population-level model.