Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model

TL;DR

This paper enhances an interaction-driven COVID-19 transmission model by incorporating meeting durations and individual disease progression, enabling better prediction of individual risks and understanding asymptomatic transmission dynamics.

Contribution

It introduces a novel enriched model that accounts for contact duration and personal disease progression, improving predictions at individual and population levels.

Findings

Asymptomatic transmission is influenced by network density.

Enriched model predicts individual risk based on virus traits and prevalence.

Asymptomatic transmission impacts are significant mainly in sparse communities.

Abstract

Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other important aspects were overlooked. Various airborne pathogens differ in the duration of exposure needed for infection. Also, from the individual perspective, Covid-19 progression differs between individuals, and its severity is statistically correlated with age. Here, we enrich an interaction-driven model of Covid-19 and similar airborne viral diseases with (a) meetings duration and (b) personal disease progression. The enriched model enables predicting outcomes at both the population and the individual levels. It further allows predicting individual risk of engaging in social interactions as a function of the virus characteristics and its prevalence in the population. We further showed that the enigmatic nature of asymptomatic transmission stems from the latent effect of the network density on this transmission and that asymptomatic transmission has a substantial impact only in sparse communities.