Epidemiological dynamics with clinically-derived infectiousness and incubation time courses

TL;DR

This paper introduces a delay-equation-based epidemiological model incorporating experimentally derived infectiousness and incubation time courses, highlighting the importance of early infectiousness and contact tracing in controlling epidemic spread.

Contribution

It presents a novel delay model using real viral load data to improve epidemic predictions and assess intervention strategies.

Findings

Early infectiousness precedes detectable symptoms, aiding rapid contact tracing.

Prompt isolation significantly reduces epidemic spread, hospital load, and fatalities.

Incorporating detailed viral load dynamics enhances model accuracy.

Abstract

To better predict the dynamics of epidemics such as COVID-19, it is important not only to investigate the network of local and long-range contagious contacts but also to understand the temporal dynamics of infectiousness and detectable symptoms. Here, we present a model of infection spread in a well-mixed group of individuals, which usually corresponds to a node in large-scale epidemiological networks. The model uses delay equations that take into account the duration of infection and are based on experimentally derived time courses of viral load and shedding, as well as the detectability of symptoms. We show that due to an early onset of infectiousness, which is reported to be synchronous or even precede the onset of detectable symptoms, the tracing and immediate testing of all who came in contact with the detected infected individual reduce the spread of epidemics, hospital load, and fatality rate. We also investigate how the strictness and promptness of the isolation of infected individuals affect the outcome of epidemics. We hope that these more precise node dynamics could be incorporated into complex large-scale epidemiological models to improve the accuracy and credibility of predictions.