Correlation of viral loads in disease transmission chains could bias early estimates of the reproduction number

TL;DR

This study uses simulations to show that correlations in viral loads within transmission chains can bias early estimates of a virus's transmission characteristics, impacting public health responses.

Contribution

It introduces a computational model revealing how viral load correlations influence early transmission estimates and cause biases in outbreak analysis.

Findings

Low initial viral loads lead to larger biases in early estimates.

Viral load correlations cause a steady-state distribution in transmission chains.

Biases can significantly affect early public health response strategies.

Abstract

Early estimates of the transmission properties of a newly emerged pathogen are critical to an effective public health response, and are often based on limited outbreak data. Here, we use simulations to investigate a potential source of bias in such estimates, arising from correlations between the viral load of cases in transmission chains. We show that this mechanism can affect estimates of fundamental transmission properties characterising the spread of a virus. Our computational model simulates a disease transmission mechanism in which the viral load of the infector at the time of transmission influences the infectiousness of the infectee. These correlations in transmission pairs produce a population-level decoherence process during which the distributions of initial viral loads in each subsequent generation converge to a steady state. We find that outbreaks arising from index cases with low initial viral loads give rise to early estimates of transmission properties that are subject to large biases. These findings demonstrate the potential for bias arising from transmission mechanics to affect estimates of the transmission properties of newly emerged viruses.