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

This dataset accompanies the an article titled, 'Correlation of viral loads in disease transmission chains could bias early estimates of the reproduction number', by T. Harris et al. which can be found at https://arxiv.org/abs/2211.08673.

Abstract:

Early estimation of the transmission properties of a newly emerged pathogen is critical to inform an effective public health response. Early estimates are based on limited data, meaning methods for estimating pathogen characteristics based on limited data are crucial. Here, we investigate a potential source of bias arising from correlations between the viral load of cases in transmission chains. We show that this mechanism can affect estimates of common transmission properties characterising the spread of a virus. To model these correlations we simulate 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. Through our simulation study, 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. 

Contents of this dataset include: 

Model code - including experiments (python3)

Estimation code (R)

Code used for figure production (python3 & R)

Raw data produced from Experiments 1-3 and presented in the text (.csv & .Rdata)

See 'README.md' in main upload folder for more information