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Early estimates of the transmission properties of a newly emerged pathogenare critical to an effective public health response, and are often based onlimited outbreak data. Here, we use simulations to investigate a potentialsource of bias in such estimates, arising from correlations between the viralload of cases in transmission chains. We show that this mechanism can affectestimates of fundamental transmission properties characterising the spread of avirus. Our computational model simulates a disease transmission mechanism inwhich the viral load of the infector at the time of transmission influences theinfectiousness of the infectee. These correlations in transmission pairsproduce a population-level decoherence process during which the distributionsof initial viral loads in each subsequent generation converge to a steadystate. We find that outbreaks arising from index cases with low initial viralloads give rise to early estimates of transmission properties that are subjectto large biases. These findings demonstrate the potential for bias arising fromtransmission mechanics to affect estimates of the transmission properties ofnewly emerged viruses.

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