  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.
