This dataset accompanies the an article titled, 'Correlation of viral loads in disease transmission could affect early estimates of the reproduction number', by T. Harris et al. which can be found at https://arxiv.org/abs/2211.08673. 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 how correlations between the viral load of cases in transmission chains can affect estimates of these fundamental transmission properties. 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 convergence 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 could be misleading. These findings demonstrate the potential for transmission mechanisms to affect estimates of the transmission properties of newly emerged viruses in ways that could be operationally significant to a public health response. 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