Bayesian estimation of transmission networks for infectious diseases.

Abstract

Reconstructing transmission networks is essential for identifying key factors like superspreaders and high-risk locations, which are critical for developing effective pandemic prevention strategies. This study presents a Bayesian transmission model that combines genomic and temporal data to reconstruct transmission networks for infectious diseases. The Bayesian transmission model incorporates the latent period and distinguishes between symptom onset and actual infection time, improving the accuracy of transmission dynamics and epidemiological models. It also assumes a homogeneous effective population size among hosts, ensuring that the coalescent process for within-host evolution remains unchanged, even with missing intermediate hosts. This allows the model to effectively handle incomplete samples. Simulation results demonstrate the model's ability to accurately estimate model parameters and transmission networks. Additionally, our proposed hypothesis test can reliably identify direct transmission events. The Bayesian transmission model was applied to a real dataset of Mycobacterium tuberculosis genomes from 69 tuberculosis cases. The estimated transmission network revealed two major groups, each with a superspreader who transmitted M. tuberculosis, either directly or indirectly, to 28 and 21 individuals, respectively. The hypothesis test identified 16 direct transmissions within the estimated network, demonstrating the Bayesian model's advantage over a fixed threshold by providing a more flexible criterion for identifying direct transmissions. This Bayesian approach highlights the critical role of genetic data in reconstructing transmission networks and enhancing our understanding of the origins and transmission dynamics of infectious diseases.