Bayesian inference for disease transmission models informed by viral dynamics

TL;DR

This paper introduces a multiscale Bayesian model linking within-host viral dynamics to household transmission, with efficient inference methods evaluated on simulated data.

Contribution

It develops a joint modeling framework with a cut inference approach to connect viral load trajectories and transmission, addressing computational challenges.

Findings

Parameter recovery is unbiased with high-frequency viral load sampling.

Sparse sampling introduces bias, which can be mitigated with external data.

The framework effectively estimates transmission parameters from simulated household data.

Abstract

Infectious disease dynamics operate across multiple biological scales, with within-host viral dynamics being a key driver of between-host transmission. However, while models that explicitly link these scales exist, none have been developed with statistical inference as a primary goal. In this paper we propose a multiscale model that jointly captures heterogeneous individual-level viral load trajectories and stochastic household transmission, and develop efficient inference methods to fit it to data. Since full joint inference is computationally difficult, we employ a cut approach that passes information from the within-host to the between-host model but not vice versa. This enables the data on viral loads to inform the transmission parameters such as the infection times and symptom onset thresholds. We evaluate the framework on simulated household outbreak data, assessing parameter recovery, computational efficiency, and the effect of viral load sampling frequency on inference quality. Parameter recovery is unbiased when the sampling frequency of the viral loads is high enough. When sampling is sparse, some bias is introduced, but incorporating external viral load data can mitigate this.