Bayesian inference for disease transmission models informed by viral dynamics

2026-04-21

AI Summary

Existing multiscale infectious disease models often lack a statistically principled design, making it challenging to integrate within-host viral load dynamics with between-host transmission processes. This work proposes a novel multiscale Bayesian framework that jointly models heterogeneous individual viral load trajectories and stochastic within-household transmission, uniquely embedding viral kinetic data directly into the inference pipeline of the transmission model. By employing a “cut” strategy to enforce unidirectional information flow, the approach preserves parameter identifiability while enhancing computational efficiency. Empirical results demonstrate that high-frequency viral load sampling enables unbiased parameter estimation, and under sparse sampling regimes, incorporating external priors on viral load dynamics substantially reduces estimation bias.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Bayesian Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Security and Privacy: Large-scale security measurements

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.

Problem

• Bayesian inference
• multiscale modeling
• viral dynamics
• disease transmission
• statistical inference

Innovation

• multiscale modeling
• Bayesian inference
• viral dynamics
• household transmission
• cut model