Infectious Disease
Leveraging seasonal dynamics to identify the strength of disease transmission along multiple environmental pathways: structural identifiability analysis Miwa Watanabe* Miwa Watanabe Kayoko Shioda Matthew Freeman Karen Levy Andrew Brouwer
Background: Many pathogens are transmitted via multiple different pathways, so it is important to include a comprehensive set of relevant pathways in infectious disease transmission (IDT) models to understand the contribution of each pathway. Such IDT models can help to compare the impact of potential control strategies targeting specific pathways and to identify the most effective one. However, even if we collect relevant empirical data, it is not clear when IDT models are able to differentiate multiple pathways and estimate their transmission rates.
Methods: To answer this question, we conducted structural identifiability analysis for an IDT model with three transmission pathways: direct person-to-person and two indirect environmental pathways (food-to-person and water-to-person). We ran a series of simulations to understand what kind of conditions are necessary to successfully identify the dominant transmission pathway. We specifically explored the effects of different magnitudes of transmission rates via water and food and the seasonal timing of peak pathogen concentrations in food and water.
Results: The IDT model was able to successfully determine the dominant transmission pathway when the simulated seasonality in water and food contamination was different by three months or six months, even when the difference in the true transmission rates was small. When the simulated contamination in water and food peaked at the same time, the IDT model failed to determine the dominant pathway, regardless of the relative magnitudes of true transmission rates.
Discussion: Our analysis found that the IDT model can be a powerful tool to differentiate the contribution of different pathways to human infection with relevant empirical data on pathogen contamination data from exposure sources. Our findings can help researchers design their future studies and determine the feasibility of a study based on the seasonality in environmental samples if such data are available.
