Leveraging seasonal dynamics to identify the strength of disease transmission along multiple environmental pathways
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Background
Many pathogens spread through multiple transmission pathways, making it difficult to identify the dominant transmission pathway for a given pathogen. Including a comprehensive set of relevant pathways in infectious disease transmission (IDT) models can provide insight into the contribution of each pathway to infections and allow for comparisons of the potential impact of control strategies targeting various pathways. Seasonal patterns in pathogen prevalence, combined with environmental testing, may allow us to differentiate the contributions of different pathways.
Methods
We conducted an identifiability analysis for an IDT model with three transmission pathways: direct person-to-person and two indirect, environmental pathways (water-to-person and food-to-person). We ran a series of simulations to understand the conditions under which we can successfully identify the dominant transmission pathway. Specifically, we explored the effects of different magnitudes of the transmission rate for each pathway and the seasonal timing of peak pathogen concentrations in water and food.
Results
Under the proposed data collection approach, we were able to successfully determine the dominant transmission pathway when simulated peak seasonality in water and food contamination differed by at least one month, even when the difference in the true transmission rates was small. However, we did not always correctly estimate the transmission rates used to generate the simulated data, even if we correctly determined the relative strengths of the pathways. 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 the true transmission rates.
Discussion
Our analysis found that IDT models can be a powerful tool to differentiate the contribution of different pathways to human infection with relevant empirical data on pathogen contamination in exposure sources. Our findings can help researchers design future studies and determine the feasibility of a study based on the seasonality in environmental samples if such data are available.
