Identifying Individual Disease Dynamics in a Stochastic Multi-pathogen Model From Aggregated Reports and Laboratory Data
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Influenza and respiratory syncytial virus are the leading etiologic agents of seasonal acute respiratory infections around the world. Medical doctors usually base the diagnosis of acute respiratory infections on patients' symptoms and do not always conduct laboratory tests necessary to identify individual viruses due to cost constraints. This limits the ability to study the interaction between specific etiological agents responsible for illnesses and make public health recommendations. We establish a framework that enables the identification of individual pathogen dynamics given aggregate reports and a small number of laboratory tests for influenza and respiratory syncytial virus in a sample of patients, which can be obtained at relatively small additional cost. We consider a stochastic Susceptible-Infected-Recovered model of two interacting epidemics and infer the parameters defining their relationship in a Bayesian hierarchical setting as well as the posterior trajectories of infections for each illness over multiple years from the available data. We conduct inference based on data collected from a sentinel program at a general hospital in San Luis Potosí, México, interpret the results and make recommendations for future data collection strategies. Additional simulations are conducted to further study identifiability for these models. Supplementary materials are provided online.
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