Approximate Inference for Interaction Dynamics in a Stochastic Kinetic Model of Multiple Pathogens from Aggregated Reports and Virological Data
Oksana Chkrebtii, Department of Statistics, The Ohio State University
Influenza and respiratory syncytial virus are the leading etiological agents of seasonal acute respiratory infections (ARI) around the world. Medical doctors typically base the diagnosis of ARI on patients' symptoms, and do not always conduct laboratory tests necessary to identify individual viruses, which limits the ability to study the interaction between specific pathogen dynamics and make public health recommendations. We consider a stochastic kinetic model for two interacting ARI pathogens circulating in a large population and an empirically motivated background process for infections with other pathogens causing similar symptoms. An extended pseudo-marginal inferential approach integrates multiple data sources and model components to distinguish individual pathogen dynamics and infer cross-immunity parameters from aggregate reports and a subset of virological data, which can be obtained at relatively small additional cost. We infer the parameters defining the pathogens' dynamics and interaction within a Bayesian hierarchical model and explore the posterior trajectories of infections for each illness based on data from six epidemic seasons collected by the state health department and a sentinel program at a general hospital in San Luis Potos\'i, Mexico.
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