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Bayesian Compartmental Model for an Infectious Disease with Multiple Infectious States (306714)Grant Brown, University of Iowa
Jacob Oleson, University of Iowa
*Marie Ozanne, University of Iowa
Keywords: empirically adjusted reproductive number, hierarchical model, ISEARN, SAYVR, SEIR, carrier state model
Stochastic compartmental models comprise a class of techniques that can be used to study infection transmission dynamics. While various models have been developed to accommodate infections with an exposed (but not infectious) class or a less infectious carrier state, they do not accommodate an infection with two different infectious groups that are potentially equally important to maintaining infection in a population. They also do not accommodate an infection from which individuals can either recover or die. We propose a Bayesian Susceptible, Asymptomatic, sYmptomatic, recoVered, Removed (SAYVR) model to address this scenario. We also present an Infection Source-specific Empirically Adjusted Reproductive Number (ISEARN) to quantify contributions from each infectious class to maintaining infection in a population of interest. We apply these methods to study the transmission dynamics of visceral leishmaniasis in the Americas.