title: Modeling two strains of disease via aggregate-level infectivity curves.
abstract: Well formulated models of disease spread, and efficient methods to fit them to observed data, are powerful tools for aiding the surveillance and control of infectious diseases. Our project considers the problem of the simultaneous spread of two related strains of disease in a context where spatial location is the key driver of disease spread. We start our modeling work with the individual level models (ILMs) of disease transmission, and extend these models to accommodate the competing spread of the pathogens in a two-tier hierarchical population (whose levels we refer to as 'farm' and 'animal'). The postulated interference mechanism between the two strains is a period of cross-immunity following infection. We also present a framework for speeding up the computationally intensive process of fitting the ILM to data, typically done using Markov chain Monte Carlo (MCMC) in a Bayesian framework, by turning the inference into a two-stage process. First, we approximate the number of animals infected on a farm over time by infectivity curves. These curves are fit to data sampled from farms, using maximum likelihood estimation, then, conditional on the fitted curves, Bayesian MCMC inference proceeds for the remaining parameters. Finally, we use posterior predictive distributions of salient epidemic summary statistics, in order to assess the model fitted.
authors.affiliation: Department of Mathematics and Statistics, University of Guelph, 50 Stone Road East, Guelph, ON, N1G 2W1, Canada. razvan_romanescu@hotmail.com.
authors.forename: Razvan
authors.initials: R
authors.lastname: Romanescu
authors.affiliation: Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary, HRIC 2AC66, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada.|Department of Mathematics and Statistics, Faculty of Science, University of Calgary, HRIC 2AC66, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada.
authors.forename: Rob
authors.initials: R
authors.lastname: Deardon
mesh_terms: D000820:Animal Diseases / Q000453:epidemiology* / Q000635:transmission; D000818:Animals; D000829:Animals, Domestic; D001499:Bayes Theorem; D003198:Computer Simulation; D058872:Epidemics / Q000706:statistics & numerical data / Q000662:veterinary*; D000072480:Farms / Q000706:statistics & numerical data; D016013:Likelihood Functions; D008390:Markov Chains; D055641:Mathematical Concepts; D008954:Models, Biological; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics
keywords: Individual level models; Model comparison; Multi-strain; Non-linear approximation