Analyzing Spatially Aggregated Infectious Disease Data Using Time-Varying Individual-Level Models

• Activity Number: 650
• Type: Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Lin Zhang*+ and Rob Deardon
• Companies: University of Guelph, Department of Mathematics and Statistics and University of Guelph
• Keywords: individual-level models ; spatial-temporal epidemic ; Markov chain Monte Carlo ; Bayesian inference ; Time-varying infectivity

Abstract

For individual-level models (ILMs) of infectious disease spread, when the level at which we wish to model consists of an aggregated set of individual units (e.g. farms, health units or census regions), it is reasonable to assume time-varying infectivity. We generalize the ILMs of Deardon et al.(2010) to allow for time-varying susceptibility, infectivity, and/or contact functions. We also examine the use of models in which parameters of the time-varying infectivity curves are dependent upon some risk factors (e.g. population size) within an aggregated unit or region.