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Activity Number: 101 - Recent Advances in Statistical Modeling of Infectious Diseases
Type: Invited
Date/Time: Monday, August 3, 2020 : 1:00 PM to 2:50 PM
Sponsor: SSC (Statistical Society of Canada)
Abstract #309250
Title: Geographically-Dependent Individual-Level Models for Infectious Disease Transmission
Author(s): Rob Deardon*
Companies: University of Calgary
Keywords: Infectious disease modelling; Spatial statistics; Disease mapping; Conditional autoregressive; Markov chain Monte Carlo; Bayesian statistics

Numerous examples exist of infectious disease models that incorporate spatial distance and other covariates at the individual level. This has been most noticeable perhaps in agricultural case studies such as the UK 2001 foot and mouth disease epidemic. However, both in agriculture and public health, many salient covariates that display spatial structure are collected at a regional level. Here, we extend individual level infectious disease models of the type proposed by Deardon et al. (2010) to incorporate such spatially structured regional / aggregate level information. This is done primarily within the context of influenza data from Calgary, Canada. We discuss issues of both inference and computation.

Deardon, R., Brooks, S., Grenfell, B., Keeling, M., Tildesley, M., Savill, N., Shaw, D., Woolhouse, M. (2010). Inference for individual-level models of infectious diseases in large populations. Statistica Sinica, 20:239-261.

Authors who are presenting talks have a * after their name.

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