Designing Sampling Schemes for Population-Level Infectious Disease Studies

• Activity Number: 492
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Nadia Bifolchi*+ and Rob Deardon and Zeny Feng
• Companies: and University of Guelph and University of Guelph
• Keywords: sampling ; infectious disease modelling ; Bayesian inference ; MCMC

Abstract

Population-level epidemiological studies can provide insight into understanding infectious disease dynamics. However, the design of such studies often involves little quantitative planning in terms of maximizing the information obtained from the observed epidemic. Few design techniques and resources for such studies are available, as they involve modelling data from dynamic and nonlinear systems. To address this issue we performed a simulation study on a fictional epidemic set in an agricultural environment. Sampling was based at the farm level and covariates obtained included the number of animals, vaccination status and contact network of each sampled farm. Individual-level models were fit to the observed data within a Bayesian framework using Markov chain Monte Carlo. The proportion of population sampled and observation times were varied to determine the effect of study design on model fit and the conclusions that could be drawn about infection dynamics. Results showed that with constrained resources, fewer farms observed more frequently generally proved to be a good design strategy.