Simple jQuery Dropdowns
Please use this identifier to cite or link to this item:
Title: Bayesian spatial modeling for the joint analysis of zoonosis between human and animal populations
Authors: Andrew B. Lawson
Chawarat Rotejanaprasert
Medical University of South Carolina
Mahidol University
Keywords: Earth and Planetary Sciences;Environmental Science;Mathematics
Issue Date: 1-Dec-2018
Citation: Spatial Statistics. Vol.28, (2018), 8-20
Abstract: © 2018 Elsevier B.V. Human case monitoring is important for investigating disease burden; however, using human cases by themselves may not be sufficient for evaluating outbreaks. Enhanced surveillance of human cases should be considered, especially when other epidemiological indexes suggest that an outbreak is suspected or anticipated. In this paper we review the requirements for the effective modeling of both human and animal disease occurrence. We advocate for the use of joint models for these disease hosts, based on the need for flexible specification of model components and the flexible specification of correlation between animal and human disease. In the special case of infective diseases we advocate the use of both direct dependencies (via compartmental models) and the use of shared effects to allow the confounder effects that are common to be modeled. Case studies of integrated surveillance are provided focused on Tularemia human incidence with rodent population data from Finnish health care districts and the multivariate monitoring of West Nile virus activity in California, USA.
ISSN: 22116753
Appears in Collections:Scopus 2018

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.