title: Assessing the impact of a movement network on the spatiotemporal spread of infectious diseases.
abstract: Linking information on a movement network with space-time data on disease incidence is one of the key challenges in infectious disease epidemiology. In this article, we propose and compare two statistical frameworks for this purpose, namely, parameter-driven (PD) and observation-driven (OD) models. Bayesian inference in PD models is done using integrated nested Laplace approximations, while OD models can be easily fitted with existing software using maximum likelihood. The predictive performance of both formulations is assessed using proper scoring rules. As a case study, the impact of cattle trade on the spatiotemporal spread of Coxiellosis in Swiss cows, 2004-2009, is finally investigated.
authors.affiliation: Division of Biostatistics, Institute for Social and Preventive Medicine, University of Zurich, Hirschengraben 84, 8001 Zurich, Switzerland.
authors.forename: Birgit
authors.initials: B
authors.lastname: Schrödle
authors.forename: Leonhard
authors.initials: L
authors.lastname: Held
authors.forename: Håvard
authors.initials: H
authors.lastname: Rue
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D001699:Biometry; D002417:Cattle; D002418:Cattle Diseases / Q000635:transmission; D003381:Coxiella; D018562:Disease Transmission, Infectious / Q000706:statistics & numerical data* / Q000662:veterinary; D005260:Female; D016905:Gram-Negative Bacterial Infections / Q000635:transmission / Q000662:veterinary; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D013557:Switzerland