title: A hidden Markov model for analysis of frontline veterinary data for emerging zoonotic disease surveillance.
abstract: Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines.
authors.affiliation: Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, Ontario, Canada. crobertson@wlu.ca
authors.forename: Colin
authors.initials: C
authors.lastname: Robertson
authors.forename: Kate
authors.initials: K
authors.lastname: Sawford
authors.forename: Walimunige S N
authors.initials: WS
authors.lastname: Gunawardana
authors.forename: Trisalyn A
authors.initials: TA
authors.lastname: Nelson
authors.forename: Farouk
authors.initials: F
authors.lastname: Nathoo
authors.forename: Craig
authors.initials: C
authors.lastname: Stephen
mesh_terms: D000818:Animals; D002417:Cattle; D002418:Cattle Diseases / Q000453:epidemiology; D003141:Communicable Diseases / Q000453:epidemiology / Q000662:veterinary*; D003198:Computer Simulation; D004196:Disease Outbreaks; D005843:Geography; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D011159:Population Surveillance; D013188:Sri Lanka / Q000453:epidemiology; D015047:Zoonoses / Q000453:epidemiology*