Surveillance of animal diseases through implementation of a Bayesian spatio-temporal model: A simulation example with neurological syndromes in horses and West Nile Virus.

Hedell, Ronny; Andersson, Mats Gunnar; Faverjon, Céline Odette; Marcillaud-Pitel, Christel; Leblond, Agnès; Mostad, Petter (2019). Surveillance of animal diseases through implementation of a Bayesian spatio-temporal model: A simulation example with neurological syndromes in horses and West Nile Virus. Preventive veterinary medicine, 162, pp. 95-106. Elsevier 10.1016/j.prevetmed.2018.11.010

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A potentially sensitive way to detect disease outbreaks is syndromic surveillance, i.e. monitoring the number of syndromes reported in the population of interest, comparing it to the baseline rate, and drawing conclusions about outbreaks using statistical methods. A decision maker may use the results to take disease control actions or to initiate enhanced epidemiological investigations. In addition to the total count of syndromes there are often additional pieces of information to consider when assessing the probability of an outbreak. This includes clustering of syndromes in space and time as well as historical data on the occurrence of syndromes, seasonality of the disease, etc. In this paper, we show how Bayesian theory for syndromic surveillance applies to the occurrence of neurological syndromes in horses in France. Neurological syndromes in horses may be connected e.g. to West Nile Virus (WNV), a zoonotic disease of growing concern for public health in Europe. A Bayesian method for spatio-temporal cluster detection of syndromes and for determining the probability of an outbreak is presented. It is shown how surveillance can be performed simultaneously for a specific class of diseases (WNV or diseases similar to WNV in terms of the information available to the system) and a non-specific class of diseases (not similar to WNV in terms of the information available to the system). We also discuss some new extensions to the spatio-temporal models and the computational algorithms involved. It is shown step-by-step how data from historical WNV outbreaks and surveillance data for neurological syndromes can be used for model construction. The model is implemented using a Gibbs sampling procedure, and its sensitivity and specificity is evaluated. Finally, it is illustrated how predictive modelling of syndromes can be useful for decision making in animal health surveillance.

Item Type:

Journal Article (Original Article)

Division/Institute:

05 Veterinary Medicine > Research Foci > Veterinary Public Health / Herd Health Management
05 Veterinary Medicine > Department of Clinical Research and Veterinary Public Health (DCR-VPH) > Veterinary Public Health Institute
05 Veterinary Medicine > Department of Clinical Research and Veterinary Public Health (DCR-VPH)

UniBE Contributor:

Faverjon, Céline Odette

Subjects:

600 Technology > 630 Agriculture

ISSN:

0167-5877

Publisher:

Elsevier

Language:

English

Submitter:

Susanne Agnes Lerch

Date Deposited:

16 Apr 2019 16:08

Last Modified:

22 Oct 2019 18:08

Publisher DOI:

10.1016/j.prevetmed.2018.11.010

PubMed ID:

30621904

Uncontrolled Keywords:

Bayesian model Gibbs sampling Hidden markov model Spatio-temporal model Syndromic surveillance West Nile Virus

BORIS DOI:

10.7892/boris.126907

URI:

https://boris.unibe.ch/id/eprint/126907

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