ArticlePloS one2011
A hidden Markov model for analysis of frontline veterinary data for emerging zoonotic disease surveillance.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of surveillance systems and methods for early detection of exotic, new and re-emerging diseases in animal populations.Epidemiology and infection · 2015Pooled it
- Stochastic modeling of central apnea events in preterm infants.Physiological measurement · 2016Article
- Animal health syndromic surveillance: a systematic literature review of the progress in the last 5 years (2011-2016).Veterinary medicine (Auckland, N.Z.) · 2016Review
- Epidemiology and control of bovine ephemeral fever.Veterinary research · 2015Review
- Comparative spatial dynamics of Japanese encephalitis and acute encephalitis syndrome in Nepal.PloS one · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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