有哪些信誉好的足球投注网站a hidden markov model for analysis of frontline veterinary data for emerging zoonotic disease surveillance隐马尔科夫模型分析前线兽医新兴人畜共患疾病监测的数据.pdfVIP
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a hidden markov model for analysis of frontline veterinary data for emerging zoonotic disease surveillance隐马尔科夫模型分析前线兽医新兴人畜共患疾病监测的数据
A Hidden Markov Model for Analysis of Frontline Veterinary Data for Emerging Zoonotic Disease Surveillance 1,2 3 4 2 Colin Robertson *, Kate Sawford , Walimunige S. N. Gunawardana , Trisalyn A. Nelson , Farouk Nathoo5, Craig Stephen3 1 Department of Geography and Environmental Studies, Wilfrid Laurier University, Waterloo, Ontario, Canada, 2 Spatial Pattern Analysis and Research Laboratory, Department of Geography, University of Victoria, Victoria, British Columbia, Canada, 3 Faculty of Veterinary Medicine, University of Calgary, Calgary, Alberta, Canada, 4 Faculty of Veterinary Medicine and Animal Science, University of Peradeniya, Peradeniya, Central Province, Sri Lanka, 5 Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada 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 probab
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