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Bayesian epidemiolo...
Bayesian epidemiological modeling over high-resolution network data
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- Engblom, Stefan (author)
- Uppsala universitet,Tillämpad beräkningsvetenskap,Avdelningen för beräkningsvetenskap
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- Eriksson, Robin (author)
- Uppsala universitet,Avdelningen för beräkningsvetenskap,Tillämpad beräkningsvetenskap
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- Widgren, Stefan (author)
- Department of Disease Control and Epidemiology, National Veterinary Institute, SE-751 89 Uppsala, Sweden
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(creator_code:org_t)
- Elsevier BV, 2020
- 2020
- English.
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In: Epidemics. - : Elsevier BV. - 1755-4365 .- 1878-0067. ; 32
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Abstract
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- Mathematical epidemiological models have a broad use, including both qualitative and quantitative applications. With the increasing availability of data, large-scale quantitative disease spread models can nowadays be formulated. Such models have a great potential, e.g., in risk assessments in public health. Their main challenge is model parameterization given surveillance data, a problem which often limits their practical usage. We offer a solution to this problem by developing a Bayesian methodology suitable to epidemiological models driven by network data. The greatest difficulty in obtaining a concentrated parameter posterior is the quality of surveillance data; disease measurements are often scarce and carry little information about the parameters. The often overlooked problem of the model's identifiability therefore needs to be addressed, and we do so using a hierarchy of increasingly realistic known truth experiments. Our proposed Bayesian approach performs convincingly across all our synthetic tests. From pathogen measurements of shiga toxin-producing Escherichia coli O157 in Swedish cattle, we are able to produce an accurate statistical model of first-principles confronted with data. Within this model we explore the potential of a Bayesian public health framework by assessing the efficiency of disease detection and -intervention scenarios.
Subject headings
- NATURVETENSKAP -- Matematik -- Beräkningsmatematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Computational Mathematics (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Keyword
- Bayesian parameter estimation
- Pathogen detection
- Disease intervention
- Synthetic likelihood
- Spatial stochastic models
Publication and Content Type
- ref (subject category)
- art (subject category)
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