ArticleBulletin of the World Health Organization2012
A Bayesian network approach to the study of historical epidemiological databases: modelling meningitis outbreaks in the Niger.
Article in Bulletin of the World Health Organization, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Preterm birth etiological pathways: a Bayesian networks and mediation analysis approach.Pediatric research · 2022Article
- Mathematical Modelling of Bacterial Meningitis Transmission Dynamics with Control Measures.Computational and mathematical methods in medicine · 2018Article
- Comparison of Models Analyzing a Small Number of Observed Meningitis Cases in Navrongo, Ghana.Journal of agricultural, biological, and environmental statistics · 2017Article
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7 authors.
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Abstract
objectiveTo develop a tool for evaluating the risk that an outbreak of meningitis will occur in a particular district of the Niger after outbreaks have been reported in other, specified districts of the country.
methodsA Bayesian network was represented by a graph composed of 38 nodes (one for each district in the Niger) connected by arrows. In the graph, each node directly influenced each of the "child" nodes that lay at the ends of the arrows arising from that node, according to conditional probabilities. The probabilities between "influencing" and "influenced" districts were estimated by analysis of databases that held weekly records of meningitis outbreaks in the Niger between 1986 and 2005. For each week of interest, each district was given a Boolean-variable score of 1 (if meningitis incidence in the district reached an epidemic threshold in that week) or 0.
findingsThe Bayesian network approach provided important and original information, allowing the identification of the districts that influence meningitis risk in other districts (and the districts that are influenced by any particular district) and the evaluation of the level of influence between each pair of districts.
conclusionBayesian networks offer a promising approach to understanding the dynamics of epidemics, estimating the risk of outbreaks in particular areas and allowing control interventions to be targeted at high-risk areas.
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