OBJECTIVE: To 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. METHODS: A 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. FINDINGS: The 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. CONCLUSION: Bayesian 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.
OBJECTIVE: To 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. METHODS: A 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. FINDINGS: The 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. CONCLUSION: Bayesian 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.
Authors: J A D Leake; M L Kone; A A Yada; L F Barry; G Traore; A Ware; T Coulibaly; A Berthe; H Mambu Ma Disu; N E Rosenstein; B D Plikaytis; K Esteves; J Kawamata; J D Wenger; D L Heymann; B A Perkins Journal: Bull World Health Organ Date: 2002 Impact factor: 9.408
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