Predicting arboviral disease emergence using Bayesian networks: a case study of dengue virus in Western Australia.
A Bayesian Belief Network (BBN) for assessing the potential risk of dengue virus emergence and distribution in Western Australia (WA) is presented and used to identify possible hotspots of dengue outbreaks in summer and winter. The model assesses the probabilities of two kinds of events which must take place before an outbreak can occur: (1) introduction of the virus and mosquito vectors to places where human population densities are high; and (2) vector population growth rates as influenced by climatic factors. The results showed that if either Aedes aegypti or Ae. albopictus were to become established in WA, three centres in the northern part of the State (Kununurra, Fitzroy Crossing, Broome) would be at particular risk of experiencing an outbreak. The model can also be readily extended to predict the risk of introduction of other viruses carried by Aedes mosquitoes, such as yellow fever, chikungunya and Zika viruses.
School of Population Health (M431),The University of Western Australia,Crawley,Perth, WA,Australia.
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Centre of Excellence in Natural Resource Management,The University of Western Australia,Albany, WA,Australia.
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Speldewinde
School of Population Health (M431),The University of Western Australia,Crawley,Perth, WA,Australia.
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Cook
D000330:Aedes / Q000254:growth & development*; D000818:Animals; D001499:Bayes Theorem; D002980:Climate; D003715:Dengue / Q000453:epidemiology*; D005260:Female; D005544:Forecasting / Q000379:methods*; D006801:Humans; D000072138:Mosquito Vectors / Q000254:growth & development*; D018570:Risk Assessment; D012621:Seasons; D014904:Western Australia / Q000453:epidemiology

               Aedes aegypti
            ; 
               Aedes albopictus
            ; Bayesian Belief Network; dengue virus; risk mapping; risk modelling