Predicting arboviral disease emergence using Bayesian networks: A case study of dengue virus in Western Australia

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

SUMMARY 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.

Original languageEnglish
Pages (from-to)54-66
Number of pages13
JournalEpidemiology and Infection
Volume145
Issue number1
DOIs
Publication statusPublished - 1 Jan 2017

Fingerprint

Western Australia
Dengue Virus
Disease Outbreaks
Aedes
Chikungunya virus
Yellow fever virus
Viruses
Dengue
Population Growth
Population Density
Culicidae

Cite this

@article{421e1e08f65e42579e3250ac335be196,
title = "Predicting arboviral disease emergence using Bayesian networks: A case study of dengue virus in Western Australia",
abstract = "SUMMARY 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.",
keywords = "Aedes albopictus, Bayesian Belief Network, dengue virus, Key words Aedes aegypti, risk mapping, risk modelling",
author = "Ho, {S. H.} and P. Speldewinde and A. Cook",
year = "2017",
month = "1",
day = "1",
doi = "10.1017/S0950268816002090",
language = "English",
volume = "145",
pages = "54--66",
journal = "Epidemiology and Infection",
issn = "0950-2688",
publisher = "Cambridge University Press",
number = "1",

}

TY - JOUR

T1 - Predicting arboviral disease emergence using Bayesian networks

T2 - A case study of dengue virus in Western Australia

AU - Ho, S. H.

AU - Speldewinde, P.

AU - Cook, A.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - SUMMARY 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.

AB - SUMMARY 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.

KW - Aedes albopictus

KW - Bayesian Belief Network

KW - dengue virus

KW - Key words Aedes aegypti

KW - risk mapping

KW - risk modelling

UR - http://www.scopus.com/inward/record.url?scp=84987677364&partnerID=8YFLogxK

U2 - 10.1017/S0950268816002090

DO - 10.1017/S0950268816002090

M3 - Article

VL - 145

SP - 54

EP - 66

JO - Epidemiology and Infection

JF - Epidemiology and Infection

SN - 0950-2688

IS - 1

ER -