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

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.

[1]  Huy T Nguyen,et al.  Interceptions of Aedes aegypti and Aedes albopictus in the port of Darwin, NT, Australia, 25 January and 5 February 2010 , 2010 .

[2]  Jonathan M. Levine,et al.  Forecasting Biological Invasions with Increasing International Trade , 2003 .

[3]  Eugene Charniak,et al.  Bayesian Networks without Tears , 1991, AI Mag..

[4]  B. Marcot,et al.  Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation , 2006 .

[5]  Kevin Fong-Rey Liu,et al.  Applying Bayesian belief networks to health risk assessment , 2012, Stochastic Environmental Research and Risk Assessment.

[6]  J. Mackenzie,et al.  Dengue in Australia. , 1996, Journal of medical microbiology.

[7]  Laura Uusitalo,et al.  Advantages and challenges of Bayesian networks in environmental modelling , 2007 .

[8]  David W Smith,et al.  Investigation of the First Case of Dengue Virus Infection Acquired in Western Australia in Seven Decades: Evidence of Importation of Infected Mosquitoes? , 2015, PLoS neglected tropical diseases.

[9]  B. Marcot,et al.  Bayesian belief networks: applications in ecology and natural resource management , 2006 .

[10]  Duane J. Gubler,et al.  Dengue, Urbanization and Globalization: The Unholy Trinity of the 21st Century , 2011, Tropical medicine and health.

[11]  O. Lucas Adetokunho,et al.  Special Programme for Research and Training in Tropical Diseases (TDR) TDR- FINAL REPORT SERIES , 2001 .

[12]  A. W. Sweeney,et al.  Australia's Dengue Risk Driven by Human Adaptation to Climate Change , 2009, PLoS neglected tropical diseases.

[13]  Andrew J Tatem,et al.  Global traffic and disease vector dispersal. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Kevin Fong-Rey.  Applying Bayesian belief networks to health risk assessment , 2011 .

[15]  P. Daszak,et al.  Predicting Pathogen Introduction: West Nile Virus Spread to Galápagos , 2006, Conservation biology : the journal of the Society for Conservation Biology.

[16]  C. Beierkuhnlein,et al.  Extrinsic Incubation Period of Dengue: Knowledge, Backlog, and Applications of Temperature Dependence , 2013, PLoS neglected tropical diseases.

[17]  C. G. Moore,et al.  The global compendium of Aedes aegypti and Ae. albopictus occurrence , 2015, Scientific Data.

[18]  S. Ritchie,et al.  Spatiotemporal patterns of Aedes aegypti populations in Cairns, Australia: assessing drivers of dengue transmission , 2013, Tropical medicine & international health : TM & IH.

[19]  A. Cook,et al.  A Bayesian Belief Network for Murray Valley encephalitis virus risk assessment in Western Australia , 2016, International Journal of Health Geographics.

[20]  A. Broom,et al.  Rainfall and vector mosquito numbers as risk indicators for mosquito-borne disease in central Australia. , 2003, Communicable diseases intelligence quarterly report.

[21]  K. Morita,et al.  An Updated Loop-Mediated Isothermal Amplification Method for Rapid Diagnosis of H5N1 Avian Influenza Viruses , 2011, Tropical medicine and health.

[22]  A. Pyke,et al.  Evidence in Australia for a Case of Airport Dengue , 2012, PLoS neglected tropical diseases.

[23]  Wayne G Landis,et al.  Analysis of Regional Scale Risk of Whirling Disease in Populations of Colorado and Rio Grande Cutthroat Trout Using a Bayesian Belief Network Model , 2014, Risk analysis : an official publication of the Society for Risk Analysis.

[24]  S. Ritchie,et al.  Aedes (Stegomyia) albopictus--a dengue threat for southern Australia? , 2005, Communicable diseases intelligence quarterly report.

[25]  B. Marcot,et al.  Using Bayesian belief networks in adaptive management , 2006 .

[26]  T. Blackburn,et al.  The role of propagule pressure in explaining species invasions. , 2005, Trends in ecology & evolution.

[27]  Craig R Williams,et al.  The development of predictive tools for pre‐emptive dengue vector control: a study of Aedes aegypti abundance and meteorological variables in North Queensland, Australia , 2010, Tropical medicine & international health : TM & IH.

[28]  Peter I Whelan,et al.  Dengue and climate change in Australia: predictions for the future should incorporate knowledge from the past , 2009, The Medical journal of Australia.

[29]  G. Kuno,et al.  Review of the factors modulating dengue transmission. , 1995, Epidemiologic reviews.

[30]  Todd Andrew Stephenson,et al.  An Introduction to Bayesian Network Theory and Usage , 2000 .

[31]  G. Dobler,et al.  Emergence of zoonotic arboviruses by animal trade and migration , 2010, Parasites & Vectors.