A Bayesian Belief Network for Murray Valley encephalitis virus risk assessment in Western Australia

Abstract

BackgroundMurray Valley encephalitis virus (MVEV) is a clinically important virus in Australia responsible for a number of epidemics over the past century. Since there is no vaccine for MVEV, other preventive health measures to curtail its spread must be considered, including the development of predictive risk models and maps to help direct public health interventions. This article aims to support these approaches by presenting a model for assessing MVEV risk in Western Australia (WA).MethodsA Bayesian Belief Network (BBN) for assessing MVEV risk was developed and used to quantify and map disease risks in WA. The model combined various abiotic, biotic, and anthropogenic factors that might affect the risk of MVEV into a predictive framework, based on the ecology of the major mosquito vector and waterbird hosts of MVEV. It was further refined and tested using retrospective climate data from 4 years (2000, 2003, 2009, and 2011).ResultsImplementing the model across WA demonstrated that it could predict locations of human MVEV infection and sentinel animal seroconversion in the 4 years tested with some degree of accuracy. In general, risks are highest in the State’s north and lower in the south. The model predicted that short-term climate change, based on the Intergovernmental Panel on Climate Change’s A1B emissions scenario, would decrease MVEV risks in summer and autumn, largely due to higher temperatures decreasing vector survival.ConclusionsTo our knowledge, this is the first model to use a BBN to quantify MVEV risks in WA. The models and maps developed here may assist public health agencies in preparing for and managing Murray Valley encephalitis in the future. In its current form, the model is knowledge-driven and based on the analysis of potential risk factors that affect the dynamics of MVEV using retrospective data. Further work and additional testing should be carried out to test its validity in future years.Electronic supplementary materialThe online version of this article (doi:10.1186/s12942-016-0036-x) contains supplementary material, which is available to authorized users.

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

“…As with the Murray Valley encephalitis virus risk model developed by the authors [9], the aim was to demonstrate the feasibility of using BBNs for predicting and mapping mosquitoborne arbovirus risks in a particular context. In addition, such models can be used to guide prevention strategies against the introduction of other exotic Aedes-borne arboviruses into WA, such as chikungunya, yellow fever, and Zika viruses.…”

“…The procedure for risk modelling and mapping as outlined below is similar to that discussed in detail in [9].…”

“…The entire process was iterative and continued until we obtained a satisfactory risk model and risk maps that reflected the literature and expert opinion. The model is therefore a 'knowledge-driven system', with CPTs populated according to a method recently described in Ho et al [9]. Briefly, a numerical score/ weight was assigned to every possible combination of parent node states.…”

“…[9][10][11]). The probability distribution of each node is either assigned based on a prior distribution model (for input nodes), or calculated using Bayes' Theorem from prior probabilities ('child' nodes) [12,13].…”

The ecology and epidemiology of Ross River and Murray Valley encephalitis viruses in Western Australia: examples of One Health in Action

“… 57 Unfortunately, neither of these two models is directly relevant to WA and, instead, a Bayesian Belief Network model has been proposed that combines various abiotic, biotic and anthropogenic factors that might affect the risk of MVEV into a predictive model, based on the ecology of the major mosquito vector and waterbird hosts of MVEV. 58 So far its accuracy has not been prospectively tested, and it will require considerable coordination to gather the data required.…”