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

Ong, Janet; Speldewinde, Peter; Cook, Angus

doi:10.1017/s0950268816002090

Cited by 12 publications

(8 citation statements)

References 25 publications

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“…This could be due to 1) old climatic data used in the modeling activity; 2) annual climate variability; or 3) real temperature increase or climate change, which warrant further investigation. Another limitation of the present study is the lack of human distribution data [52–54]. While environmental factors influence vector population growth, human population distribution influences the transmission of disease [54, 55].…”

Section: Discussionmentioning

confidence: 99%

“…Another limitation of the present study is the lack of human distribution data [52–54]. While environmental factors influence vector population growth, human population distribution influences the transmission of disease [54, 55]. In the arid areas of northern and northwestern China and in numerous parts of the Qinghai-Tibetan Plateau, the human population distribution is sparse; therefore, potential dengue fever risks are low, particularly in the frigid area on top of the plateau.…”

Section: Discussionmentioning

confidence: 99%

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Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability

Zheng

Zhong

et al. 2019

Infect Dis Poverty

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BackgroundAedes albopictus is a highly invasive mosquito species and a major vector of numerous viral pathogens. Many recent dengue fever outbreaks in China have been caused solely by the vector. Mapping of the potential distribution ranges of Ae. albopictus is crucial for epidemic preparedness and the monitoring of vector populations for disease control. Climate is a key factor influencing the distribution of the species. Despite field studies indicating seasonal population variations, very little modeling work has been done to analyze how environmental conditions influence the seasonality of Ae. albopictus. The aim of the present study was to develop a model based on available observations, climatic and environmental data, and machine learning methods for the prediction of the potential seasonal ranges of Ae. albopictus in China.MethodsWe collected comprehensive up-to-date surveillance data in China, particularly records from the northern distribution margin of Ae. albopictus. All records were assigned long-term (1970–2000) climatic data averages based on the WorldClim 2.0 data set. Machine learning regression tree models were developed using a 10-fold cross-validation method to predict the potential seasonal (or monthly) distribution ranges of Ae. albopictus in China at high resolution based on environmental conditions. The models were assessed based on sensitivity, specificity, and accuracy, using area under curve (AUC). WorldClim 2.0 and climatic and environmental data were used to produce environmental conduciveness (probability) prediction surfaces. Predicted probabilities were generated based on the averages of the 10 models.ResultsDuring 1998–2017, Ae. albopictus was observed at 200 out of the 242 localities surveyed. In addition, at least 15 new Ae. albopictus occurrence sites lay outside the potential ranges that have been predicted using models previously. The average accuracy was 98.4% (97.1–99.5%), and the average AUC was 99.1% (95.6–99.9%). The predicted Ae. albopictus distribution in winter (December–February) was limited to a small subtropical-tropical area of China, and Ae. albopictus was predicted to occur in northern China only during the short summer season (usually June–September). The predicted distribution areas in summer could reach northeastern China bordering Russia and the eastern part of the Qinghai-Tibet Plateau in southwestern China. Ae. albopictus could remain active in expansive areas from central to southern China in October and November.ConclusionsClimate and environmental conditions are key factors influencing the seasonal distribution of Ae. albopictus in China. The areas predicted to potentially host Ae. albopictus seasonally in the present study could reach northeastern China and the eastern slope of the Qinghai-Tibet Plateau. Our results present new evidence and suggest the expansion of systematic vector population monitoring activities and regular re-assessment of epidemic risk potential.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability

Zheng

Zhong

et al. 2019

Infect Dis Poverty

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“…In 2013, the first acquired DENV infection in WA after 1960 was reported 8 , thought to be caused by a bite from a single infected mosquito vector that was transiently introduced to the region. 34,35 . Therefore, case and vector surveillance as In Focus…”

Section: The Risk Of Dengue and Chikungunya Emergence In Australiamentioning

confidence: 99%

Movement of arboviruses between Indonesia and Western Australia

Harapan

Imrie

2021

Microbiol. Aust.

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Dengue virus (DENV) and chikungunya virus (DENV) are arboviruses of major public health importance. Monitoring circulation of medically important mosquito-borne viruses in the Indo Pacific region allows countries to predict disease outbreaks and prepare mitigation and control strategies. We have monitored long-term molecular epidemiology of DENV and CHIKV in Indonesia and Western Australia (WA), with febrile Western Australian travellers returning from Indonesia as sentinels. Our findings provide insights into the transmission dynamics of CHIKV genotypes and DENV serotypes, genotypes and lineages in the region and virus importation to WA. Our ongoing studies provide valuable and timely information on transmission of emerging and re-emerging arboviruses in the Indo Pacific region and furthermore provide detailed genomic data that inform our understanding of viral and epidemic virulence.

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“…Using mobile applications as means of communication also raises ethical, medical and legal issues regarding personal data security, privacy and confidentiality as individuals become more connected via the internet and technology [11,31,32]. Digital applications access and uptake ethics and rights principles adherence via open channels (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Newer technologies that have been used to create maps for the Zika and Ebola outbreak geographical distribution mapping that is a result of data collected from epidemiological, clinical and genomic operational research. HealthMap uses a text processing algorithm to collect data about the spread of Ebola [31,43]. Call Data Record (CDR) maps inform epidemiologists about the location and type of message being delivered, the identity of the caller and the exact location of the person who answered the call to build detailed maps of where people are located, where they are moving to not only inform government about population movement, but shape the nature and location of public health interventions [45,47].…”

Section: Introductionmentioning

confidence: 99%

Digital Technology and Mobile Applications Impact on Zika and Ebola Epidemics Data Sharing and Emergency Response

Tambo¹,

M²,

Cf³

et al. 2017

J Health Med Inform

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Increasing diversified world, era of international power diffusion and regionalisation as the world muddles through travel and trade to climate change requires promoting a more cohesive global regime to ameliorate the deficiencies in local and global 2013-2015 and 2015-2016 Ebola and Zika virus epidemics public health emergency of international concern respectively. Zika epidemics devastating and complex complications exposed the flaws in the global surveillance architecture to deal with cross-border health pandemics prevention and containment. This paper examines trade-off between technology-based data access and dissemination, Zika virus (ZIKV) epidemics complications and digital implications in care delivery solutions in pre-, during and post epidemics response and rebuilding. Our findings showed that IT-based health informatics and mobile applications are evolving and minimum global standards to open access and sharing of relevant epidemics data and information on risk communication. Public awareness and alertness, health promotion and education, counselling and guidelines on integrated syndrome surveillance and response were the most common goals and objectives during viral epidemics reduce and avert further transmission. The uses in healthcare services delivery, management, and planning was also documented and have played a critical role in raising awareness EVD and ZIKV travel restriction and delay pregnancy to women at reproductive age, effective use of personal protective equipment and cultural practices against EVD and ZIKV infections spread. Importantly, IT-based enhanced frontline improved collaborative data sharing and communication, coordinated response and recovery. The efficiency of IT-based data and information sharing and communication practices is of critical benefits for evidence informed early warning, social mobilization, advocacy, monitoring and evaluation capabilities. Mobile-based applications innovations during Zika and Ebola epidemics were very crucial for better evidence-based local and global preparedness and solutions to prevent and control epidemics impacts, promote shared benefits and economic growth.

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

Cited by 12 publications

References 25 publications

Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability

Seasonality modeling of the distribution of Aedes albopictus in China based on climatic and environmental suitability

Movement of arboviruses between Indonesia and Western Australia

Digital Technology and Mobile Applications Impact on Zika and Ebola Epidemics Data Sharing and Emergency Response

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