Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis
ABSTRACTDengue is an important emerging vector-borne disease in Bhutan. This study aimed to quantify the spatial and temporal patterns of dengue and their relationship to environmental factors in dengue-affected areas at the sub-district level. A multivariate zero-inflated Poisson regression model w...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-01-01
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| Series: | Emerging Microbes and Infections |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22221751.2020.1775497 |
| _version_ | 1827320731615100928 |
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| author | Tsheten Tsheten Archie C.A. Clements Darren J. Gray Sonam Wangchuk Kinley Wangdi |
| author_facet | Tsheten Tsheten Archie C.A. Clements Darren J. Gray Sonam Wangchuk Kinley Wangdi |
| author_sort | Tsheten Tsheten |
| collection | DOAJ |
| description | ABSTRACTDengue is an important emerging vector-borne disease in Bhutan. This study aimed to quantify the spatial and temporal patterns of dengue and their relationship to environmental factors in dengue-affected areas at the sub-district level. A multivariate zero-inflated Poisson regression model was developed using a Bayesian framework with spatial and spatiotemporal random effects modelled using a conditional autoregressive prior structure. The posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. A total of 708 dengue cases were notified through national surveillance between January 2016 and June 2019. Individuals aged ≤14 years were found to be 53% (95% CrI: 42%, 62%) less likely to have dengue infection than those aged >14 years. Dengue cases increased by 63% (95% CrI: 49%, 77%) for a 1°C increase in maximum temperature, and decreased by 48% (95% CrI: 25%, 64%) for a one-unit increase in normalized difference vegetation index (NDVI). There was significant residual spatial clustering after accounting for climate and environmental variables. The temporal trend was significantly higher than the national average in eastern sub-districts. The findings highlight the impact of climate and environmental variables on dengue transmission and suggests prioritizing high-risk areas for control strategies. |
| first_indexed | 2024-03-07T17:24:39Z |
| format | Article |
| id | doaj.art-f676bae1783248e6b7299baa8023dbad |
| institution | Directory Open Access Journal |
| issn | 2222-1751 |
| language | English |
| last_indexed | 2024-04-25T00:51:11Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Emerging Microbes and Infections |
| spelling | doaj.art-f676bae1783248e6b7299baa8023dbad2024-03-11T16:04:24ZengTaylor & Francis GroupEmerging Microbes and Infections2222-17512020-01-01911360137110.1080/22221751.2020.1775497Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysisTsheten Tsheten0Archie C.A. Clements1Darren J. Gray2Sonam Wangchuk3Kinley Wangdi4Department of Global Health, Research School of Population Health, Australian National University, Canberra, AustraliaFaculty of Health Sciences, Curtin University, Perth, AustraliaDepartment of Global Health, Research School of Population Health, Australian National University, Canberra, AustraliaRoyal Centre for Disease Control, Ministry of Health, Thimphu, BhutanDepartment of Global Health, Research School of Population Health, Australian National University, Canberra, AustraliaABSTRACTDengue is an important emerging vector-borne disease in Bhutan. This study aimed to quantify the spatial and temporal patterns of dengue and their relationship to environmental factors in dengue-affected areas at the sub-district level. A multivariate zero-inflated Poisson regression model was developed using a Bayesian framework with spatial and spatiotemporal random effects modelled using a conditional autoregressive prior structure. The posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. A total of 708 dengue cases were notified through national surveillance between January 2016 and June 2019. Individuals aged ≤14 years were found to be 53% (95% CrI: 42%, 62%) less likely to have dengue infection than those aged >14 years. Dengue cases increased by 63% (95% CrI: 49%, 77%) for a 1°C increase in maximum temperature, and decreased by 48% (95% CrI: 25%, 64%) for a one-unit increase in normalized difference vegetation index (NDVI). There was significant residual spatial clustering after accounting for climate and environmental variables. The temporal trend was significantly higher than the national average in eastern sub-districts. The findings highlight the impact of climate and environmental variables on dengue transmission and suggests prioritizing high-risk areas for control strategies.https://www.tandfonline.com/doi/10.1080/22221751.2020.1775497DenguetemporalspatialBayesianBhutan |
| spellingShingle | Tsheten Tsheten Archie C.A. Clements Darren J. Gray Sonam Wangchuk Kinley Wangdi Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis Emerging Microbes and Infections Dengue temporal spatial Bayesian Bhutan |
| title | Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis |
| title_full | Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis |
| title_fullStr | Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis |
| title_full_unstemmed | Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis |
| title_short | Spatial and temporal patterns of dengue incidence in Bhutan: a Bayesian analysis |
| title_sort | spatial and temporal patterns of dengue incidence in bhutan a bayesian analysis |
| topic | Dengue temporal spatial Bayesian Bhutan |
| url | https://www.tandfonline.com/doi/10.1080/22221751.2020.1775497 |
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