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Predicting the deng...
Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia : a modelling study
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- Ramadona, Aditya Lia (author)
- Umeå University,Umeå universitet,Institutionen för epidemiologi och global hälsa,Avdelningen för hållbar hälsa,Department of Health Behavior, Environment and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
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- Tozan, Yesim (author)
- School of Global Public Health, New York University, New York, United States
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- Wallin, Jonas (author)
- Lund University,Lunds universitet,Statistiska institutionen,Ekonomihögskolan,Department of Statistics,Lund University School of Economics and Management, LUSEM
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- Lazuardi, Lutfan (author)
- Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
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- Utarini, Adi (author)
- Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia
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- Rocklöv, Joacim, Professor, 1979- (author)
- Umeå universitet,Avdelningen för hållbar hälsa,Heidelberg Institute of Public Health & Heidelberg Interdisciplinary Centre for Scientific Computing, Heidelberg University, Heidelberg, Germany
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- Elsevier, 2023
- 2023
- English.
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In: The Lancet Regional Health - Southeast Asia. - : Elsevier. - 2772-3682. ; 15
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Abstract
Subject headings
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- Background: Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia.Methods: We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model.Findings: When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale.Interpretation: The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)
Keyword
- Arbovirus
- Big data
- Climate services
- Climate Variability
- Dengue
- DLNM
- Early warning
- Epidemic
- Forecasting model
- INLA
- Population mobility
- Rainfall
- Social media
- Spatiotemporal model
- Temperature
- Twitter
- Weather
- Arbovirus
- Dengue
- Temperature
- Rainfall
- Weather
- Climate Variability
- Population mobility
- Twitter
- Social media
- Forecasting model
- Early warning
- Epidemic
- Big data
- INLA
- DLNM
- Spatiotemporal model
- Climate services
Publication and Content Type
- ref (subject category)
- art (subject category)
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