Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study.

Arbovirus Big data Climate Variability Climate services DLNM Dengue Early warning Epidemic Forecasting model INLA Population mobility Rainfall Social media Spatiotemporal model Temperature Twitter Weather

Journal

The Lancet regional health. Southeast Asia
ISSN: 2772-3682
Titre abrégé: Lancet Reg Health Southeast Asia
Pays: England
ID NLM: 9918419282806676

Informations de publication

Date de publication:
Aug 2023
Historique:
received: 12 10 2022
revised: 23 11 2022
accepted: 25 04 2023
medline: 24 8 2023
pubmed: 24 8 2023
entrez: 24 8 2023
Statut: epublish

Résumé

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. 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. 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. 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. Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

Sections du résumé

Background UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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.
Funding UNASSIGNED
Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

Identifiants

DOI: 10.1016/j.lansea.2023.100209 PMID: 37614350 PMC: PMC10442971
pubmed: 37614350
doi: 10.1016/j.lansea.2023.100209
pii: S2772-3682(23)00069-0
pmc: PMC10442971
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100209

Informations de copyright

© 2023 The Authors.

Déclaration de conflit d'intérêts

JR and YT received financial support from the 10.13039/501100001862Swedish Research Council Formas (no. 2018-01754). ALR was funded partly by the Umeå Centre for Global Health Research with support from the 10.13039/501100001861Swedish Council for Working Life and Social Research (no. 2006-01512). JR and JW were financially supported by the Swedish research council 10.13039/501100001858VINNOVA (no. 2020-03367). JR received support from the 10.13039/100005156Alexander von Humboldt Foundation through the funding instrument of an Alexander von Humboldt Professorship endowed by the 10.13039/501100002347Federal Ministry of Education and Research in Germany.

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Auteurs

Aditya Lia Ramadona (AL)

Department of Epidemiology and Global Health, Umeå University, Umeå, 90187, Sweden.
Department of Public Health and Clinical Medicine, Units: Section of Sustainable Health, Umeå University, Umeå, 90187, Sweden.
Department of Health Behavior, Environment and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia.

Yesim Tozan (Y)

School of Global Public Health, New York University, New York, 10003, United States.

Jonas Wallin (J)

Department of Statistics, Lund University, Lund, 22363, Sweden.

Lutfan Lazuardi (L)

Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia.

Adi Utarini (A)

Department of Health Policy and Management, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, 55281, Indonesia.

Joacim Rocklöv (J)

Department of Public Health and Clinical Medicine, Units: Section of Sustainable Health, Umeå University, Umeå, 90187, Sweden.
Heidelberg Institute of Public Health & Heidelberg Interdisciplinary Centre for Scientific Computing, Heidelberg University, Heidelberg, 69120, Germany.

Classifications MeSH