ArticlePLoS neglected tropical diseases2020
Spatiotemporal variability in dengue transmission intensity in Jakarta, Indonesia.
Article in PLoS neglected tropical diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Modelling the risk of West Nile virus infection in seven European countries from published serological and case notification data, 2008 to 2022.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2026Pooled it
- Overall and serotype-specific dengue virus transmission intensity in Mexico, 2016-2023: A modelling study of case-notification data.PLoS medicine · 2026Article
- Dengue epidemiology and transmission intensity across Panama during 2000-2024: a modelling study.Lancet regional health. Americas · 2026Article
- Estimating dengue force of infection from age-stratified surveillance data in Java, Indonesia.Journal of the Royal Society, Interface · 2025Article
- Priorities for modelling arbovirus transmission under climate change.Trends in molecular medicine · 2025Review
- Pre- and post-COVID-19 pandemic identification of dengue hotspots and exploration of population and environmental determinants of dengue in Quezon City, Philippines.Tropical medicine and health · 2025Article
- Reconciling heterogeneous dengue virus infection risk estimates from different study designs.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Reconciling heterogeneous dengue virus infection risk estimates from different study designs.medRxiv : the preprint server for health sciences · 2024Article
- Age-specific case data reveal varying dengue transmission intensity in US states and territories.PLoS neglected tropical diseases · 2024Article
- A simulation-based method to inform serosurvey design for estimating the force of infection using existing blood samples.PLoS computational biology · 2023Article
- From serological surveys to disease burden: a modelling pipeline for Chagas disease.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Dengue Dilemma in Nepal.Nepal journal of epidemiology · 2022Article
- Dengue Incidence Trends and Its Burden in Major Endemic Regions from 1990 to 2019.Tropical medicine and infectious disease · 2022Article
- Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease.PLoS neglected tropical diseases · 2022Article
- Estimating dengue transmission intensity from serological data: A comparative analysis using mixture and catalytic models.PLoS neglected tropical diseases · 2022Article
- Combining rapid diagnostic tests to estimate primary and post-primary dengue immune status at the point of care.PLoS neglected tropical diseases · 2022Article
- Spatiotemporal variations in exposure: Chagas disease in Colombia as a case study.BMC medical research methodology · 2022Article
- Estimating the annual dengue force of infection from the age of reporting primary infections across urban centres in endemic countries.BMC medicine · 2021Article
- Spatio-Temporal Patterns of Dengue Incidence in Medan City, North Sumatera, Indonesia.Tropical medicine and infectious disease · 2021Article
- A serological framework to investigate acute primary and post-primary dengue cases reporting across the Philippines.BMC medicine · 2020Article
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7 authors.
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Abstract
backgroundApproximately 70% of the global burden of dengue disease occurs on the Asian continent, where many large urban centres provide optimal environments for sustained endemic transmission and periodic epidemic cycles. Jakarta, the capital of Indonesia, is a densely populated megacity with hyperendemic dengue transmission. Characterization of the spatiotemporal distribution of dengue transmission intensity is of key importance for optimal implementation of novel control and prevention programmes, including vaccination. In this paper we use mathematical models to provide the first detailed description of spatial and temporal variability in dengue transmission intensity in Jakarta. METHODOLOGY/PRINCIPAL
findingsWe applied catalytic models in a Bayesian framework to age-stratified dengue case notification data to estimate dengue force of infection and reporting probabilities in 42 subdistricts of Jakarta. The model was fitted to yearly and average annual data covering a 10-year period between 2008 and 2017. We estimated a long-term average annual transmission intensity of 0.130 (95%CrI: 0.129-0.131) per year in Jakarta province, ranging from 0.090 (95%CrI: 0.077-0.103) to 0.164 (95%CrI: 0.153-0.174) across subdistricts. Annual average transmission intensity in Jakarta province during the 10-year period ranged from 0.012 (95%CrI: 0.011-0.013) in 2017 to 0.124 (95%CrI: 0.121-0.128) in 2016. CONCLUSIONS/SIGNIFICANCE: While the absolute number of dengue case notifications cannot be relied upon as a measure of endemicity, the age-distribution of reported dengue cases provides valuable insights into the underlying nature of transmission. Our estimates from yearly and average annual case notification data represent the first detailed estimates of dengue transmission intensity in Jakarta's subdistricts. These will be important to consider when assessing the population-level impact and cost-effectiveness of potential control and prevention programmes in Jakarta province, such as the controlled release of Wolbachia-carrying mosquitoes and vaccination.
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