Dengue transmission heterogeneity across Indonesia's archipelago: climate-driven spatiotemporal patterns and policy implications
Djaafara, B. A.; Elyazar, I. R.; Silalahi, F. S.; Surya, A.; Handito, A.; Thohir, B.; Aryani, D.; Kamal, M.; Ramadona, A. L.; Gunawan, D.; Hipokrates, H.; Khoirun Nisa, A.; Prianto, E.; Samad, I.; Sugiarto, A.; Fornace, K.; Clapham, H. E.; Faria, N. R.; Mishra, S.
Show abstract
Indonesia has the highest dengue burden in Southeast Asia, with 488 of 514 districts reporting cases annually across its 17,000-island archipelago. Despite this substantial burden, spatiotemporal transmission patterns remain poorly characterised. We analysed province-level dengue surveillance data (2010-2024) from Indonesias Ministry of Health alongside local and regional climate variables to characterise heterogeneity in dengue periodicity and identify provinces where climate-based early warning may be feasible. Using wavelet phase analysis, dynamic time warping clustering, and distributed lag non-linear models, we examined relationships between climate and dengue incidence across 34 provinces. A systematic west-to-east gradient in dengue wave timing was identified, with Northern Sumatran provinces peaking earlier than other provinces, aligning with Australian-Asian monsoon progression. This gradient was robust in western Indonesia (Spearman{rho} = 0.7 between longitude and phase lag) but weakened in eastern provinces. Multi-annual outbreak peaks (2015-2016, 2023-2024) coincided with strong El Nino events, with mean incidence during strong El Nino years was 96% higher than other years. The Indian Ocean Dipole showed no significant association. Phase coherence analysis identified 18 provinces where precipitation-dengue timing was sufficiently consistent (coherence [≥]0.85) for potential early warning applications and DLNM confirmed significant dose-response associations in 11 of these. Indonesias dengue-climate relationships exhibit structured heterogeneity that precludes uniform national prediction approaches but may enable province-specific early warning in high-coherence areas. A two-tier system combining ENSO monitoring for strategic preparedness with local climate monitoring for tactical intervention timing could improve outbreak response across Indonesias diverse epidemiological landscapes.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Spatial Patterns of Dengue Incidence in Nepal During Record Outbreaks in 2022 and 2023: Implications for Public Health Interventions 98%
- Spatiotemporal tools for emerging and endemic disease hotspots in small areas--an analysis of dengue and chikungunya in Barbados, 2013 - 2016 95%
- Did COVID-19 or COVID-19 vaccines influence the patterns of Dengue in 2021: An exploratory analysis of two observational studies from North India 92%
Similar papers in this journal
- Genomic investigation of a dengue virus outbreak in Thiès, Senegal, in 2018 94%
- Malaria parasite density and detailed qualitative microscopy enhances large-scale profiling of infection endemicity in Nigeria 93%
- Insecticide resistance and population structure of the invasive malaria vector, Anopheles stephensi, from Fiq, Ethiopia 93%
Similar papers in this journal
- Geographical and temporal variation in reduction of malaria infection among children under five years of age throughout Nigeria 94%
- Utility of surveillance data for planning for dengue elimination in Yogyakarta, Indonesia: a scenario tree modelling approach 94%
- Spatial and epidemiological drivers of P. falciparum malaria among adults in the Democratic Republic of the Congo 92%
Similar papers in this journal
- Trends and Cross-Country Inequalities in Dengue, 1990-2021 95%
- Patterns of Aedes aegypti immature ecology and arboviral epidemic risks in peri-urban and intra-urban villages of Cocody-Bingerville, Cote d'Ivoire: insights from a dengue outbreak 94%
- Spatiotemporal Dynamics of Aedes aegypti and Culex quinquefasciatus populations in Miami-Dade County, Florida 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.