Evidence map›Paper›PMID 41634874›Full record

ArticleTropical medicine and health2026

Refining the suitable conditions index to predict dengue fever transmission in Bangladesh and Sri Lanka.

Jahirul Islam, Caroline K Dowsett, Xin Qi, Hilary Bambrick, Francesca D Frentiu, Wenbiao Hu

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Article in Tropical medicine and health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Jahirul IslamEcosystem Change and Population Health Research Group, Centre for Immunology and Infection Control, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Brisbane, QLD, 4059, Australia.
Caroline K DowsettEcosystem Change and Population Health Research Group, Centre for Immunology and Infection Control, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Brisbane, QLD, 4059, Australia.
Xin QiSchool of Public Health, Xi'an Jiaotong University, Xi'an, 710061, China.
Hilary BambrickNational Centre for Epidemiology and Population Health, The Australian National University, Canberra, ACT, 2601, Australia.
Francesca D FrentiuFaculty of Health, School of Biomedical Sciences, Centre for Immunology and Infection Control, Queensland University of Technology, Brisbane, Australia.
Wenbiao HuEcosystem Change and Population Health Research Group, Centre for Immunology and Infection Control, School of Public Health and Social Work, Queensland University of Technology, Kelvin Grove, Brisbane, QLD, 4059, Australia. w2.hu@qut.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe developed a Suitable Conditions Index (SCI) to predict dengue transmission in our prior work. However, the initial SCI was not refined with other important abiotic parameters. Therefore, in this study we refined the index by calculating three variants: temperature-based baseline daily average SCI (BDA-SCI), precipitation-weighted daily average SCI (PWDA-SCI), and waterbody-weighted daily average SCI (WWDA-SCI).

methodsWe used the district-wise data for two South Asian dengue-endemic countries: Bangladesh and Sri Lanka. Temperature-suitable days specific to Aedes aegypti (17.05-34.61 ℃) and Aedes albopictus (15.84-31.51 ℃) were averaged (BDA-SCI) and weighted by district-level precipitation (PWDA-SCI) and waterbody data (WWDA-SCI). We assessed the association between dengue incidence and each SCI, along with other covariates using negative binomial regression models. Furthermore, a binomial logistic regression model (BLR) was used to measure the predictive accuracy of each SCI.

resultsThe BDA-SCI for Ae. aegypti was highest in Sri Lanka at 0.96 (Standard deviation [SD] 0.04, range 0.85-1.00), compared to Bangladesh 0.68 (SD 0.06, range 0.61-0.87). For Ae. aegypti, WWDA-SCI (Relative risk [RR]

conclusionsOverall, the SCI method demonstrated a practical approach for identifying dengue vector suitability and transmission risk. Refining this index with location-specific climatic and environmental variables may enhance the model accuracy and may be used for future predictions under climate change scenarios. Thus, our refined SCI will assist in creating a reliable early warning system and inform the policymakers to initiate vector control strategies, including monitoring and eliminating dengue breeding sites and implementing biocontrol strategies within hotspots.

Indexed as

Aedes aegyptiAedes albopictusDengue predictionSuitable conditions indexVector-borne diseases

Identifiers

PMID41634874
PMCPMC12930654

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