Background We 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). Methods We 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. Results The 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]aegypti = 1.06, p = 0.056, Akaike Information Criteria [AIC] 1218.6) and BDA-SCI (RRaegypti = 1.05, p = 0.008, AIC 1214.2) had a stronger association with dengue incidence in Bangladesh than PWDA-SCI (RRaegypti = 1.06, p = 0.056, AIC 1232.2), whereas in Sri Lanka, PWDA-SCI (RRaegypti = 1.06, p = 0.056, AIC 472.63) performed better (AICBDA-SCI: 481.36, AICWWDA-SCI: 475.89) in the multivariable model, similar to the findings for Ae. albopictus. The BLR model predicted districts with above-median dengue incidence, and model performance indicated that BDA-SCI achieved highest accuracy for Bangladesh, while WWDA-SCI performed best for Sri Lanka, based on higher sensitivity and the Area Under the Curve value. Conclusions Overall, 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.

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

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2026年2月3日

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

摘要

Background

We 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).

Methods

We 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.

Results

The 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]_aegypti = 1.06, p = 0.056, Akaike Information Criteria [AIC] 1218.6) and BDA-SCI (RR_aegypti = 1.05, p = 0.008, AIC 1214.2) had a stronger association with dengue incidence in Bangladesh than PWDA-SCI (RR_aegypti = 1.06, p = 0.056, AIC 1232.2), whereas in Sri Lanka, PWDA-SCI (RR_aegypti = 1.06, p = 0.056, AIC 472.63) performed better (AIC_BDA-SCI: 481.36, AIC_WWDA-SCI: 475.89) in the multivariable model, similar to the findings for Ae. albopictus. The BLR model predicted districts with above-median dengue incidence, and model performance indicated that BDA-SCI achieved highest accuracy for Bangladesh, while WWDA-SCI performed best for Sri Lanka, based on higher sensitivity and the Area Under the Curve value.

Conclusions

Overall, 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.