District-level measles outbreak prediction in Bangladesh using geospatial artificial intelligence.
- 1Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh.
- 2Department of Statistics, Begum Rokeya University, Rangpur, Bangladesh. [email protected].
Abstract
Background
Measles has re-emerged as a growing public health threat despite the availability of a safe and effective vaccine. In Bangladesh, recurring outbreaks and widening immunization disparities underscore the urgent need for proactive district-level risk assessment. This study developed an explainable machine learning framework to identify drivers of and forecast district-level measles outbreak risk in Bangladesh.Methods
District-level measles surveillance data (2017-2023) were combined with climatic, socio-demographic, healthcare capacity, and vaccination indicators. Eight machine learning classifiers (Decision Tree, Support Vector Machine, Naive Bayes, Random Forest, LightGBM, CatBoost, XGBoost, and an Elastic Net GLM) were compared using temporal train /test splitting, and nested cross-validation with F1-optimised decision thresholds selected within training fold. XGBoost was selected based on superior AUC-ROC and Matthews Correlation Coefficient. SHapley Additive exPlanations quantified predictor contributions on measles outbreak prediction. The model was retrained on the full 2017-2023 dataset to forecast district-level outbreak probabilities for 2024-2028, with 95% confidence intervals.Results
Measles showed marked spatiotemporal heterogeneity shifting from northeastern districts during 2017-2018 toward the Dhaka periphery during 2021-2023. XGBoost achieved an AUC-ROC of 0.838 (accuracy 81.3%; F1 0.840; MCC 0.624), supporting risk-based district prioritisation rather than precise case-level prediction. Sociodemographic factors contributed most to the outbreak prediction(41.37%), followed by climatic(39.22%) and healthcare-related variables(19.41%). Vaccination coverage showed lower standalone importance, reflecting contextual risk modulation. Minimum temperature, household size, and poverty headcount were the dominant predictors of measles outbreak prediction. Sylhet, Kushtia, and Dhaka were most vulnerable districts of measles outbreak, while Khulna and Chittagong divisions maintained persistently elevated risk through 2028.Conclusion
Measles outbreak risk in Bangladesh is shaped by climatic and socioeconomic vulnerabilities alongside vaccination gaps. Explainable machine learning can support precision immunisation and targeted surveillance, while requiring cautious interpretation of forecast uncertainty.
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BibTeX
@article{shiddik2026districtlevel,
title = {{District-level measles outbreak prediction in Bangladesh using geospatial artificial intelligence.}},
author = {Md Abu Bokkor Shiddik and Md Siddikur Rahman},
journal = {BMC infectious diseases},
year = {2026},
month = aug,
doi = {10.1186/s12879-026-14067-y},
url = {https://europepmc.org/article/MED/42967980},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - District-level measles outbreak prediction in Bangladesh using geospatial artificial intelligence.
AU - Md Abu Bokkor Shiddik
AU - Md Siddikur Rahman
PY - 2026
DA - 2026/08/04
JO - BMC infectious diseases
DO - 10.1186/s12879-026-14067-y
UR - https://europepmc.org/article/MED/42967980
AB - Background Measles has re-emerged as a growing public health threat despite the availability of a safe and effective vaccine. In Bangladesh, recurring outbreaks and widening immunization disparities underscore the urgent need for proactive district-level risk assessment. This study developed an explainable machine learning framework to identify drivers of and forecast district-level measles outbreak risk in Bangladesh.Methods District-level measles surveillance data (2017-2023) were combined with climatic, socio-demographic, healthcare capacity, and vaccination indicators. Eight machine learning classifiers (Decision Tree, Support Vector Machine, Naive Bayes, Random Forest, LightGBM, CatBoost, XGBoost, and an Elastic Net GLM) were compared using temporal train /test splitting, and nested cross-validation with F1-optimised decision thresholds selected within training fold. XGBoost was selected based on superior AUC-ROC and Matthews Correlation Coefficient. SHapley Additive exPlanations quantified predictor contributions on measles outbreak prediction. The model was retrained on the full 2017-2023 dataset to forecast district-level outbreak probabilities for 2024-2028, with 95% confidence intervals.Results Measles showed marked spatiotemporal heterogeneity shifting from northeastern districts during 2017-2018 toward the Dhaka periphery during 2021-2023. XGBoost achieved an AUC-ROC of 0.838 (accuracy 81.3%; F1 0.840; MCC 0.624), supporting risk-based district prioritisation rather than precise case-level prediction. Sociodemographic factors contributed most to the outbreak prediction(41.37%), followed by climatic(39.22%) and healthcare-related variables(19.41%). Vaccination coverage showed lower standalone importance, reflecting contextual risk modulation. Minimum temperature, household size, and poverty headcount were the dominant predictors of measles outbreak prediction. Sylhet, Kushtia, and Dhaka were most vulnerable districts of measles outbreak, while Khulna and Chittagong divisions maintained persistently elevated risk through 2028.Conclusion Measles outbreak risk in Bangladesh is shaped by climatic and socioeconomic vulnerabilities alongside vaccination gaps. Explainable machine learning can support precision immunisation and targeted surveillance, while requiring cautious interpretation of forecast uncertainty.
SN - 1471-2334
ER -