Geospatial and machine learning approaches for malaria risk mapping in flood-prone districts: Implications for public health decision-making
- DOI
- 10.1016/j.puhe.2026.106367
- Published
- 2026-08
- Container
- Public Health
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.puhe.2026.106367,
title = {Geospatial and machine learning approaches for malaria risk mapping in flood-prone districts: Implications for public health decision-making},
author = {Yahya Khan and Umair Bin Nisar and Muhammad Rizwan Mughal and Athar Hussain and Sidra Daud and Siddique Akhtar Ehsan},
year = {2026},
journal = {Public Health},
doi = {10.1016/j.puhe.2026.106367},
url = {https://doi.org/10.1016/j.puhe.2026.106367}
}RIS
TY - JOUR TI - Geospatial and machine learning approaches for malaria risk mapping in flood-prone districts: Implications for public health decision-making AU - Yahya Khan AU - Umair Bin Nisar AU - Muhammad Rizwan Mughal AU - Athar Hussain AU - Sidra Daud AU - Siddique Akhtar Ehsan PY - 2026 JO - Public Health DO - 10.1016/j.puhe.2026.106367 UR - https://doi.org/10.1016/j.puhe.2026.106367 ER -
APA
Khan, Y., Nisar, U. B., Mughal, M. R., Hussain, A., Daud, S., & Ehsan, S. A. (2026). Geospatial and machine learning approaches for malaria risk mapping in flood-prone districts: Implications for public health decision-making. Public Health. https://doi.org/10.1016/j.puhe.2026.106367
Source records
- crossref · retrieved 2026-09-25T05:46:19.364Z