Early-warning prediction of visceral leishmaniasis mortality using a multivariate STL–deep learning hybrid approach on 20 years of monthly time series
- DOI
- 10.3389/fpubh.2026.1754966
- Published
- 2026-03-26
- Container
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fpubh.2026.1754966,
title = {Early-warning prediction of visceral leishmaniasis mortality using a multivariate STL–deep learning hybrid approach on 20 years of monthly time series},
author = {Fathelrhman El Guma and Maaweya Awadalla and Halah Z. Al Rawi and Bashayer Saeed and Huda M. Alshanbari and Alshaikh A. Shokeralla and Bandar Alosaimi},
year = {2026},
journal = {Frontiers in Public Health},
doi = {10.3389/fpubh.2026.1754966},
url = {https://doi.org/10.3389/fpubh.2026.1754966}
}RIS
TY - JOUR TI - Early-warning prediction of visceral leishmaniasis mortality using a multivariate STL–deep learning hybrid approach on 20 years of monthly time series AU - Fathelrhman El Guma AU - Maaweya Awadalla AU - Halah Z. Al Rawi AU - Bashayer Saeed AU - Huda M. Alshanbari AU - Alshaikh A. Shokeralla AU - Bandar Alosaimi PY - 2026 JO - Frontiers in Public Health DO - 10.3389/fpubh.2026.1754966 UR - https://doi.org/10.3389/fpubh.2026.1754966 ER -
APA
Guma, F. E., Awadalla, M., Rawi, H. Z. A., Saeed, B., Alshanbari, H. M., Shokeralla, A. A., & Alosaimi, B. (2026). Early-warning prediction of visceral leishmaniasis mortality using a multivariate STL–deep learning hybrid approach on 20 years of monthly time series. Frontiers in Public Health. https://doi.org/10.3389/fpubh.2026.1754966
Source records
- crossref · retrieved 2026-09-26T19:40:10.122Z