A multi-state climate epidemiology analysis of Lassa fever using interpretable machine learning and an early warning framework

Ayokunle John Tadema, Ayobami Emmanuel Mesioye, Sulymon A. Saka

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DOI: 10.1186/s12879-026-14203-8
Published: 2026-08-08
Container: BMC Infectious Diseases
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1186/s12879-026-14203-8,
  title = {A multi-state climate epidemiology analysis of Lassa fever using interpretable machine learning and an early warning framework},
  author = {Ayokunle John Tadema and Ayobami Emmanuel Mesioye and Sulymon A. Saka},
  year = {2026},
  journal = {BMC Infectious Diseases},
  doi = {10.1186/s12879-026-14203-8},
  url = {https://doi.org/10.1186/s12879-026-14203-8}
}

RIS

TY  - JOUR
TI  - A multi-state climate epidemiology analysis of Lassa fever using interpretable machine learning and an early warning framework
AU  - Ayokunle John Tadema
AU  - Ayobami Emmanuel Mesioye
AU  - Sulymon A. Saka
PY  - 2026
JO  - BMC Infectious Diseases
DO  - 10.1186/s12879-026-14203-8
UR  - https://doi.org/10.1186/s12879-026-14203-8
ER  -

APA

Tadema, A. J., Mesioye, A. E., & Saka, S. A. (2026). A multi-state climate epidemiology analysis of Lassa fever using interpretable machine learning and an early warning framework. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14203-8

Source records

crossref · retrieved 2026-11-07T05:01:32.818Z