A data-driven analysis and forecasting of Leishmaniasis-COVID-19 co-infection model using ensemble Kalman filter.

Said M, Stephano MA, Jung IH

DOI: 10.1038/s41598-026-55901-2
Published: 2026 Jun 8
Container: Scientific reports
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-026-55901-2,
  title = {A data-driven analysis and forecasting of Leishmaniasis-COVID-19 co-infection model using ensemble Kalman filter.},
  author = {Said M and Stephano MA and Jung IH},
  year = {2026},
  journal = {Scientific reports},
  doi = {10.1038/s41598-026-55901-2},
  url = {https://doi.org/10.1038/s41598-026-55901-2}
}

RIS

TY  - JOUR
TI  - A data-driven analysis and forecasting of Leishmaniasis-COVID-19 co-infection model using ensemble Kalman filter.
AU  - Said M
AU  - Stephano MA
AU  - Jung IH
PY  - 2026
JO  - Scientific reports
DO  - 10.1038/s41598-026-55901-2
UR  - https://doi.org/10.1038/s41598-026-55901-2
ER  -

APA

M, S., MA, S., & IH, J. (2026). A data-driven analysis and forecasting of Leishmaniasis-COVID-19 co-infection model using ensemble Kalman filter.. Scientific reports. https://doi.org/10.1038/s41598-026-55901-2

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pubmed · retrieved 2026-09-25T23:46:56.236Z