Integrating machine learning and data-driven modelling: predicting dengue outbreaks and revealing spatially heterogeneous climatic drivers in Brazil.

Li D, Li W, Zhang H, Hu S

DOI: 10.1098/rsif.2026.0071
Published: 2026 Sep 16
Container: Journal of the Royal Society, Interface
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1098/rsif.2026.0071,
  title = {Integrating machine learning and data-driven modelling: predicting dengue outbreaks and revealing spatially heterogeneous climatic drivers in Brazil.},
  author = {Li D and Li W and Zhang H and Hu S},
  year = {2026},
  journal = {Journal of the Royal Society, Interface},
  doi = {10.1098/rsif.2026.0071},
  url = {https://doi.org/10.1098/rsif.2026.0071}
}

RIS

TY  - JOUR
TI  - Integrating machine learning and data-driven modelling: predicting dengue outbreaks and revealing spatially heterogeneous climatic drivers in Brazil.
AU  - Li D
AU  - Li W
AU  - Zhang H
AU  - Hu S
PY  - 2026
JO  - Journal of the Royal Society, Interface
DO  - 10.1098/rsif.2026.0071
UR  - https://doi.org/10.1098/rsif.2026.0071
ER  -

APA

D, L., W, L., H, Z., & S, H. (2026). Integrating machine learning and data-driven modelling: predicting dengue outbreaks and revealing spatially heterogeneous climatic drivers in Brazil.. Journal of the Royal Society, Interface. https://doi.org/10.1098/rsif.2026.0071

Source records

pubmed · retrieved 2026-09-25T13:10:13.034Z