Integrating causal inference and machine learning to quantify climate-malaria relationships: Evidence of temperature and rainfall thresholds from Colombian municipalities
- DOI
- 10.1371/journal.pgph.0005925
- Published
- 2026-02-05
- Container
- PLOS Global Public Health
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pgph.0005925,
title = {Integrating causal inference and machine learning to quantify climate-malaria relationships: Evidence of temperature and rainfall thresholds from Colombian municipalities},
author = {Juan David Gutiérrez},
year = {2026},
journal = {PLOS Global Public Health},
doi = {10.1371/journal.pgph.0005925},
url = {https://doi.org/10.1371/journal.pgph.0005925}
}RIS
TY - JOUR TI - Integrating causal inference and machine learning to quantify climate-malaria relationships: Evidence of temperature and rainfall thresholds from Colombian municipalities AU - Juan David Gutiérrez PY - 2026 JO - PLOS Global Public Health DO - 10.1371/journal.pgph.0005925 UR - https://doi.org/10.1371/journal.pgph.0005925 ER -
APA
Gutiérrez, J. D. (2026). Integrating causal inference and machine learning to quantify climate-malaria relationships: Evidence of temperature and rainfall thresholds from Colombian municipalities. PLOS Global Public Health. https://doi.org/10.1371/journal.pgph.0005925
Source records
- crossref · retrieved 2026-09-25T22:21:16.578Z