| Autor: |
Bo Dong, Latifur Khan, Madison Smith, Jesus Trevino, Bingxin Zhao, Gabriel L. Hamer, Uriel A. Lopez-Lemus, Aracely Angulo Molina, Jailos Lubinda, Uyen-Sa D. T. Nguyen, Ubydul Haque |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Communications Medicine, Vol 2, Iss 1, Pp 1-11 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2730-664X |
| DOI: |
10.1038/s43856-022-00192-7 |
| Popis: |
Dong et al. analyse Aedes-borne diseases (ABDs) presence, local climate, and socio-demographic factors of 2,469 municipalities in Mexico, and apply machine learning to predict areas most at risk of ABDs clusters. Dengue was most prevalent, and socio-demographic and climatic factors influenced ABDs occurrence in different regions of Mexico. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink