Effects of environment and globalization on the double and triple burdens of infection symptoms among under-five children across low-middle income countries using machine learning algorithms
The study found substantial variation in the prevalences of D/TB of illness among children under five and identified several environmental and sociodemographic predictors of these health outcomes. The Random Forest algorithm performed best in predicting these burdens. The study emphasized how integrating environmental and sociodemographic data with machine learning can enhance targeted interventions to reduce childhood infectious disease burdens in low- and middle-income countries.
Authors
- Haile Mekonnen Fenta (ORCID: https://orcid.org/0000-0002-3919-2762)
- Aino K. Rantala (ORCID: https://orcid.org/0000-0002-4845-076X)
- Adeladza Kofi Amegah (ORCID: https://orcid.org/0000-0001-5868-6402)
- Inês Paciência (ORCID: https://orcid.org/0000-0001-5762-7814)
- Jouni J. K. Jaakkola (ORCID: https://orcid.org/0000-0003-4168-4683)
Institutions
- Finnish Meteorological Institute (FI)
- University of Cape Coast (GH)
- Oulu University Hospital (FI)
- Bahir Dar University (ET)
- Oulu University of Applied Sciences (FI)
- University of Oulu (FI)
Publication Details
- Journal
- Infectious Diseases of Poverty
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1186/s40249-025-01387-5
- Primary Topic
- Immune responses and vaccinations
- Type
- article
- Field-Weighted Citation Impact
- 0.00