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[Application of machine learning models in schistosomiasis control: a review]
1Department of Epidemiology, School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Tropical Disease Research Center, Fudan University, Shanghai 200032, China.
Summary
Machine learning offers advanced solutions for schistosomiasis control, outperforming traditional models. This technology aids in precise disease prediction and risk assessment for effective public health strategies.
Area of Science:
- Public Health
- Artificial Intelligence
- Parasitology
Background:
- Schistosomiasis poses a significant global health challenge.
- Conventional statistical models struggle with the complex transmission of schistosomiasis, limiting precision control.
- Machine learning (ML) presents a promising alternative for disease management.
Purpose of the Study:
- To review the characteristics of ML models.
- To explore ML applications in schistosomiasis research, including intermediate host snails.
- To highlight ML's role in achieving precision control of schistosomiasis.
Main Methods:
- Literature review of machine learning models.
- Analysis of ML applications in schistosomiasis epidemiology and control.
- Synthesis of findings on ML for disease prediction and risk assessment.
Main Results:
- Machine learning models demonstrate significant advantages over traditional methods.
- ML is effective for predicting schistosomiasis outbreaks and assessing risks.
- Applications include optimizing control strategies and resource allocation.
Conclusions:
- Machine learning is a powerful tool for the precision control of schistosomiasis.
- ML enhances disease prediction, risk assessment, and strategic planning.
- Further research into ML applications can improve schistosomiasis eradication efforts.
