Journal
PATHOGENS
Volume 11, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/pathogens11020224
Keywords
schistosomiasis; risk identification; pathogen biology; immunology; 3S technology; mathematical modeling; molecular biology; big data; artificial intelligence; China
Categories
Funding
- National Natural Science Foundation of China [82173586]
- Jiangsu Provincial Department of Science and Technology [BZ2020003]
- Medical Research Project of Jiangsu Provincial Health Commission [M2021102, x202105]
- Scientific Research Project of Wuxi Municipal Health Commission [M202121]
- Wuxi Science and Technology Development Fund Project [Y20212048]
- Public Health Research Center of Jiangnan University [JUPH201837, JUPH202008]
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Schistosomiasis is a serious parasitic disease that is of great importance to the prevention and control work in China. The combination of traditional and new technologies allows for accurate assessment of schistosomiasis transmission risk, providing more effective approaches to accelerate the elimination of schistosomiasis.
Schistosomiasis is serious parasitic disease with an estimated global prevalence of active infections of more than 190 million. Accurate methods for the assessment of schistosomiasis risk are crucial for schistosomiasis prevention and control in China. Traditional approaches to the identification of epidemiological risk factors include pathogen biology, immunology, imaging, and molecular biology techniques. Identification of schistosomiasis risk has been revolutionized by the advent of computer network communication technologies, including 3S, mathematical modeling, big data, and artificial intelligence (AI). In this review, we analyze the development of traditional and new technologies for risk identification of schistosomiasis transmission in China. New technologies allow for the integration of environmental and socio-economic factors for accurate prediction of the risk population and regions. The combination of traditional and new techniques provides a foundation for the development of more effective approaches to accelerate the process of schistosomiasis elimination.
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