High-resolution remote sensing-based spatial modeling for the prediction of potential risk areas of schistosomiasis in the Dongting Lake area, China

A comprehensive exploration of schistosomiasis: Global impact, molecular characterization, drug discovery, artificial intelligence and future prospects

Schistosomiasis, one of the neglected tropical diseases which affects both humans and animals, is caused by trematode worms of the genus Schistosoma. The disease is caused by several species of Schistosoma which affect several organs such as urethra, liver, bladder, intestines, skin and bile ducts. The life cycle of the disease involves an intermediate host (snail) and a mammalian host. It affects people who are in close proximity to water bodies where the intermediate host is abundant. Common clinical manifestations of the disease at various stages include fever, chills, headache, cough, dysuria, hyperplasia and hydronephrosis. To date, most of the control strategies are dependent on effective diagnosis, chemotherapy and public health education on the biology of the vectors and parasites. Microscopy (Kato-Katz) is considered the golden standard for the detection of the parasite, while praziquantel is the drug of choice for the mass treatment of the disease since no vaccines have yet been developed. Most of the previous reviews on schistosomiasis have concentrated on epidemiology, life cycle, diagnosis, control and treatment. Thus, a comprehensive review that is in tune with modern developments is needed. Here, we extend this domain to cover historical perspectives, global impact, symptoms and detection, biochemical and molecular characterization, gene therapy, current drugs and vaccine status. We also discuss the prospects of using plants as potential and alternative sources of novel anti-schistosomal agents. Furthermore, we highlight advanced molecular techniques, imaging and artificial intelligence that may be useful in the future detection and treatment of the disease. Overall, the proper detection of schistosomiasis using state-of-the-art tools and techniques, as well as development of vaccines or new anti-schistosomal drugs may aid in the elimination of the disease.

Development of New Technologies for Risk Identification of Schistosomiasis Transmission in China

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.

Risk prediction of two types of potential snail habitats in Anhui Province of China: Model-based approaches

Elimination of the intermediate snail host of Schistosoma is the most effective way to control schistosomiasis and the most important first step is to accurately identify the snail habitats. Due to the substantial resources required for traditional, manual snail-searching in the field, and potential risk of miss-classification of potential snail habitats by remote sensing, more convenient and precise methods are urgently needed. Snail data (N = 15,000) from two types of snail habitats (lake/marshland and hilly areas) in Anhui Province, a typical endemic area for schistosomiasis, were collected together with 36 environmental variables covering the whole province. Twelve different models were built and evaluated with indices, such as area under the curve (AUC), Kappa, percent correctly classified (PCC), sensitivity and specificity. We found the presence-absence models performing better than those based on presence-only. However, those derived from machine-learning, especially the random forest (RF) approach were preferable with all indices above 0.90. Distance to nearest river was found to be the most important variable for the lake/marshlands, while the climatic variables were more important for the hilly endemic areas. The predicted high-risk areas for potential snail habitats of the lake/marshland type exist mainly along the Yangtze River, while those of the hilly type are dispersed in the areas south of the Yangtze River. We provide here the first comprehensive risk profile of potential snail habitats based on precise examinations revealing the true distribution and habitat type, thereby improving efficiency and accuracy of snail control including better allocation of limited health resources.