Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling
Abstract Background Poverty contributes to the transmission of schistosomiasis via multiple pathways, with the insufficiency of appropriate interventions being a crucial factor. The aim of this article is to provide more economical and feasible intervention measures for endemic areas with varying le...
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | Parasites & Vectors |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13071-023-06001-x |
| _version_ | 1797577803293523968 |
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| author | Qin Li Jin-Xin Zheng Tie-Wu Jia Xin-Yu Feng Chao Lv Li-Juan Zhang Guo-Jing Yang Jing Xu Xiao-Nong Zhou |
| author_facet | Qin Li Jin-Xin Zheng Tie-Wu Jia Xin-Yu Feng Chao Lv Li-Juan Zhang Guo-Jing Yang Jing Xu Xiao-Nong Zhou |
| author_sort | Qin Li |
| collection | DOAJ |
| description | Abstract Background Poverty contributes to the transmission of schistosomiasis via multiple pathways, with the insufficiency of appropriate interventions being a crucial factor. The aim of this article is to provide more economical and feasible intervention measures for endemic areas with varying levels of poverty. Methods We collected and analyzed the prevalence patterns along with the cost of control measures in 11 counties over the last 20 years in China. Seven machine learning models, including XGBoost, support vector machine, generalized linear model, regression tree, random forest, gradient boosting machine and neural network, were used for developing model and calculate marginal benefits. Results The XGBoost model had the highest prediction accuracy with an R 2 of 0.7308. Results showed that risk surveillance, snail control with molluscicides and treatment were the most effective interventions in controlling schistosomiasis prevalence. The best combination of interventions was interlacing seven interventions, including risk surveillance, treatment, toilet construction, health education, snail control with molluscicides, cattle slaughter and animal chemotherapy. The marginal benefit of risk surveillance is the most effective intervention among nine interventions, which was influenced by the prevalence of schistosomiasis and cost. Conclusions In the elimination phase of the national schistosomiasis program, emphasizing risk surveillance holds significant importance in terms of cost-saving. Graphical Abstract |
| first_indexed | 2024-03-10T22:13:10Z |
| format | Article |
| id | doaj.art-4852520783ab4245bd8cc9266d146fff |
| institution | Directory Open Access Journal |
| issn | 1756-3305 |
| language | English |
| last_indexed | 2024-03-10T22:13:10Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Parasites & Vectors |
| spelling | doaj.art-4852520783ab4245bd8cc9266d146fff2023-11-19T12:31:45ZengBMCParasites & Vectors1756-33052023-11-0116111110.1186/s13071-023-06001-xOptimized strategy for schistosomiasis elimination: results from marginal benefit modelingQin Li0Jin-Xin Zheng1Tie-Wu Jia2Xin-Yu Feng3Chao Lv4Li-Juan Zhang5Guo-Jing Yang6Jing Xu7Xiao-Nong Zhou8National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical DiseasesRuijin Hospital Affiliated to The Shanghai Jiao Tong University Medical SchoolNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical DiseasesNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical DiseasesNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical DiseasesNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical DiseasesSchool of Tropical Medicine, Hainan Medical UniversityNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical DiseasesNational Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), National Health Commission Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical DiseasesAbstract Background Poverty contributes to the transmission of schistosomiasis via multiple pathways, with the insufficiency of appropriate interventions being a crucial factor. The aim of this article is to provide more economical and feasible intervention measures for endemic areas with varying levels of poverty. Methods We collected and analyzed the prevalence patterns along with the cost of control measures in 11 counties over the last 20 years in China. Seven machine learning models, including XGBoost, support vector machine, generalized linear model, regression tree, random forest, gradient boosting machine and neural network, were used for developing model and calculate marginal benefits. Results The XGBoost model had the highest prediction accuracy with an R 2 of 0.7308. Results showed that risk surveillance, snail control with molluscicides and treatment were the most effective interventions in controlling schistosomiasis prevalence. The best combination of interventions was interlacing seven interventions, including risk surveillance, treatment, toilet construction, health education, snail control with molluscicides, cattle slaughter and animal chemotherapy. The marginal benefit of risk surveillance is the most effective intervention among nine interventions, which was influenced by the prevalence of schistosomiasis and cost. Conclusions In the elimination phase of the national schistosomiasis program, emphasizing risk surveillance holds significant importance in terms of cost-saving. Graphical Abstracthttps://doi.org/10.1186/s13071-023-06001-xSchistosomiasis eliminationMarginal benefit analysisMachine learning analysisModelingIntegrated control strategyCost-effectiveness |
| spellingShingle | Qin Li Jin-Xin Zheng Tie-Wu Jia Xin-Yu Feng Chao Lv Li-Juan Zhang Guo-Jing Yang Jing Xu Xiao-Nong Zhou Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling Parasites & Vectors Schistosomiasis elimination Marginal benefit analysis Machine learning analysis Modeling Integrated control strategy Cost-effectiveness |
| title | Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling |
| title_full | Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling |
| title_fullStr | Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling |
| title_full_unstemmed | Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling |
| title_short | Optimized strategy for schistosomiasis elimination: results from marginal benefit modeling |
| title_sort | optimized strategy for schistosomiasis elimination results from marginal benefit modeling |
| topic | Schistosomiasis elimination Marginal benefit analysis Machine learning analysis Modeling Integrated control strategy Cost-effectiveness |
| url | https://doi.org/10.1186/s13071-023-06001-x |
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