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Risk Prediction of Emerging Sites Infested with Schistosome-Transmitting Oncomelania hupensis in Shanghai, China
Yu Zhou
Yu Zhou
Fudan University School of Public Health, Shanghai, China;
Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Yanjun Jin
Yanjun Jin
Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
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Yanfeng Gong
Yanfeng Gong
Fudan University School of Public Health, Shanghai, China;
Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Junhui Huang
Junhui Huang
Fudan University School of Public Health, Shanghai, China;
Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
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Jiamin Wang
Jiamin Wang
Fudan University School of Public Health, Shanghai, China;
Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Ning Xu
Ning Xu
Fudan University School of Public Health, Shanghai, China;
Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Qingwu Jiang
Qingwu Jiang
Fudan University School of Public Health, Shanghai, China;
Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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Qing Yu
yuqing_1@scdc.sh.cn
Qing Yu
Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China
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Yibiao Zhou
z_yibiao@hotmail.com
Yibiao Zhou
Fudan University School of Public Health, Shanghai, China;
Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China;
Fudan University Center for Tropical Disease Research, Shanghai, China;
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ABSTRACT.
Oncomelania hupensis, the sole intermediate host of Schistosoma japonicum, plays an essential role in the transmission of schistosomiasis. In 1985, snails were eliminated throughout Shanghai city. However, snail-infested sites have continuously emerged since the 1990s. The resurgence of snail habitats may signal the recurrence of schistosomiasis. Therefore, implementing continuous monitoring measures for snails is crucial, and predicting potential habitats for snails in Shanghai is essential for enhancing surveillance effectiveness, providing early warnings to health authorities, optimizing resource allocation, maintaining the elimination status of schistosomiasis in Shanghai, and ultimately, advancing the goal of eliminating schistosomiasis in China. Our research developed an ensemble model to predict the current and future distributions of snails in Shanghai by collecting emerging snail-infested records from 1991 to 2020 and integrating them with 19 environmental variables, including climate, geography, and socioeconomics. The ensemble model identified the annual average surface temperature as the most significant factor influencing snail occurrence. The highly suitable areas were primarily located in the northwestern part of Jinshan District and the southern part of Songjiang District. In the future, the southwestern part of Shanghai will continue to provide suitable habitats for snails in the long term. Therefore, even in areas where schistosomiasis has been eliminated, surveillance of snails and the disease should not be relaxed, and ongoing monitoring in these areas is necessary.
- Supplemental Materials (PDF 5759.0 KB)
Author Notes
Financial support: This research was supported by the
Current contact information: Yu Zhou, Yanfeng Gong, Junhui Huang, Jiamin Wang, Ning Xu, Qingwu Jiang, and Yibiao Zhou, Fudan University School of Public Health, Shanghai, China, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China, and Fudan University Center for Tropical Disease Research, Shanghai, China, E-mails: 23211020080@m.fudan.edu.cn, 22111020031@m.fudan.edu, 22211020054@m.fudan.edu.cn, 22211020179@m.fudan.edu.cn, 21111020043@m.fudan.edu.cn, jiangqw@fudan.edu.cn, and z_yibiao@hotmail.com. Yanjun Jin and Qing Yu, Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China, E-mails: jinyanjun@scdc.sh.cn and yuqing_1@scdc.sh.cn.
ProCite
RefWorks
Reference Manager
BibTeX
Zotero
EndNote
-
1.
Lo NC et al., 2022. Review of 2022 WHO guidelines on the control and elimination of schistosomiasis. Lancet Infect Dis 22: e327–e335.
-
2.
Zhou Y, Chen Y, Jiang Q, 2021. History of human schistosomiasis (bilharziasis) in China: From discovery to elimination. Acta Parasitol 66: 760–769.
-
3.
LoVerde PT, 2019. Schistosomiasis. Adv Exp Med Biol 1154: 45–70.
-
4.
Zhang L et al., 2023. Progress of schistosomiasis control in People’s Republic of China in 2022. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 35: 217–224.
-
5.
Gong Y, Li Y, Zhang L, Lv S, Xu J, Li S, 2021. The potential distribution prediction of Oncomelania hupensis based on newly emerging and reemergent habitats—China, 2015–2019. China CDC Wkly 3: 90–93.
-
6.
Elith J, Leathwick JR, 2009. Species distribution models: Ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40: 677–697.
-
7.
Gonzalez SC, Soto-Centeno JA, Reed DL, 2011. Population distribution models: Species distributions are better modeled using biologically relevant data partitions. BMC Ecol 11: 20.
-
8.
KMN MN, KR S, Paul Sreeram M, 2024. Muscling mussels: Understanding the invasive potential of the South American bivalve Mytella strigata (Hanley, 1843) in the northern Indian Ocean. Sci Total Environ 916: 170243.
-
9.
Tong Y-X et al., 2023. Prediction of the risk distributions for Anopheles sinensis, a vector for malaria in Shanghai, China. Am J Trop Med Hyg 108: 599–608.
-
10.
Tong Y, Jiang H, Xu N, Wang Z, Xiong Y, Yin J, Huang J, Chen Y, Jiang Q, Zhou Y, 2023. Global distribution of Culex tritaeniorhynchus and impact factors. Int J Environ Res Public Health 20: 4701.
-
11.
Wang Y, Zhao R, Zhou X, Zhang X, Zhao G, Zhang F, 2023. Prediction of potential distribution areas and priority protected areas of Agastache rugosa based on Maxent model and Marxan model. Front Plant Sci 14: 1200796.
-
12.
Xu N et al., 2023. Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models. Parasit Vectors 16: 377.
-
13.
Zhang J, Yue M, Hu Y, Bergquist R, Su C, Gao F, Cao ZG, Zhang Z, 2020. Risk prediction of two types of potential snail habitats in Anhui Province of China: Model-based approaches. PLoS Negl Trop Dis 14: e0008178.
-
14.
Gong Y-F et al., 2022. Projecting the proliferation risk of Oncomelania hupensis in China driven by SSPs: A multi-scenario comparison and integrated modeling study. Adv Clim Chang Res 13: 258–265.
-
15.
Hong Z, Li L, Zhang L, Wang Q, Xu J, Li S, Zhou XN, 2022. Elimination of Schistosomiasis japonica in China: From the One Health perspective. China CDC Wkly 4: 130–134.
-
16.
Wang X, Jiang X, Yu S, Lu S. Review of Schistosomiasis Eradication in Shanghai. Shanghai, China: Shanghai Science and Technology Press, 1988.
-
17.
Guo J-Y, Xu J, Zhang LJ, Lv S, Cao CL, Li SZ, Zhou XN, 2020. Surveillance on schistosomiasis in five provincial-level administrative divisions of the People’s Republic of China in the post-elimination era. Infect Dis Poverty 9: 136.
-
18.
Xu J et al., 2011. Retrospective investigation on national endemic situation of schistosomiasis. III. Changes of endemic situation in endemic rebounded counties after transmission of schistosomiasis under control or interruption. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 23: 350–357.
-
19.
Gong YF et al., 2022. Prediction of trends for fine-scale spread of Oncomelania hupensis in Shanghai Municipality based on supervised machine learning models. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 34: 241–251.
-
20.
Cui L, Shi J, 2012. Urbanization and its environmental effects in Shanghai, China. Urban Clim 2: 1–15.
-
21.
Zheng Y et al., 2022. Environmental determinants for snail density in Dongting Lake Region: An ecological study incorporating spatial regression. Am J Trop Med Hyg 107: 1178–1184.
-
22.
Echeverry-Cárdenas E, López-Castañeda C, Carvajal-Castro JD, Aguirre-Obando OA, 2021. Potential geographic distribution of the tiger mosquito Aedes albopictus (Skuse, 1894) (Diptera: Culicidae) in current and future conditions for Colombia. PLoS Negl Trop Dis 15: e0008212.
-
23.
Kamal M, Kenawy MA, Rady MH, Khaled AS, Samy AM, 2018. Mapping the global potential distributions of two arboviral vectors Aedes aegypti and Ae. albopictus under changing climate. PLoS One 13: e0210122.
-
24.
Warren DL et al., 2021. ENMTools 1.0: An R package for comparative ecological biogeography. Ecography 44: 504–511.
-
25.
Fick SE, Hijmans RJ, 2017. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int J Climatology 37: 4302–4315.
-
26.
Venter O et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat Commun 7: 12558.
-
27.
Venter O, Sanderson EW, Magrach A, Allan JR, Beher J, Jones KR, Levy MA, Watson JE, 2018. Last of the Wild Project, Version 3 (LWP-3): 2009 Human Footprint, 2018 Release. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).
-
28.
Wu T et al., 2014. An overview of BCC climate system model development and application for climate change studies. Acta Meteorol Sin 28: 34–56.
-
29.
Ayugi B, Shilenje ZW, Babaousmail H, Lim Kam Sian KTC, Mumo R, Dike VN, Iyakaremye V, Chehbouni A, Ongoma V, 2022. Projected changes in meteorological drought over East Africa inferred from bias-adjusted CMIP6 models. Nat Hazards (Dordr) 113: 1151–1176.
-
30.
Cheng X, Han Y, Lin J, Jiang F, Cai Q, Shi Y, Cui D, Wen X, 2023. Time to step up conservation: Climate change will further reduce the suitable habitats for the vulnerable species marbled polecat (Vormela peregusna). Animals (Basel) 13: 2341.
-
31.
Yang Y et al., 2018. Prediction of the potential global distribution for Biomphalaria straminea, an intermediate host for Schistosoma mansoni. PLoS Negl Trop Dis 12: e0006548.
-
32.
Thuiller W, Georges D, Engler R, 2016. biomod2: Ensemble Platform for Species Distribution Modeling. 3: r539 R Package Version. Available at: https://www.researchgate.net/publication/309762991_biomod2_Ensemble_Platform_for_Species_Distribution_Modeling. Accessed March 3, 2024.
-
33.
Thuiller W, Georges D & Gueguen M. biomod2: Ensemble Platform for Species Distribution Modeling. R Package v. 4.2-5[EB/OL]. Available at: https://biomodhub.github.io/biomod2/. Accessed: March 3, 2024.
-
34.
Čengić M, Rost J, Remenska D, Janse JH, Huijbregts MAJ, Schipper AM, 2020. On the importance of predictor choice, modelling technique, and number of pseudo-absences for bioclimatic envelope model performance. Ecol Evol 10: 12307–12317.
-
35.
Jiang R, Zou M, Qin Y, Tan G, Huang S, Quan H, Zhou J, Liao H, 2021. Modeling of the potential geographical distribution of three Fritillaria species under climate change. Front Plant Sci 12: 749838.
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