{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T17:10:55Z","timestamp":1740157855485,"version":"3.37.3"},"reference-count":35,"publisher":"Korean Society of Epidemiology","license":[{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81773487","81973102"],"award-info":[{"award-number":["81773487","81973102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["e-epih.org"],"crossmark-restriction":true},"short-container-title":["Epidemiol Health"],"abstract":"<jats:p>OBJECTIVES: To achieve the ambitious goal of eliminating schistosome infections, the Chinese government has implemented diverse control strategies. This study explored the progress of the 2 most recent national schistosomiasis control programs in an endemic area along the Yangtze River in China.METHODS: We obtained village-level parasitological data from cross-sectional surveys combined with environmental data in Anhui Province, China from 1997 to 2015. A convolutional neural network (CNN) based on a hierarchical integro-difference equation (IDE) framework (i.e., CNN-IDE) was used to model spatio-temporal variations in schistosomiasis. Two traditional models were also constructed for comparison with 2 evaluation indicators: the mean-squared prediction error (MSPE) and continuous ranked probability score (CRPS).RESULTS: The CNN-IDE model was the optimal model, with the lowest overall average MSPE of 0.04 and the CRPS of 0.19. From 1997 to 2011, the prevalence exhibited a notable trend: it increased steadily until peaking at 1.6 per 1,000 in 2005, then gradually declined, stabilizing at a lower rate of approximately 0.6 per 1,000 in 2006, and approaching zero by 2011. During this period, noticeable geographic disparities in schistosomiasis prevalence were observed; high-risk areas were initially dispersed, followed by contraction. Predictions for the period 2012 to 2015 demonstrated a consistent and uniform decrease.CONCLUSIONS: The proposed CNN-IDE model captured the intricate and evolving dynamics of schistosomiasis prevalence, offering a promising alternative for future risk modeling of the disease. The comprehensive strategy is expected to help diminish schistosomiasis infection, emphasizing the necessity to continue implementing this strategy.<\/jats:p>","DOI":"10.4178\/epih.e2024039","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T14:52:55Z","timestamp":1710514375000},"page":"e2024039","update-policy":"https:\/\/doi.org\/10.4178\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unraveling trends in schistosomiasis: deep learning insights into national control programs in China"],"prefix":"10.4178","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5685-1116","authenticated-orcid":true,"given":"Qing","family":"Su","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2337-7965","authenticated-orcid":true,"given":"Cici Xi 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