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- Original article Unraveling trends in schistosomiasis: deep learning insights into national control programs in China
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Qing Su1,2
, Cici Xi Chen Bauer4
, Robert Bergquist5
, Zhiguo Cao6
, Fenghua Gao7
, Yi Hu1,3
, Zhijie Zhang3,8
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Epidemiol Health 2024;e2024039
DOI: https://doi.org/10.4178/epih.e2024039 [Accepted]
Published online: March 13, 2024
2Xuhui District Center for Disease Control and Prevention, Shanghai 200032, China, Shanghai, China
3Key Laboratory of Public Health Safety, Ministry of Education, Shanghai 200032, China, sahnghai, China
4Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA, Houston, United States
5Ingerod, Brastad, Sweden, Brastad, Australia
6Anhui Institute of Parasitic Diseases, Wuhu, People's Republic of China 230061, China, Anhui, China
7Anhui Institute of Parasitic Diseases, Wuhu, People's Republic of China 230061, China, Anhui, China
8Fudan University, sahnghai, China
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Corresponding author:
Yi Hu,
Email: huyi@fudan.edu.cn
Zhijie Zhang,
Email: huyi@fudan.edu.cn
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Abstract
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 1000 in 2005, then gradually declined, stabilizing at a lower rate of approximately 0.6 per 1000 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.
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