Epidemiol Health.
2024;46(1):e2024039. 10.4178/epih.e2024039.
Unraveling trends in schistosomiasis: deep learning insights into national control programs in China
- Affiliations
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- 1Department of Epidemiology and Biostatistics, School of Public Health, Fudan University, Shanghai, China
- 2Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China
- 3Xuhui District Center for Disease Control and Prevention, Shanghai, China
- 4Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, TX, USA
- 5Ingerod, Brastad, Sweden
- 6Anhui Institute of Parasitic Diseases, Wuhu, China
- 7Laboratory for Spatial Analysis and Modeling, School of Public Health, Fudan University, Shanghai, China
- KMID: 2558810
- DOI: http://doi.org/10.4178/epih.e2024039
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 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.
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