Unraveling trends in schistosomiasis: deep learning insights into national control programs in China
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (35)
- King CH, "Reassessment of the cost of chronic helmintic infection : a meta-analysis of disability-related outcomes in endemic schistosomiasis", Lancet, 365, pp.1561~1569 (2005)[DOI]
- "Schistosomiasis (Bilharzia)"[URL]
- GBD 2017 Disease and Injury Incidence and Prevalence Collaborators, "Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017", Lancet, 392, pp.1789~1858 (2018)[DOI]
- Gordon CA, "Asian schistosomiasis : current status and prospects for control leading to elimination", Trop Med Infect Dis, 4, pp.40~ (2019)[DOI]
- Mao SP, "Schistosomiasis control in the people’s Republic of China", Am J Trop Med Hyg, 31, pp.92~99 (1982)[DOI]
- Zhang LJ, "Endemic status of schistosomiasis in People’s Republic of China in 2020", Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi, 33, pp.225~233 (2021)[DOI]
- Heuvelink GB, "Space–time geostatistics for geography : a case study of radiation monitoring across parts of Germany", Geogr Anal, 42, pp.161~179 (2010)[DOI]
- Xianyi C, "Schistosomiasis control in China : the impact of a 10-year World Bank Loan Project(1992-2001)", Bull World Health Organ, 83, pp.43~48 (2005)
- Collins C, "Schistosomiasis control and the health system in P. R. China", Infect Dis Poverty, 1, pp.8~ (2012)[DOI]
- Liu R, "The new national integrated strategy emphasizing infection sources control for schistosomiasis control in China has made remarkable achievements", Parasitol Res, 112, pp.1483~1491 (2013)[DOI]
- Hu Y, "Spatio-temporal patterns of schistosomiasis japonica in lake and marshland areas in China : the effect of snail habitats", Am J Trop Med Hyg, 91, pp.547~554 (2014)[DOI]
- Hu Y, "Spatio-temporal transmission and environmental determinants of Schistosomiasis Japonica in Anhui Province, China", PLoS Negl Trop Dis, 9, pp.e0003470~ (2015)[DOI]
- Hu Y, "Spatial pattern of schistosomiasis in Xingzi, Jiangxi Province, China : the effects of environmental factors", Parasit Vectors, 6, pp.214~ (2013)[DOI]
- Zhang ZJ, "Location of active transmission sites of Schistosoma japonicum in lake and marshland regions in China", Parasitology, 136, pp.737~746 (2009)[DOI]
- Hu Y, "Dynamic evolution of schistosomiasis distribution under different control strategies : results from surveillance covering 1991-2014 in Guichi, China", PLoS Negl Trop Dis, 15, pp.e0008976~ (2021)[DOI]
- Miyashita K, "Idiopathic hyperaldosteronism(IHA)", Nihon Rinsho, (Suppl 1), pp.624~627 (2006)
- Yu JM, "Comparison of the Kato-Katz technique, hatching test and indirect hemagglutination assay(IHA)for the diagnosis of Schistosoma japonicum infection in China", Parasitol Int, 56, pp.45~49 (2007)[DOI]
- Su Q, "A comparison of modelling the spatio-temporal pattern of disease : a case study of schistosomiasis japonica in Anhui Province, China", Trans R Soc Trop Med Hyg, 116, pp.555~563 (2022)[DOI]
- Box GE, "An analysis of transformations", J R Stat Soc Series B Stat Methodol, 26, pp.211~243 (1964)[DOI]
- Diggle PJ, "Model-based geostatistics", J R Stat Soc Ser C Appl Stat, 47, pp.299~350 (1998)[DOI]
- Hu Y, "Comparison of data-fitting models for schistosomiasis : a case study in Xingzi, China", Geospat Health, 8, pp.125~132 (2013)[DOI]
- Zammit-Mangion A, "Deep integro-difference equation models for spatio-temporal forecasting", Spat Stat, 37, pp.100408~ (2020)[DOI]
- Wikle CK, "Spatio-temporal statistics with R", pp.210~266 (2019), CRC Press
- Zhu HR, "Ecological model to predict potential habitats of Oncomelania hupensis, the intermediate host of Schistosoma japonicum in the mountainous regions, China", PLoS Negl Trop Dis, 9, pp.e0004028~ (2015)[DOI]
- Ajakaye OG, "Modeling the risk of transmission of schistosomiasis in Akure North Local Government Area of Ondo State, Nigeria using satellite derived environmental data", PLoS Negl Trop Dis, 11, pp.e0005733~ (2017)[DOI]
- Gao F, "Implications from assessing environmental effects on spatio-temporal pattern of schistosomiasis in the Yangtze Basin, China", Geospat Health, 13, pp.730~ (2018)[DOI]
- Pflüger W, "Experimental epidemiology of schistosomiasis. I. The prepatent period and cercarial production of Schistosoma mansoni in Biomphalaria snails at various constant temperatures", Z Parasitenkd, 63, pp.159~169 (1980)[DOI]
- Hu Y, "Dynamics of spatial clustering of schistosomiasis in the Yangtze River Valley at the end of and following the World Bank Loan Project", Parasitol Int, 63, pp.500~505 (2014)[DOI]
- Zhou YB, "Factors impacting on progress towards elimination of transmission of schistosomiasis japonica in China", Parasit Vectors, 5, pp.275~ (2012)[DOI]
- Zhou XN, "Epidemiology of schistosomiasis in the People’s Republic of China, 2004", Emerg Infect Dis, 13, pp.1470~1476 (2007)[DOI]
- Wang L, "Schistosomiasis control : experiences and lessons from China", Lancet, 372, pp.1793~1795 (2008)[DOI]
- Wang LD, "China’s new strategy to block Schistosoma japonicum transmission : experiences and impact beyond schistosomiasis", Trop Med Int Health, 14, pp.1475~1483 (2009)[DOI]
- Wang LD, "A strategy to control transmission of Schistosoma japonicum in China", N Engl J Med, 360, pp.121~128 (2009)[DOI]
- Ben-Jiao H, "Measures and achievements of schistosomiasis control in the Yangtze River Basin", Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi, 30, pp.592~595 (2018)[DOI]
- Guo S, "Sentinel surveillance of schistosomiasis-China, 2021", China CDC Wkly, 5, pp.278~282 (2023)[DOI]