Schistosomiasis Surveillance — China, 2015−2018

Li, Yinlong; Dai, Hui; Zhang, Lijuan; Li, Guangping; Xiao-Wei, Shan; Yuan, Min; Ding, Songjun; Feng, Yong; Mei-Fen, Shen; Xu, Jing; Jin, Yinli; Du, Haijuan; Wen-cheng, LU; Tang, Wen; Jiang, Dianwei; Yuan, Yong; Lyu, Shan; Li, Shi-Zhu; Zhou, Xiao‐Nong

doi:10.46234/ccdcw2020.011

Cited by 7 publications

(11 citation statements)

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“…Schistosomiasis data were derived from the national schistosomiasis survey of 2005–2019 [ 14 , 15 ]. Villages with indigenous cases were selected as distribution points (Fig.…”

Section: Methodsmentioning

confidence: 99%

Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt

Gong

Zhu

et al. 2021

Infect Dis Poverty

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Background Schistosomiasis control is striving forward to transmission interruption and even elimination, evidence-lead control is of vital importance to eliminate the hidden dangers of schistosomiasis. This study attempts to identify high risk areas of schistosomiasis in China by using information value and machine learning. Methods The local case distribution from schistosomiasis surveillance data in China between 2005 and 2019 was assessed based on 19 variables including climate, geography, and social economy. Seven models were built in three categories including information value (IV), three machine learning models [logistic regression (LR), random forest (RF), generalized boosted model (GBM)], and three coupled models (IV + LR, IV + RF, IV + GBM). Accuracy, area under the curve (AUC), and F1-score were used to evaluate the prediction performance of the models. The optimal model was selected to predict the risk distribution for schistosomiasis. Results There is a more prone to schistosomiasis epidemic provided that paddy fields, grasslands, less than 2.5 km from the waterway, annual average temperature of 11.5–19.0 °C, annual average rainfall of 1000–1550 mm. IV + GBM had the highest prediction effect (accuracy = 0.878, AUC = 0.902, F1 = 0.920) compared with the other six models. The results of IV + GBM showed that the risk areas are mainly distributed in the coastal regions of the middle and lower reaches of the Yangtze River, the Poyang Lake region, and the Dongting Lake region. High-risk areas are primarily distributed in eastern Changde, western Yueyang, northeastern Yiyang, middle Changsha of Hunan province; southern Jiujiang, northern Nanchang, northeastern Shangrao, eastern Yichun in Jiangxi province; southern Jingzhou, southern Xiantao, middle Wuhan in Hubei province; southern Anqing, northwestern Guichi, eastern Wuhu in Anhui province; middle Meishan, northern Leshan, and the middle of Liangshan in Sichuan province. Conclusions The risk of schistosomiasis transmission in China still exists, with high-risk areas relatively concentrated in the coastal regions of the middle and lower reaches of the Yangtze River. Coupled models of IV and machine learning provide for effective analysis and prediction, forming a scientific basis for evidence-lead surveillance and control. Graphic Abstract

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“…Schistosomiasis data were derived from the national schistosomiasis survey of 2005–2019 [ 14 , 15 ]. Villages with indigenous cases were selected as distribution points (Fig.…”

Section: Methodsmentioning

confidence: 99%

Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt

Gong

Zhu

et al. 2021

Infect Dis Poverty

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“…The sites covered all schistosomiasis-endemic counties as well as four counties in the Three Gorges Reservoir area. Surveillance included case monitoring, transmission factors, and transmission risk [9]. From 2015 to 2019, the prevalence of S. japonicum infections reduced from 2.0682% to zero in humans, from 9.42% to zero in livestock, and from 0.26% to zero in O. hupensis based on the national schistosomiasis surveillance sites.…”

Section: Discussionmentioning

confidence: 99%

“…Snail surveys were conducted in snail habitats and in suspected snail-infested habits by systematic sampling and environmental sampling at national schistosomiasis surveillance sites. All captured snails were assessed for viability and S. japonicum infection [8,9]. Field schistosomiasis surveillance was conducted by local schistosomiasis control institutions.…”

Section: Data Collectionmentioning

confidence: 99%

“…Permanent residents aged 6 to 65 years underwent serological screening at national schistosomiasis surveillance sites for S. japonicum infection, as well as measurements of antibody titers for S. japonicum. Seropositive individuals were tested for parasites by the Kato-Katz technique and by the miracidium hatching test [8,9]. The miracidium hatching test was used to detect S. japonicum infections in livestock [8,9].…”

Section: Data Collectionmentioning

confidence: 99%

“…Large-scale schistosomiasis surveillance programs have been launched to facilitate the national schistosomiasis elimination program in China [7]. Data captured from the national schistosomiasis surveillance programs will provide insight into endemic schistosomiasis, an understanding of the prevalence of S. japonicum infections in humans and livestock, as well as the endemic foci of Oncomelania hupensis in China [8,9]. The focus of currently available surveillance data is on the description of spatial, temporal, and population distribution of schistosomiasis.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Heterogeneity of Schistosomiasis in China Based on Multi-stage, Continuous Downscaling of Sentinel Monitoring

Gong¹,

Feng²,

Luo³

et al. 2021

Preprint

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Background: There is a continuous decline in the prevalence of schistosomiasis and the number of Schistosoma japonicum infections in humans and livestock in China. However, there are a large number of factors that have not been resolved and which may contribute to future transmission of schistosomiasis. These include a range of sources for S. japonicum infection, difficulty in management of S. japonicum sources of infection, frequent emergence and re-emergence of Oncomelania hupensis snail habitats, and the problematic elimination of snail habitats. These factors challenge progress towards the elimination of schistosomiasis in China.Methods: Based on multi-stage continuous downscaling of sentinel monitoring, county-based schistosomiasis surveillance data were captured from the national schistosomiasis surveillance sites of China from 2005 to 2019. The data included S. japonicum infections in humans, livestock, and O. hupensis. The spatio-temporal trends for schistosomiasis were detected using a Joinpoint regression model, with a standard deviational ellipse (SDE) tool, which determined the central tendency and dispersion in spatial distribution of schistosomiasis. Further, spatio-temporal clusters of S. japonicum infections in humans, livestock, and O. hupensis were evaluated by Poisson model. Results: The prevalence of S. japonicum human infections was reduced from 2.06% to zero based on the national schistosomiasis surveillance sites of China during the period from 2005 to 2019, with a reduction from 9.42% to zero for the prevalence of S. japonicum infections in livestock, and from 0.26% to zero for the prevalence of S. japonicum infections in O. hupensis. The decline in prevalence of S. japonicum infections in humans, livestock, and O. hupensis was statistically significant from 2005 to 2019 (P < 0.01). There was an exception to the decline in S. japonicum infections in livestock during the period from 2008 to 2012. Using an SDE tool, schistosomiasis-affected regions were reduced yearly from 2005 to 2014 in the endemic provinces of Hunan, Hubei, Jiangxi, and Anhui, as well as in the Poyang and Dongting Lake regions. Poisson model revealed 11 clusters of S. japonicum human infections, six clusters of S. japonicum infections in livestock, and nine clusters of S. japonicum infections in O. hupensis. The clusters of human infection were found to be highly consistent with clusters of S. japonicum infections in livestock and O. hupensis. These clusters were in the five provinces of Hunan, Hubei, Jiangxi, Anhui, and Jiangsu, as well as along the middle and lower reaches of the Yangtze River. Humans, livestock, and O. hupensis infections with S. japonicum were mainly concentrated in the north of the Hunan Province, south of the Hubei Province, north of the Jiangxi Province, and southwestern portion of Anhui Province. In the two mountainous provinces of Sichuan and Yunnan; human, livestock, and O. hupensis infections with S. japonicum were mainly concentrated in the northwestern portion of the Yunnan Province, the Daliangshan area in the south of Sichuan Province, and the hilly regions in the middle of Sichuan Province. Conclusions: This study demonstrate a significant spatio-temporal heterogeneity of schistosomiasis in China. A remarkable decline in endemic schistosomiasis was observed between 2005 and 2019. However, there continues to be a long-term risk of schistosomiasis transmission in local areas, with high-risk areas primarily located in the Poyang Lake and Dongting Lake regions, with frequent acute S. japonicum infections. Using a One Health approach, further reinforcement of an integrated schistosomiasis control strategy, with an emphasis on the sources of S. japonicum infection, is required to facilitate the elimination of schistosomiasis in China by 2030.

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Cross-sectoral role and community empowerment in controlling schistosomiasis in Indonesia

Erlan,

Sudrajad,

Suryatma

et al. 2024

IJPHS

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The role of cross-sectors in synergy with community empowerment in controlling schistosomiasis is a very effective model. Until mid-2017, the incidence rate of disease in humans in 28 endemic villages still ranged 0.00-2.15%. This research aims to evaluate schistosomiasis control activities that have been carried out across sectors based on a road map for schistosomiasis eradication and community empowerment in controlling schistosomiasis. The study was mixed method. Data were obtained by in-depth interview with key informants and evaluation of the results of cross-sectoral and community activities. The research was carried out in an endemic area for schistosomiasis on the Bada Plateau, Indonesia, from January to November 2019. In conclusion, the potential of cross-sector collaboration in controlling schistosomiasis has not been fully realized due to budget constraints. Community empowerment is carried out to eliminate snail foci, increase the scope of feces collection and change community behavior for the better in order to avoid infection and always try to clean up snail foci.

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Schistosomiasis Surveillance — China, 2015−2018

Cited by 7 publications

References 1 publication

Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt

Identification of the high-risk area for schistosomiasis transmission in China based on information value and machine learning: a newly data-driven modeling attempt

Spatio-Temporal Heterogeneity of Schistosomiasis in China Based on Multi-stage, Continuous Downscaling of Sentinel Monitoring

Cross-sectoral role and community empowerment in controlling schistosomiasis in Indonesia

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