[Analysis of schistosomiasis prevalence in endemic areas using retrospective space-time permutation scan statistics].

Objective: To analyze the characteristics of schistosomasis prevalence by using the spatial epidemiological method, and test the application of retrospective space-time permutation scan statistics in determining mountainous and lake-type endemic areas of schistosomiasis. Methods: The data of schistosomasis in humans, cattle and snails in Jiangxi Province during 2009-2014 and in Yunnan Province during 2004-2013 were collected and analyzed. The temporal and spatial distribution of schistosomiasis endemic areas in the two provinces was analyzed with retrospective space-time permutation scan statistics. Results: The prevalence of schistosomiasis in residents and Oncomelania snails showed a trend of decline in Jiangxi, from 0.21% and 0.03% in 2009 to 0.01% and zero in 2014. A similar trend was found in cattle, from 1.25% in 2012 to 0.12% in 2014. The average annual percentage change (APC) in residents was-47.36%(P < 0.05). The space-time permutation clustering analysis revealed a temporal and spatial clustering of schistosomiasis prevalence from 2009 to 2014 in residents, cattle, and snails, with 3,2 and 1 clustering areas, respectively, all distributed in Poyang Lake Region. A similar declining trend of schistosomiasis prevalence was found in residents, snails and cattle in Yunnan during 2004-2013, from 2.49%,0.70% and 3.76% in 2004 to no infection in residents and snails and 0.02% in cattle in 2013. The APC in residents was-49.17%(P < 0.05). There was a temporal and spatial clustering of schistosomiasis prevalence during 2004-2013 in residents, cattle, and snails, with 2,2 and 6 clustering areas, respectively. Conclusion: A declining trend of schistosomiasis prevalence is shown in lake-type endemic areas in Jiangxi during 2009-2014 and in mountainous endemic areas in Yunnan during 2004-2013. The retrospective space-time permutation scan statistics reveal a clustering of schistosomiasis in humans, cattle, and snails, suggesting its applicability in analyzing the temporal and spatial distribution of schistosomiasis.

Recognizing and monitoring infectious sources of schistosomiasis by developing deep learning models with high-resolution remote sensing images.

BACKGROUND: China is progressing towards the goal of schistosomiasis elimination, but there are still some problems, such as difficult management of infection source and snail control. This study aimed to develop deep learning models with high-resolution remote sensing images for recognizing and monitoring livestock bovine, which is an intermediate source of Schistosoma japonicum infection, and to evaluate the effectiveness of the models for real-world application. METHODS: The dataset of livestock bovine's spatial distribution was collected from the Chinese National Platform for Common Geospatial Information Services. The high-resolution remote sensing images were further divided into training data, test data, and validation data for model development. Two recognition models based on deep learning methods (ENVINet5 and Mask R-CNN) were developed with reference to the training datasets. The performance of the developed models was evaluated by the performance metrics of precision, recall, and F1-score. RESULTS: A total of 50 typical image areas were selected, 1125 bovine objectives were labeled by the ENVINet5 model and 1277 bovine objectives were labeled by the Mask R-CNN model. For the ENVINet5 model, a total of 1598 records of bovine distribution were recognized. The model precision and recall were 81.9% and 80.2%, respectively. The F1 score was 0.81. For the Mask R-CNN mode, 1679 records of bovine objectives were identified. The model precision and recall were 87.3% and 85.2%, respectively. The F1 score was 0.87. When applying the developed models to real-world schistosomiasis-endemic regions, there were 63 bovine objectives in the original image, 53 records were extracted using the ENVINet5 model, and 57 records were extracted using the Mask R-CNN model. The successful recognition ratios were 84.1% and 90.5% for the respectively developed models. CONCLUSION: The ENVINet5 model is very feasible when the bovine distribution is low in structure with few samples. The Mask R-CNN model has a good framework design and runs highly efficiently. The livestock recognition models developed using deep learning methods with high-resolution remote sensing images accurately recognize the spatial distribution of livestock, which could enable precise control of schistosomiasis.

Potential impact of flooding on schistosomiasis in Poyang Lake regions based on multi-source remote sensing images.

BACKGROUND: Flooding is considered to be one of the most important factors contributing to the rebound of Oncomelania hupensis, a small tropical freshwater snail and the only intermediate host of Schistosoma japonicum, in endemic foci. The aim of this study was to assess the risk of intestinal schistosomiasis transmission impacted by flooding in the region around Poyang Lake using multi-source remote sensing images. METHODS: Normalized Difference Vegetation Index (NDVI) data collected by the Landsat 8 satellite were used as an ecological and geographical suitability indicator of O. hupensis habitats in the Poyang Lake region. The expansion of the water body due to flooding was estimated using dual-polarized threshold calculations based on dual-polarized synthetic aperture radar (SAR). The image data were captured from the Sentinel-1B satellite in May 2020 before the flood and in July 2020 during the flood. A spatial database of the distribution of snail habitats was created using the 2016 snail survey in Jiangxi Province. The potential spread of O. hupensis snails after the flood was predicted by an overlay analysis of the NDVI maps in the flood-affected areas around Poyang Lake. The risk of schistosomiasis transmission was classified based on O. hupensis snail density data and the related NDVI. RESULTS: The surface area of Poyang Lake was approximately 2207 km2 in May 2020 before the flood and 4403 km2 in July 2020 during the period of peak flooding; this was estimated to be a 99.5% expansion of the water body due to flooding. After the flood, potential snail habitats were predicted to be concentrated in areas neighboring existing habitats in the marshlands of Poyang Lake. The areas with high risk of schistosomiasis transmission were predicted to be mainly distributed in Yongxiu, Xinjian, Yugan and Poyang (District) along the shores of Poyang Lake. By comparing the predictive results and actual snail distribution, we estimated the predictive accuracy of the model to be 87%, which meant the 87% of actual snail distribution was correctly identified as snail habitats in the model predictions. CONCLUSIONS: Data on water body expansion due to flooding and environmental factors pertaining to snail breeding may be rapidly extracted from Landsat 8 and Sentinel-1B remote sensing images. Applying multi-source remote sensing data for the timely and effective assessment of potential schistosomiasis transmission risk caused by snail spread during flooding is feasible and will be of great significance for more precision control of schistosomiasis.

Construction and application of surveillance and response systems for parasitic diseases in China, led by NIPD-CTDR.

Parasitic diseases have been widely epidemic in China with a long history. Great endeavours made in past 70 years led to significant decrease in morbidity and mortablity caused by several major parasitic diseases, while challenges existed to eliminate parasitic diseases. Surveillance-response system has play a crucial role in identifying public health problems, ascertaining the distribution and epidemic dynamics, discovering outbreaks and epidemic anomalies, evaluating the effects of on-site intervention activities and identifying risk factors. In this article, we reviewed the progress of the surveillance system for parasitic diseases, analysed the role of NIPD in the construction and application of surveillance-response system of parasitic diseases through elaborating the surveillance activities and typical surveillance-response events led by NIPD. Suggestion and comments for improve the surveillance-response system were put forward for further control or elimination of parasitic diseases.

High-resolution remote sensing-based spatial modeling for the prediction of potential risk areas of schistosomiasis in the Dongting Lake area, China.

The geographical distribution of snail (i.e., the intermediate host of schistosomiasis) is consistent with that of endemic areas. The suitable snail habitus requires necessary environmental conditions for snail population. The high-resolution remote sensing provides an important tool for the spatio-temporal analysis of disease monitoring and prediction. This study conducted a typical schistosomiasis epidemic area in the marshland and lake regions along the Yangtze River, Yueyang City, Hunan Province of China. And three types of environmental factors, i.e., NDVI, soil moisture, and shortest distance to water body, associated with the geographical distribution of snail population, were extracted from the high-resolution remoting sensing data. The predicted distribution of snail habitus from the high-resolution environmental factors were compared with the data of annual program of snail survey. The results have shown that the application of high-resolution remote sensing can improve the accuracy of the modeled and predicted the potential risk areas of schistosomiasis, and may become an important tool for the ongoing national schistosomiasis control program.

High-resolution remote sensing-based spatial modeling for the prediction of potential risk areas of schistosomiasis in the Dongting Lake area, China.

The geographical distribution of snail (i.e., the intermediate host of schistosomiasis) is consistent with that of endemic areas. The suitable snail habitus requires necessary environmental conditions for snail population. The high-resolution remote sensing provides an important tool for the spatio-temporal analysis of disease monitoring and prediction. This study conducted a typical schistosomiasis epidemic area in the marshland and lake regions along the Yangtze River, Yueyang City, Hunan Province of China. And three types of environmental factors, i.e., NDVI, soil moisture, and shortest distance to water body, associated with the geographical distribution of snail population, were extracted from the high-resolution remoting sensing data. The predicted distribution of snail habitus from the high-resolution environmental factors were compared with the data of annual program of snail survey. The results have shown that the application of high-resolution remote sensing can improve the accuracy of the modeled and predicted the potential risk areas of schistosomiasis, and may become an important tool for the ongoing national schistosomiasis control program.

[Analysis of endemic changes of schistosomiasis in China from 2002 to 2010].

OBJECTIVE: To provide the reference for schistosomiasis control through analyzing the changes of endemic status of schistosomiasis in China in recent years. METHODS: The annual data of schistosomiasis prevention and control from 2002 to 2012 were collected, and the descriptive analysis was conducted to describe the changes of schistosomiasis endemic status. RESULTS: Sichuan, Yunan and Jiangxi provinces reached the criteria of transmission controlled from 2002 to 2010 while Hubei, Hunan, Jiangxi and Anhui provinces reached the criteria of infection controlled in 2008. The number of counties where the transmission of schistosomiasis was controlled decreased from 110 in 2002 to 80 in 2010. The numbers of estimated schistosomiasis cases and reported acute cases fell from 810.4 thousands and 913 in 2002 to 325.8 thousands and 43 in 2010, respectively, and they were reduced by 59.79% and 95.40% respectively. The number of infected bovine went down from 23,199 in 2002 to 7,173 in 2010, with a fall of 69.03%. However, the Oncomelania hupensis snails breeding areas kept in 3.7 to 3.8 billion m2 and among which, 125 million m2 areas were newly discovered in non-endemic areas during the nine years. In 2010, 90% of schistosomiasis cases, acute cases, infected bovine, and snail breeding areas were concentrated in 5 provinces, especially in the Dongting Lake and Poyang Lake areas and the regions along the Yangtze River. CONCLUSIONS: The epidemic of schistosomiasis decreased significantly from 2002 to 2010. The lake regions are the key and tough areas for schistosomiasis prevention and control. To consolidate the achievements, the surveillance and case management need to be strengthened as the extension of snail breeding areas and acute cases reported from other places becomes common.