Abstract
BackgroundSchistosomiasis 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.MethodsThe 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.ResultsThere 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.ConclusionsThe 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
Highlights
Schistosomiasis control is striving forward to transmission interruption and even elimination, evidencelead control is of vital importance to eliminate the hidden dangers of schistosomiasis
Correlation analysis among schistosomiasis and environmental factors Based on the principle of chi-square binning, the upper limit of binning is set to 8, and the Information value (IV) of different levels of influencing factors is calculated according to the binning situation (Table 3)
The risk of schistosomiasis transmission is higher when the distance from waterways is less than 2.5 km, the altitude is less than 100 m, the land use is paddy field, grassland, and water area, and the landform type is plain
Summary
Schistosomiasis control is striving forward to transmission interruption and even elimination, evidencelead 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. Over the past 70 years of active control, China’s schistosomiasis control program has achieved remarkable success [2]. By the end of 2020, 337 (74.9%) of the 450 schistosomiasis endemic counties in China had achieved the elimination standard, 97 (21.6%) have achieved the transmission blocking standard and 16 (3.6%) have achieved transmission control [3]. China’s 13th Five-Year Plan for national schistosomiasis control identifies risk monitoring and early warning to be essential to reduce potential transmission risk. Prediction model design is an effective means by which to achieve accurate monitoring and evidence-lead control of schistosomiasis [4]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have