Environmental and biotic factors affecting freshwater snail intermediate hosts in the Ethiopian Rift Valley region

Olkeba, Beekam Kebede; Boets, Pieter; Mereta, Seid Tiku; Yeshigeta, Mesfin; Akessa, Geremew Muleta; Ambelu, Argaw; Goethals, Peter

doi:10.1186/s13071-020-04163-6

Cited by 30 publications

(17 citation statements)

References 55 publications

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“…The relationships among the spatial change of schistosomiasis risk and environmental factors can be explained by a biological knowledge of S. japonicum and snails [ 34 ]. Suitable climatic conditions, small slopes, and proximity to rivers are conducive to the growth and reproduction of S. japonicum and snails [ 35 ], which in turn leads to the prevalence of schistosomiasis. This study demonstrates that temperature, rainfall, altitude, and the risk of schistosomiasis transmission are closely related.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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

View full text Add to dashboard Cite

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“…The relationships among the spatial change of schistosomiasis risk and environmental factors can be explained by a biological knowledge of S. japonicum and snails [32]. Suitable climatic conditions, small slopes, and proximity to rivers are conducive to the growth and reproduction of S. japonicum and snails [33], which in turn leads to the prevalence of schistosomiasis. This study demonstrates that temperature, rainfall, altitude, and the risk of schistosomiasis transmission are closely related.…”

Section: Discussionmentioning

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 Try

Gong¹,

Zhu²,

Li³

et al. 2021

Preprint

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Objective Information value (IV) and machine learning models were used to analyze and predict the high-risk distribution of schistosomiasis, in order to provide scientific evidence for disease surveillance and control in China. 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 IV, three machine learning models (logistic regression, LR; random forest, RF; generalized boosted model, GBM), and three coupled models (coupled model of information value and logistic regression, IV + LR; coupled model of information value and random forest, IV + RF; coupled model of information value and generalized boosted model, IV + GBM). Accuracy, AUC (area under the curve), and F1-score were used to evaluate the prediction performance of the models. The best model was selected to predict the risk distribution for schistosomiasis. Results IV + GBM had the highest prediction effect (accuracy = 0.878, AUC = 0.902, F1 = 0.920). The results of IV + GBM showed that the risk area for transmission comprised 4.66% of China, 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. Risk areas can be divided into low-risk (2.47%), medium-risk (1.35%), and high-risk (0.84%). High-risk areas are primarily distributed in eastern Changde, western Yueyang, northeastern Yiyang, middle Changsha of the 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 within regions. Coupled models of IV and machine learning provide for effective analysis and prediction, forming a scientific basis for surveillance and control within key areas.

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“…Schistosomiasis, when compared to other snailborne disease is a major public health concern with about 800 million people being at risk [8], and majority of these people are mainly in sub-Saharan Africa [8,9]. Although drugs and molluscicide have been used to contain the spread of this disease in the tropics [10], the prevalence still persist [11].…”

Section: Introductionmentioning

confidence: 99%

“…The transmission of snail-borne diseases is dependent on the distribution of specific snail intermediate hosts [7,8]. Hence, one way to counter the spread of these diseases is to investigate the presence of snails in humandominated landscapes and educate the public on the mode of transmission of snail-borne diseases.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, one way to counter the spread of these diseases is to investigate the presence of snails in humandominated landscapes and educate the public on the mode of transmission of snail-borne diseases. This approach has been successful in some part of Africa, e.g., in Sudan [14], Ethiopia [8], and South Africa [15]. However, in Nigeria a dearth of studies means very little evidence is available to the public to suggest that the presence of the snails may increase the spread of any of the diseases.…”

Section: Introductionmentioning

confidence: 99%

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Abundance and Diversity of Snails in Relation to Physicochemical Parameters along Kwadom Stream, Gombe State, Nigeria

Tela

Abubakar

2021

AJFAR

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Aims: The aim of the study is to investigate the abundance and diversity of snails along Kwadom stream, Gombe state, Nigeria. Study Design: Snails were collected using a benthic scoop net with mesh size of 0.2 mm and hand picking from three sampling stations (home, farmlands, and fishponds sites) along Kwadom stream; between 6:30 am to 11:00 am weekly. In addition, physicochemical parameters (temperature, dissolved oxygen, conductivity, alkalinity, water depth and pH) were measured fortnightly to determine their effect on the diversity of snail species. Place and Duration of Study: The study was conducted along Kwadom stream in Yamaltu Deba Local Government Area of Gombe State, Nigeria from March to July 2021. Methodology: Snails were collected from each of the three stations and identified to species level using hand lens and taxonomy keys. Water physicochemical parameters: temperature, dissolved oxygen, conductivity, alkalinity, water depth and pH were measured using standard method. General linear models (GLM) were used to compare the abundance and diversity of the snails across the sampling station, as well as the effect of physicochemical parameters on the diversity of snails. Results: 160 individual snails were recorded from 11 species, including Biomphalaria pfeifferi– the intermediate host of Schistosoma mansoni. The result showed there was a significant difference in the abundance of snails (p<0.01) across the three study sites – home site 68 (42.5%), farmlands site 56 (35%) and fishpond site 36 (22.5%). The home site had a higher diversity of snail species (p<0.01) relative to the farmlands and fishponds sites. Temperature, conductivity, and alkalinity had a significant effect on the diversity of snails. Conclusion: Overall, Kwadom stream harbors many individual snail species, suggesting the need for educating the public on the mode of transmission of diseases that are caused by these snails.

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Environmental and biotic factors affecting freshwater snail intermediate hosts in the Ethiopian Rift Valley region

Cited by 30 publications

References 55 publications

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

Identification of the High-Risk Area for Schistosomiasis Transmission in China Based on Information Value and Machine Learning—A Newly Data-Driven Modeling Try

Abundance and Diversity of Snails in Relation to Physicochemical Parameters along Kwadom Stream, Gombe State, Nigeria

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