The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana

Wrable, Madeline; Kulinkina, Alexandra V.; Liss, Alexander; Koch, Magaly; Cruz, Melissa; Biritwum, Nana‐Kwadwo; Ofosu, Anthony; Gute, David M.; Kosinski, Karen C.; Naumova, Elena N.

doi:10.1007/s10661-019-7411-6

Cited by 14 publications

(6 citation statements)

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“…Environmental data can be obtained using remote sensing techniques (RS) and the geographic information system (GIS) [ 319 , 329 , 331 , 332 ]. A study in Ghana [ 333 ] utilised environmental variables including land surface temperatures (LST), normalized difference vegetation index (NDVI), and accumulated precipitation (AP) to analyse disease patterns with wide geographic coverage and varying levels of spatial and temporal aggregation. This assessment reported reduced rates of schistosomiasis, seasonal patterns across time zones and the associated schistosome infection rates, features that are useful in determining disease patterns [ 333 ].…”

Section: A Brief History Of Schistosomiasis In Africamentioning

confidence: 99%

Schistosomiasis with a Focus on Africa

et al. 2021

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Schistosomiasis is a common neglected tropical disease of impoverished people and livestock in many developing countries in tropical Africa, the Middle East, Asia, and Latin America. Substantial progress has been made in controlling schistosomiasis in some African countries, but the disease still prevails in most parts of sub-Saharan Africa with an estimated 800 million people at risk of infection. Current control strategies rely primarily on treatment with praziquantel, as no vaccine is available; however, treatment alone does not prevent reinfection. There has been emphasis on the use of integrated approaches in the control and elimination of the disease in recent years with the development of health infrastructure and health education. However, there is a need to evaluate the present status of African schistosomiasis, primarily caused by Schistosoma mansoni and S. haematobium, and the factors affecting the disease as the basis for developing more effective control and elimination strategies in the future. This review provides an historical perspective of schistosomiasis in Africa and discusses the current status of control efforts in those countries where the disease is endemic.

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Section: A Brief History Of Schistosomiasis In Africamentioning

confidence: 99%

Schistosomiasis with a Focus on Africa

et al. 2021

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“…Surveillance in endemic low- and middle-income countries is inhibited by limited health infrastructure and cases evading clinical detection due to lower parasite burden and lessened symptoms that result from preventive chemotherapy with the anthelmintic drug praziquantel. Passive health facility-based surveillance and reporting systems are known to severely underestimate the number of infections [ 7 , 8 ]. For example, a total of ~25,000 schistosomiasis cases were reported into the Ghanaian District Health Information Management System (DHIMS) in 2010 (data received from GHS, 2016).…”

Section: Introductionmentioning

confidence: 99%

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles

et al. 2018

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BackgroundSchistosomiasis is a water-related neglected tropical disease. In many endemic low- and middle-income countries, insufficient surveillance and reporting lead to poor characterization of the demographic and geographic distribution of schistosomiasis cases. Hence, modeling is relied upon to predict areas of high transmission and to inform control strategies. We hypothesized that utilizing remotely sensed (RS) environmental data in combination with water, sanitation, and hygiene (WASH) variables could improve on the current predictive modeling approaches.MethodologySchistosoma haematobium prevalence data, collected from 73 rural Ghanaian schools, were used in a random forest model to investigate the predictive capacity of 15 environmental variables derived from RS data (Landsat 8, Sentinel-2, and Global Digital Elevation Model) with fine spatial resolution (10–30 m). Five methods of variable extraction were tested to determine the spatial linkage between school-based prevalence and the environmental conditions of potential transmission sites, including applying the models to known human water contact locations. Lastly, measures of local water access and groundwater quality were incorporated into RS-based models to assess the relative importance of environmental and WASH variables.Principal findingsPredictive models based on environmental characterization of specific locations where people contact surface water bodies offered some improvement as compared to the traditional approach based on environmental characterization of locations where prevalence is measured. A water index (MNDWI) and topographic variables (elevation and slope) were important environmental risk factors, while overall, groundwater iron concentration predominated in the combined model that included WASH variables.Conclusions/SignificanceThe study helps to understand localized drivers of schistosomiasis transmission. Specifically, unsatisfactory water quality in boreholes perpetuates reliance on surface water bodies, indirectly increasing schistosomiasis risk and resulting in rapid reinfection (up to 40% prevalence six months following preventive chemotherapy). Considering WASH-related risk factors in schistosomiasis prediction can help shift the focus of control strategies from treating symptoms to reducing exposure.

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“…Firstly, a dataset was obtained and then standardised to remove any biases among the various features. Then, several feature selection approaches are used to extract significant features from high-dimensional data [ 28 , 29 ]. In addition, several resampling approaches have been employed to address the data imbalance problem.…”

Section: Methodsmentioning

confidence: 99%

A Proposed Framework for Early Prediction of Schistosomiasis

et al. 2022

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Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 individuals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data resampling algorithms, including Random undersampling, Random oversampling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis.

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The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana

Abstract: The use of remotely sensed environmental parameters for spatial and temporal schistosomiasis prediction across climate zones in Ghana.." Environ

Cited by 14 publications

References 29 publications

Schistosomiasis with a Focus on Africa

Schistosomiasis with a Focus on Africa

Improving spatial prediction of Schistosoma haematobium prevalence in southern Ghana through new remote sensors and local water access profiles

A Proposed Framework for Early Prediction of Schistosomiasis

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