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Open AccessArticle

Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables

1
European Space Agency, Climate Office, ECSAT, Harwell OX11 0FD, UK
2
Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth PL1 3DH, UK
3
National Centre For Earth Observation, PML, Plymouth PL1 3DH, UK
*
Author to whom correspondence should be addressed.
Current address: National Oceanography Centre, School of Ocean and Earth Sciences, University of Southampton, European Way, Southampton SO14 3ZH, UK.
Int. J. Environ. Res. Public Health 2020, 17(24), 9378; https://doi.org/10.3390/ijerph17249378
Received: 7 October 2020 / Revised: 24 November 2020 / Accepted: 9 December 2020 / Published: 15 December 2020
(This article belongs to the Section Environmental Science and Engineering)
Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems. View Full-Text
Keywords: cholera; coastal environment; climate; remote sensing; essential climate variables; machine learning; AI; random forest cholera; coastal environment; climate; remote sensing; essential climate variables; machine learning; AI; random forest
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MDPI and ACS Style

Campbell, A.M.; Racault, M.-F.; Goult, S.; Laurenson, A. Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables. Int. J. Environ. Res. Public Health 2020, 17, 9378. https://doi.org/10.3390/ijerph17249378

AMA Style

Campbell AM, Racault M-F, Goult S, Laurenson A. Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables. International Journal of Environmental Research and Public Health. 2020; 17(24):9378. https://doi.org/10.3390/ijerph17249378

Chicago/Turabian Style

Campbell, Amy M.; Racault, Marie-Fanny; Goult, Stephen; Laurenson, Angus. 2020. "Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables" Int. J. Environ. Res. Public Health 17, no. 24: 9378. https://doi.org/10.3390/ijerph17249378

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