Cholera risk: A machine learning approach applied to essential climate variables

Campbell, Amy Marie, Racault, Marie-Fanny ORCID: https://orcid.org/0000-0002-7584-2515, Goult, Stephen and Laurenson, Angus (2020) Cholera risk: A machine learning approach applied to essential climate variables. International Journal of Environmental Research and Public Health, 17 (24). ISSN 1660-4601

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Abstract

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.

Item Type: Article
Additional Information: Funding Information: This research was funded by the United Kingdom Research and Innovation (UKRI) Towards a Sustainable Earth (TaSE) program PODCAST project under the Natural Environment Research Council (NERC), the India Department for Biotechnology (DBT) and the Japan Science and Technology Agency (JST) grant numbers [NE/S012567/1 and BT/IN/TaSE/71/AA/2018-19], and the ESA-Future Earth COP26 Demonstrator PODCAST-DEMO grant number [ESA-2020-04].
Uncontrolled Keywords: ai,cholera,climate,coastal environment,essential climate variables,machine learning,random forest,remote sensing,public health, environmental and occupational health,pollution,health, toxicology and mutagenesis,sdg 15 - life on land,sdg 13 - climate action,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2700/2739
Faculty \ School: Faculty of Science > School of Environmental Sciences
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Depositing User: LivePure Connector
Date Deposited: 11 Feb 2022 13:30
Last Modified: 22 Oct 2022 17:33
URI: https://ueaeprints.uea.ac.uk/id/eprint/83424
DOI: 10.3390/ijerph17249378

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