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

Cholera risk: a machine learning approach applied to essential climate variables
Cholera risk: a machine learning approach applied to essential climate variables
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.
1660-4601
Campbell, Amy Marie
b623d9a6-2917-4715-9333-c8b459002100
Racault, Marie-fanny
d944a02f-ae3d-46f4-91fd-c297d445ac56
Goult, Stephen
bece2b33-4918-42c6-a20c-393b5e045d25
Laurenson, Angus
cf705ccd-a1c6-40ff-b261-29bf3fbc46b9
Campbell, Amy Marie
b623d9a6-2917-4715-9333-c8b459002100
Racault, Marie-fanny
d944a02f-ae3d-46f4-91fd-c297d445ac56
Goult, Stephen
bece2b33-4918-42c6-a20c-393b5e045d25
Laurenson, Angus
cf705ccd-a1c6-40ff-b261-29bf3fbc46b9

Campbell, Amy Marie, Racault, Marie-fanny, 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), [9378]. (doi:10.3390/ijerph17249378).

Record type: Article

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.

Text
ijerph-17-09378-v2 - Version of Record
Available under License Creative Commons Attribution.
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More information

Accepted/In Press date: 9 December 2020
Published date: 15 December 2020

Identifiers

Local EPrints ID: 447148
URI: http://eprints.soton.ac.uk/id/eprint/447148
ISSN: 1660-4601
PURE UUID: 8a6c5949-8dc4-4954-a57f-cd7d93cc9e58
ORCID for Amy Marie Campbell: ORCID iD orcid.org/0000-0003-4111-8286

Catalogue record

Date deposited: 04 Mar 2021 17:38
Last modified: 26 Nov 2021 03:22

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Contributors

Author: Marie-fanny Racault
Author: Stephen Goult
Author: Angus Laurenson

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