Estimation of Ebola’s spillover infection exposure in Sierra Leone based on sociodemographic and economic factors

Zoonotic diseases spread through pathogens-infected animal carriers. In the case of Ebola Virus Disease (EVD), evidence supports that the main carriers are fruit bats and non-human primates. Further, EVD spread is a multi-factorial problem that depends on sociodemographic and economic (SDE) factors....

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Published in:PloS one 2022-09, Vol.17 (9), p.e0271886-e0271886
Main Authors: Mursel, Sena, Alter, Nathaniel, Slavit, Lindsay, Smith, Anna, Bocchini, Paolo, Buceta, Javier
Format: Article
Language:English
Subjects:
Analysis
Biology and Life Sciences
Computer and Information Sciences
COVID-19
Demographics
Diagnosis
Disease transmission
Distribution
Earth Sciences
Ebola virus
Ebola virus infections
Ebolavirus
Economic factors
Economics
Epidemics
Exposure
Health risks
Infections
Infectious diseases
Machine learning
Medicine and Health Sciences
Pandemics
People and places
Polls & surveys
Population
Prediction models
Research and Analysis Methods
Rural areas
Sociodemographics
Socioeconomic factors
Viral diseases
Viruses
Zoonoses
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Alter, Nathaniel
Slavit, Lindsay
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description Zoonotic diseases spread through pathogens-infected animal carriers. In the case of Ebola Virus Disease (EVD), evidence supports that the main carriers are fruit bats and non-human primates. Further, EVD spread is a multi-factorial problem that depends on sociodemographic and economic (SDE) factors. Here we inquire into this phenomenon and aim at determining, quantitatively, the Ebola spillover infection exposure map and try to link it to SDE factors. To that end, we designed and conducted a survey in Sierra Leone and implement a pipeline to analyze data using regression and machine learning techniques. Our methodology is able (1) to identify the features that are best predictors of an individual’s tendency to partake in behaviors that can expose them to Ebola infection, (2) to develop a predictive model about the spillover risk statistics that can be calibrated for different regions and future times, and (3) to compute a spillover exposure map for Sierra Leone. Our results and conclusions are relevant to identify the regions in Sierra Leone at risk of EVD spillover and, consequently, to design and implement policies for an effective deployment of resources (e.g., drug supplies) and other preventative measures (e.g., educational campaigns).
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subjects Analysis
Biology and Life Sciences
Computer and Information Sciences
COVID-19
Demographics
Diagnosis
Disease transmission
Distribution
Earth Sciences
Ebola virus
Ebola virus infections
Ebolavirus
Economic factors
Economics
Epidemics
Exposure
Health risks
Infections
Infectious diseases
Machine learning
Medicine and Health Sciences
Pandemics
People and places
Polls & surveys
Population
Prediction models
Research and Analysis Methods
Rural areas
Sociodemographics
Socioeconomic factors
Viral diseases
Viruses
Zoonoses
title Estimation of Ebola’s spillover infection exposure in Sierra Leone based on sociodemographic and economic factors
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