ArticleInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases2020
Spatiotemporal variability in case fatality ratios for the 2013-2016 Ebola epidemic in West Africa.
Article in International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- The economic value of personal protective equipment for healthcare workers.PLOS global public health · 2023Article
- A stabilized spatiotemporal kriging method for disease mapping and application to male oral cancer and female breast cancer in Taiwan.BMC medical research methodology · 2022Article
- Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease.PLoS neglected tropical diseases · 2022Article
- Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China.International journal of environmental research and public health · 2020Article
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Abstract
backgroundFor the 2013-2016 Ebola epidemic in West Africa, the largest Ebola virus disease (EVD) epidemic to date, we aim to analyse the patient mix in detail to characterise key sources of spatiotemporal heterogeneity in the case fatality ratios (CFR).
methodsWe applied a non-parametric Boosted Regression Trees (BRT) imputation approach for patients with missing survival outcomes and adjusted for model imperfection. Semivariogram analysis and kriging were used to investigate spatiotemporal heterogeneities.
resultsCFR estimates varied significantly between districts and over time over the course of the epidemic. BRT modelling accounted for most of the spatiotemporal variation and interactions in CFR, but moderate spatial autocorrelation remained for distances up to approximately 90 km. Combining district-level CFR estimates and kriged district-level residuals provided the best linear unbiased predicted map of CFR accounting for the both explained and unexplained spatial variation. Temporal autocorrelation was not observed in the district-level residuals from the BRT estimates.
conclusionsThis study provides new insight into the epidemiology of the 2013-2016 West African Ebola epidemic with a view of informing future public health contingency planning, resource allocation and impact assessment. The analytical framework developed in this analysis, coupled with key domain knowledge, could be deployed in real time to support the response to ongoing and future outbreaks.
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