My research aims to understand the sub-national level trajectories of Ebola Virus Disease (EVD) in West Africa, and the influence of health worker distribution during the epidemic. Furthermore, I will investigate factors that could be associated with the Pre/Post-Ebola clinical outcomes of survivors and if these also show sub-national variations. Hopefully, evidence generated from my project would be used to inform future outbreak policies and responses. Presently, I am using a machine learning (Boosted regression trees) model to impute the outcome (i.e. survival or death) of Ebola cases in the WHO line-list data collected during the West African Ebola outbreak in 2013-2015. The observed and imputed outcomes will improve the current estimates of the case fatality ratio. My Ph.D. is supervised by Professor Christl Donnelly, Dr. Pierre Nouvellet and Dr. Ilaria Dorigatti. I am funded by the Commonwealth Scholarship Commission, United Kingdom.
et al., 2019, Case fatality ratio estimates for the 2013 – 2016 West African Ebola epidemic: application of Boosted Regression Trees for imputation, Clinical Infectious Diseases, ISSN:1058-4838
et al., 2018, Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April–May, 2018: an epidemiological study, The Lancet, Vol:392, ISSN:0140-6736, Pages:213-221
et al., 2019, Case fatality ratio estimates for the 2013-2016 West African Ebola epidemic: application of Boosted Regression Trees for imputation, ELSEVIER SCI LTD, Pages:128-128, ISSN:1201-9712