Imperial College London


Faculty of MedicineSchool of Public Health

Research Associate







Norfolk PlaceSt Mary's Campus





Publication Type

4 results found

Forna A, Nouvellet P, Dorigatti I, Donnelly Cet al., 2020, Case fatality ratio estimates for the 2013 – 2016 West African Ebola epidemic: application of Boosted Regression Trees for imputation, Clinical Infectious Diseases, Vol: 70, Pages: 2476-2483, ISSN: 1058-4838

BackgroundThe 2013-2016 West African Ebola epidemic has been the largest to date with more than 11,000 deaths in the affected countries. The data collected have provided more insight than ever before into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naïve CFR remain limited because 44% of survival outcomes were unreported.MethodsUsing a Boosted Regression Tree (BRT) model, we imputed survival outcomes (i.e. survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation and adjusted with imputation. The method allowed us to further identify and explore relevant clinical and demographic predictors of the CFR.ResultsThe out-of-sample performances of our model were good: sensitivity=69.7% (95% CI 52.5%-75.6%), specificity=69.8% (95% CI 54.1%-75.6%), percentage correctly classified=69.9% (95% CI 53.7%-75.5%) and area under the ROC curve= 76.0% (95% CI 56.8%-82.1%). The adjusted CFR estimates for the 2013-2016 West African epidemic were 82.8% (95% CI 45%.6-85.6%) overall and 89.1% (95% CI 40.8%-91.6%) , 65.6% (95% CI 61.3%-69.6%) and 79.2% (95% CI 45.4-84.1) for Sierra Leone, Guinea and Liberia, respectively. We found that district, hospitalisation status, age, case classification and quarter explained 93.6% of the variance in the naïve CFR.ConclusionsThe adjusted CFR estimates improved the naïve CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted estimates will inform public health contingency planning for future Ebola epidemic, and help better allocate resources and evaluate the effectiveness of future inventions.

Journal article

Forna A, Dorigatti I, Nouvellet P, Donnelly Cet al., 2020, Spatiotemporal variability in case fatality ratios for 2013–2016 Ebola epidemic in West Africa, International Journal of Infectious Diseases, Vol: 93, Pages: 48-55, ISSN: 1201-9712

Background: For 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 ofspatiotemporal heterogeneity in the case fatality ratios (CFR).Methods: We applied a non-parametric Boosted Regression Trees (BRT) imputation approach for patientswith missing survival outcomes and adjusted for model imperfection. Semivariogram analysis andkriging were used to investigate spatiotemporal heterogeneities.Results: CFR estimates varied significantly between districts and over time over the course of theepidemic. BRT modelling accounted for most of the spatiotemporal variation and interactions in CFR, butmoderate spatial autocorrelation remained for distances up to approximately 90 km. Combining districtlevel CFR estimates and kriged district-level residuals provided the best linear unbiased predicted map ofCFR accounting for the both explained and unexplained spatial variation. Temporal autocorrelation wasnot observed in the district-level residuals from the BRT estimates.Conclusions: This study provides new insight into the epidemiology of the 2013–2016 West African Ebolaepidemic with a view of informing future public health contingency planning, resource allocation andimpact assessment. The analytical framework developed in this analysis, coupled with key domainknowledge, could be deployed in real time to support the response to ongoing and future outbreaks.

Journal article

Forna A, Nouvellet P, Dorigatti I, Donnelly CAet al., 2019, Case fatality ratio estimates for the 2013-2016 West African Ebola epidemic: application of Boosted Regression Trees for imputation, Publisher: ELSEVIER SCI LTD, Pages: 128-128, ISSN: 1201-9712

Conference paper

The Ebola Outbreak Epidemiology Team, Bhatia S, Cori A, Donnelly CA, Dorigatti I, Ferguson NM, Fitzjohn RG, Forna A, Garske T, Gaythorpe KAM, Imai N, Nouvellet Pet al., 2018, Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April–May, 2018: an epidemiological study, The Lancet, Vol: 392, Pages: 213-221, ISSN: 0140-6736

BackgroundOn May 8, 2018, the Government of the Democratic Republic of the Congo reported an outbreak of Ebola virus disease in Équateur Province in the northwest of the country. The remoteness of most affected communities and the involvement of an urban centre connected to the capital city and neighbouring countries makes this outbreak the most complex and high risk ever experienced by the Democratic Republic of the Congo. We provide early epidemiological information arising from the ongoing investigation of this outbreak.MethodsWe classified cases as suspected, probable, or confirmed using national case definitions of the Democratic Republic of the Congo Ministère de la Santé Publique. We investigated all cases to obtain demographic characteristics, determine possible exposures, describe signs and symptoms, and identify contacts to be followed up for 21 days. We also estimated the reproduction number and projected number of cases for the 4-week period from May 25, to June 21, 2018.FindingsAs of May 30, 2018, 50 cases (37 confirmed, 13 probable) of Zaire ebolavirus were reported in the Democratic Republic of the Congo. 21 (42%) were reported in Bikoro, 25 (50%) in Iboko, and four (8%) in Wangata health zones. Wangata is part of Mbandaka, the urban capital of Équateur Province, which is connected to major national and international transport routes. By May 30, 2018, 25 deaths from Ebola virus disease had been reported, with a case fatality ratio of 56% (95% CI 39–72) after adjustment for censoring. This case fatality ratio is consistent with estimates for the 2014–16 west African Ebola virus disease epidemic (p=0·427). The median age of people with confirmed or probable infection was 40 years (range 8–80) and 30 (60%) were male. The most commonly reported signs and symptoms in people with confirmed or probable Ebola virus disease were fever (40 [95%] of 42 cases), intense general fatigue (37 [90%] of 41 cases), an

Journal article

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