Search

Ebola virus disease poses a recurring risk to human health. We conducted a systematic review (PROSPERO CRD42023393345) of Ebola virus disease transmission models and parameters published from database inception to July 7, 2023, from PubMed and Web of Science. Two people screened each abstract and full text. Papers were extracted with a bespoke Access database, 10% were double extracted. We extracted 1280 parameters and 295 models from 522 papers. Basic reproduction number estimates were highly variable, as were effective reproduction numbers, likely reflecting spatiotemporal variability in interventions. Random-effect estimates were 15·4 days (95% CI 13·2-17·5) for the serial interval, 8·5 days (7·7-9·2) for the incubation period, 9·3 days (8·5-10·1) for the symptom-onset-to-death delay, and 13·0 days (10·4-15·7) for symptom-onset-to-recovery. Common effect estimates were similar, albeit with narrower CIs. Case-fatality ratio estimates were generally high but highly variable, which could reflect heterogeneity in underlying risk factors. Although a substantial body of literature exists on Ebola virus disease models and epidemiological parameter estimates, many of these studies focus on the west African Ebola epidemic and are primarily associated with Zaire Ebola virus, which leaves a key gap in our knowledge regarding other Ebola virus species and outbreak contexts., Competing Interests: Declaration of interests AC reports payment from Pfizer for teaching mathematical modelling of infectious diseases. PD reports payment from WHO for consulting on integrated modelling. AC was supported by the Academy of Medical Sciences Springboard scheme (reference SBF005\1044). CM acknowledges the Schmidt Foundation for research funding (grant code 6–22–63345). PD and TN received funding from Community Jameel. DJ has received funding from the Wellcome Trust and Royal Society (216427/Z/19/Z) and PhD funding from Engineering and Physical Sciences Research Council. GC-D has received funding from the Royal Society. RM has received funding from the National Institute for Health and Care Research Health Protection Research Unit in Emerging and Zoonotic Infections, a partnership between the UK Health Security Agency, University of Oxford, University of Liverpool, and Liverpool School of Tropical Medicine (grant code NIHR200907). JW has received research funding from the Wellcome Trust (grant 102169/Z/13/Z). RKN and DN have received research funding from the Medical Research Council Doctoral Training Partnership (grant MR/N014103/1). KM acknowledges research funding from the Imperial College President's PhD Scholarship. AF acknowledges funding from the commonwealth scholarship commission. KF acknowledges funding from the Bill & Melinda Gates Foundation, Gavi, and the Wellcome Trust. HJTU has received funding from the Moderna Charitable Foundation. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

Background: Machine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML algorithms behave within the context of outbreak data where missingness of data is almost ubiquitous., Methods: Using simulated data, we use a ML algorithmic framework to evaluate data imputation performance and the resulting case fatality ratio (CFR) estimates, focusing on the scale and type of data missingness (i.e., missing completely at random-MCAR, missing at random-MAR, or missing not at random-MNAR)., Results: Across ML methods, dataset sizes and proportions of training data used, the area under the receiver operating characteristic curve decreased by 7% (median, range: 1%-16%) when missingness was increased from 10% to 40%. Overall reduction in CFR bias for MAR across methods, proportion of missingness, outbreak size and proportion of training data was 0.5% (median, range: 0%-11%)., Conclusion: ML methods could reduce bias and increase the precision in CFR estimates at low levels of missingness. However, no method is robust to high percentages of missingness. Thus, a datacentric approach is recommended in outbreak settings-patient survival outcome data should be prioritised for collection and random-sample follow-ups should be implemented to ascertain missing outcomes., Competing Interests: The authors have declared that no competing interest exist.

Background: The 2013-2016 West African Ebola epidemic has been the largest to date with >11 000 deaths in the affected countries. The data collected have provided more insight into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naive CFR remain limited because 44% of survival outcomes were unreported., Methods: Using a boosted regression tree model, we imputed survival outcomes (ie, 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., Results: The out-of-sample performance (95% confidence interval [CI]) of our model was good: sensitivity, 69.7% (52.5-75.6%); specificity, 69.8% (54.1-75.6%); percentage correctly classified, 69.9% (53.7-75.5%); and area under the receiver operating characteristic curve, 76.0% (56.8-82.1%). The adjusted CFR estimates (95% CI) for the 2013-2016 West African epidemic were 82.8% (45.6-85.6%) overall and 89.1% (40.8-91.6%), 65.6% (61.3-69.6%), and 79.2% (45.4-84.1%) for Sierra Leone, Guinea, and Liberia, respectively. We found that district, hospitalisation status, age, case classification, and quarter (date of case reporting aggregated at three-month intervals) explained 93.6% of the variance in the naive CFR., Conclusions: The adjusted CFR estimates improved the naive 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 epidemics, and help better allocate resources and evaluate the effectiveness of future inventions., (© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America.)

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 of spatiotemporal heterogeneity in the case fatality ratios (CFR)., Methods: We 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., Results: CFR 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., Conclusions: This 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., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)