Ebola Virus Disease mathematical models and epidemiological parameters: a systematic review and meta-analysis

Abstract

Background: Ebola Virus Disease (EVD) poses a recurring risk to human health. Modelling can provide key insights informing epidemic response, hence synthesising current evidence about EVD epidemiology and models is critical to prepare for future outbreaks. Methods: We conducted a systematic review (PROSPERO CRD42023393345) and meta-analysis of EVD transmission models and parameters characterising EVD transmission, evolution, natural history, severity, risk factors and seroprevalence published prior to 7th July 2023 from PubMed and Web of Science. Two people screened each abstract and full text. Papers were extracted using a bespoke Access database, 10% were double extracted. Meta-analyses were conducted to synthesise information where possible. Findings: We extracted 1,280 parameters and 295 models from 522 papers. Basic reproduction number estimates were highly variable (central estimates between 0.1 and 12.0 for high quality assessment scores), as were effective reproduction numbers, likely reflecting spatiotemporal variability in interventions. Pooled random effect estimates were 15.4 days (95% Confidence Interval (CI) 13.2-17.5) for the serial interval, 8.5 (95% CI 7.7-9.2) for the incubation period, 9.3 (95% CI 8.5-10.1) for the symptom-onset-to-death delay and 13.0 (95% CI 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 (from 0 to 100%), which could reflect heterogeneity in underlying risk factors such as age and caring responsibilities. Interpretation: While a significant body of literature exists on EVD models and epidemiological parameter estimates, many of these studies focus on the West African Ebola epidemic and are primarily associated with Zaire Ebola virus. This leaves a critical gap in our knowledge regarding other Ebola virus species and outbreak contexts. Funding: UKRI, NIHR, Academy of Medical Sciences, Wellcome, UK Department for Business, Energy, and Industrial Strategy, BHF, Diabetes UK, Schmidt Foundation, Community Jameel, Royal Society, and Imperial College London.