Systematic review of pathogens with pandemic potential: Two Talk Special
TALK ONE
Speakers: Sangeeta Bhatia (Imperial College London), Juliette Unwin (University of Bristol), Christian Morgenstern (Imperial College London), Patrick E Doohan (Imperial College London)
Speakers: Sangeeta Bhatia, UKHSA and Imperial College London, H Juliette T Unwin, University of Bristol and Imperial College London
Title: Ebola virus disease mathematical models and epidemiological parameters: a systematic review
Abstract
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. 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.
These reviews are a part of a broader initiative by the Pathogen Epidemiology Review Group to compile a comprehensive library of epidemiological parameters and mathematical models of all pathogens identified as having pandemic potential by the WHO. We will introduce our project, the overarching methodology, and the accompanying R package {epireview} before delving into the results.
TALK TWO
Speakers: Christian Morgenstern, Imperial College London, Patrick E Doohan, Imperial College London
Title: Lassa fever outbreaks, mathematical models, and disease parameters: a systematic review and meta-analysis
Abstract:
Background Lassa fever, caused by Lassa virus (LASV), poses a significant public health threat in West Africa. Understanding the epidemiological parameters and transmission dynamics of LASV is crucial for informing evidence-based interventions and outbreak response strategies.
Methods We conducted a systematic review (PROSPERO CRD42023393345) to compile and analyse key epidemiological parameters, mathematical models, and past outbreaks of LASV. Data were double extracted from published literature, focusing on past outbreaks, seroprevalence, transmissibility, epidemiological delays, and disease severity.
Findings We found 157 publications meeting our inclusion criteria and extracted 374 relevant parameter estimates. Although LASV is endemic in West Africa, spatiotemporal coverage of recent seroprevalence estimates, ranging from 0.06% to 35%, was poor. Highlighting the uncertainty in LASV risk spatially. Similarly, only two basic reproduction number estimates at 1.13 and 1.19 were available. We estimated a pooled total random effect case fatality ratio of 33.1% (95% CI: 25.7 – 41.5, I^2 = 94%) and found potential variation in severity by geographic regions typically associated with specific LASV lineages. We estimated a pooled total random effect mean symptom-onset-to-hospital-admission delay of 8.3 days (95% CI: 7.4 – 9.3, I^2 = 92%), but other epidemiological delays were poorly characterised.
Interpretation Our findings highlight the relative lack of empirical LASV parameter estimates despite its high severity. Improved surveillance to capture mild cases and approaches that integrate rodent populations are needed to better understand LASV transmission dynamics. Addressing these gaps is essential for developing accurate mathematical models and informing evidence-based interventions to mitigate the impact of Lassa fever on public health in endemic regions.