Marburg Virus Disease outbreaks, mathematical models, and disease parameters: a Systematic Review

Abstract Background Recent Marburg virus disease (MVD) outbreaks in Equatorial Guinea and Tanzania highlighted the importance of better understanding this highly lethal infectious pathogen. Past epidemics of Ebola, COVID-19, and other pathogens have re-emphasised the usefulness of mathematical models in guiding public health responses during outbreaks.Methods We conducted a systematic review, registered with PROSPERO (CRD42023393345) and reported according to PRISMA guidelines, of peer-reviewed papers reporting historical out-breaks, modelling studies and epidemiological parameters focused on MVD, including contextual information. We searched PubMed and Web of Science until 31st March 2023. Two reviewers evaluated all titles and abstracts, with consensus-based decision-making. To ensure agreement, 31% (13/42) of studies were double-extracted and a custom-designed quality assessment questionnaire was used to assess the risk of bias.Findings We present detailed outbreak, model and parameter information on 970 reported cases and 818 deaths from MVD until 31 March 2023. Analysis of historical outbreaks and sero-prevalence estimates suggests the possibility of undetected MVD outbreaks, asymptomatic transmission and/or cross-reactivity with other pathogens. Only one study presented a mathematical model of MVD transmission. We estimate an unadjusted, pooled total random effect case fatality ratio for MVD of 61.9% (95% CI: 38.8-80.6%,I2=93%). We identify key epidemiological parameters relating to transmission and natural history for which there are few estimates.Interpretation This review provides a comprehensive overview of the epidemiology of MVD, identifying key knowledge gaps about this pathogen. The extensive collection of knowledge gathered here will be crucial in developing mathematical models for use in the early stages of future outbreaks of MVD. All data are published alongside this article with functionality to easily update the database as new data become available.Funding MRC Centre for Global Infectious Disease AnalysisResearch in Context <jats:list list-type="bullet">Evidence before this studyWe searched Web of Science and PubMed up to 31 March 2023 using the search terms Marburg virus, epidemiology, outbreaks, models, transmissibility, severity, delays, risk factors, mutation rates and seroprevalence. We found five systematic reviews, all of which considered MVD alongside Ebola virus disease (EVD). One modelling study of Marburg virus disease (MVD) focused on animals, and not on computational models to understand past or project future disease transmission. One systematic review collated risk factors for transmission based on four MVD studies, but did not report attack rates due to missing underlying MVD estimates; another systematic review pooled estimates of MVD case fatality ratios (CFR): 53.8% (95% CI: 26.5–80.0%) and seroprevalence: 1.2% (95% CI: 0.5–2.0%). No systematic review covered transmission models of MVD, and the impact of public health and social measures is unknown.Added value of this studyWe provide a comprehensive summary of the available, peer-reviewed literature of historical outbreaks, transmission models and parameters for MVD. Meta-analysis of existing estimates of CFRs, and our original estimates based on historical outbreak information, illustrate the severity of MVD with our pooled random effect estimated CFR of 61.9% (95% CI: 38.8-80.6%,I2=93%). We demonstrate the sparsity of evidence on MVD transmission and disease dynamics, particularly on transmissibility and natural history, which are key input parameters for computational models supporting outbreak response. Our work highlights key areas where further disease characterization is necessary.Implications of all the available evidencePrevious outbreaks of infectious pathogens emphasized the usefulness of computational modelling in assessing epidemic trajectories and the impact of mitigation strategies. Our study provides necessary information for using mathematical models in future outbreaks of MVD, identifies uncertainties and knowledge gaps in MVD transmission and natural history, and highlights the severity of MVD..

Medienart:	Preprint

Erscheinungsjahr:	2024
Erschienen:	2024

Enthalten in:	bioRxiv.org - (2024) vom: 23. Apr. Zur Gesamtaufnahme - year:2024

Sprache:	Englisch

Beteiligte Personen:

Cuomo-Dannenburg, Gina [VerfasserIn] McCain, Kelly [VerfasserIn] McCabe, Ruth [VerfasserIn] Unwin, H. Juliette T. [VerfasserIn] Doohan, Patrick [VerfasserIn] Nash, Rebecca K. [VerfasserIn] Hicks, Joseph T. [VerfasserIn] Charniga, Kelly [VerfasserIn] Geismar, Cyril [VerfasserIn] Lambert, Ben [VerfasserIn] Nikitin, Dariya [VerfasserIn] Skarp, Janetta [VerfasserIn] Wardle, Jack [VerfasserIn] Kont, Mara [VerfasserIn] Bhatia, Sangeeta [VerfasserIn] Imai, Natsuko [VerfasserIn] van Elsland, Sabine [VerfasserIn] Cori, Anne [VerfasserIn] Morgenstern, Christian [VerfasserIn]

Links:	Volltext [lizenzpflichtig] Volltext [kostenfrei]

Themen:	570 Biology

doi:	10.1101/2023.07.10.23292424

funding:
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PPN (Katalog-ID):	XBI04018305X