Marburg Virus Disease (MVD)

In 2018, the World Health Organization (WHO) published a list of nine known pathogens (in addition to an unknown Pathogen X) for research and development (R&D) prioritisation, due to both their epidemic and pandemic potential and the absence of licensed vaccines or therapeutics. Among these prioritised pathogens is MVD, a highly-lethal infectious Filoviridae single-stranded RNA virus first described in Germany and Serbia (formerly Yugoslavia) in 1967. Subsequent outbreaks of this virus have primarily occurred in sub-Saharan Africa. The Pathogen Epidemiology Review Group (PERG) has published a systematic review for MVD, if you use any of our results please cite our paper:

@article{marburg_systematic_review_2023, author = {Gina Cuomo-Dannenburg and Kelly McCain and Ruth McCabe and H Juliette T Unwin and Patrick Doohan and Rebecca K Nash and Joseph T Hicks and Kelly Charniga and Cyril Geismar and Ben Lambert and Dariya Nikitin and Janetta E Skarp and Jack Wardle and Pathogen Epidemiology Review Group and Mara Kont and Sangeeta Bhatia and Natsuko Imai and Sabine L van Elsland and Anne Cori and Christian Morgenstern}, year={2023},
title={{Marburg Virus Disease outbreaks, mathematical models, and disease parameters: a Systematic Review}},
doi = {10.1101/2023.07.10.23292424}, publisher = {Cold Spring Harbor Laboratory Press}, URL = {https://www.medrxiv.org/content/early/2023/07/12/2023.07.10.23292424}, eprint = {https://www.medrxiv.org/content/early/2023/07/12/2023.07.10.23292424.full.pdf}, journal = {medRxiv} }

All Tables and Figures from the paper are re-produced below on the latest available data in our data set. For convenience we label the Figures and Tables with the same numbers as in the paper.

Outbreaks

Table 1: Overview of MVD outbreaks i.e. location, timing, and size, as reported in the studies included in this review. We report in bold the country and outbreak year, the location refers to the place of the actual outbreak in the country if known. Blank cells correspond to information which we were unable to find in or extract from the literature.

Country	Location	Article	Start	End	Deaths	Confirmed	Suspected	Asymptomatic	Severe/hospitalised	Confirmation Method
Angola		Carroll2013	2005	2005	227	252			0	Not specified
Angola		Palvin2014	2004	2005	227	252
Angola	Uige Province	Towner2006	Oct2004	Jul2005	227	252			0
Democratic Republic of the Congo	Durba and Watsa	Borchert2002	Oct1998	May1999	61	73			0	Symptoms
Democratic Republic of the Congo	Durba and Watsa	Bausch2006	Oct1998	Sep2000	125	48	106		0	Molecular
Democratic Republic of the Congo	Durba and Watsa villages	Borchert2006	Oct1998	Aug2000		76	33	0		Molecular
Democratic Republic of the Congo		Palvin2014	1998	2000	128	154
Germany	Marburg, Frankfurt am Main	Albarino2013	18Aug1968	13Nov1968	5	23		1		Symptoms
Germany,Yugoslavia	Marburg (23), Frankfurt am Main (6), Belgrade (2)	Martini1973	Aug1968	Nov1968	7	31		1		Symptoms
Germany,Yugoslavia		Pavlin2014	1967		7	31
Kenya		Pavlin2014	1980		1	2
Kenya		Pavlin2014	1987		1	1
Netherlands		Pavlin2014	2008		1	1
Russian Federation		Pavlin2014	1988		1	1
Russian Federation		Pavlin2014	1990		0	1
South Africa,Zimbabwe	Johannesburg	Conrad1978	12Feb1975		1	3			1	Molecular
Uganda	Kamwenge and Ibanda	Adjemian2011	10Jun2007	14Sep2007	1	4			0	Molecular
Uganda	Kabale; Ibanda; Mbarara; Kampala	Albarino2013	18Oct2012	7Nov2012	4	15				Molecular
Uganda	Ibanda, Kabale and Kamwenge Districts	Knust2015	Jul2012	10Nov2012	15	15	11			Molecular
Uganda	Ibanda and Kabale	Mbonye2012	Sep2012	13Nov2012	7	9	5			Molecular
Uganda	Kampala	Nyakarahuka2017	17Sep2014	28Sep2014	1	1			1	Molecular
Uganda		Pavlin2014	2007		2	4
United States	Colorado	Pavlin2014	2008		0	1

Parameters

Reproduction numbers

Figure 3 (A): Estimates of the reproduction number. The blue and red points correspond to estimates of the effective reproduction number (Re) and basic reproduction number (R0) respectively, with associated uncertainty shown by the solid lines where available. The dashed vertical line presents the threshold for epidemic growth.

Severity

Figure 2 (A): Case Fatality Ratio (CFR) meta-analyses, using logit-transformed proportions and a generalized linear mixed-effects model (full details in SI A.4 in the paper). The forest plot displays studies includedin each meta-analyis: the red squares indicate study weight, and for each study, a 95% binomial confidence interval is provided. As summaries we display the total common effects, where all data are effectively pooled and assumed to come from a single data-generating process with one common CFR and total random effect estimates, which allow the CFR to vary by study and accordingly give different weights to each study when determining an overall estimate [19]. CFR estimates reported in the included studies.

Figure 2 (B): CFR estimated from extracted outbreak data, including only one observation per outbreak using the study with the longest duration of the outbreak reported ensuring no case is double counted.

Figure S2 (A): Overview of the estimates of the case fatality ratio (CFR) obtained from the included studies. CFR estimates reported in the included studies, stratified according to estimation method. Points represent central estimates. Error bars represent an uncertainty interval associated with the point estimate, as reported in the original study.

Figure S2 (B): Overview of the estimates of the case fatality ratio (CFR) obtained from the included studies. CFR estimated from extracted outbreak data, including only one observation per outbreak using the study with the longest duration of the outbreak reported ensuring each case is not double counted. Shaded bars represents the imputed Binomial confidence interval for studies with a sample size, n > 1. Vertical dotted lines represent 0% and 100% CFR.

Delays

Table 2: Overview of the delay parameter estimates extracted from the included studies. These are stratified into five categories: Generation Time, Incubation Period, Time in Care, Time from Symptom to Careseeking and Time from Symptom to Outcome. Estimates and associated uncertainty are provided, along with information regarding the population group corresponding to the estimate and the timing and location of the outbreak. ‘Other’ refers to a range of different values which are specified in the underlying papers.

Article	Country	Parameter type	Delays (days)	Statistic	Uncertainty type	Population Group	Timing of survey	Outcome
Ajelli2012	Angola	Generation Time	9.0	Mean	CI95%	General population	Mid outbreak
Ajelli2012	Angola	Generation Time	5.4	Standard Deviation	CI95%	General population	Mid outbreak
Ajelli2012	Angola	Generation Time	9.3	Mean	CI95%	General population	Mid outbreak
Martini1973	Germany,Yugoslavia	Incubation Period			Range
Gear1975	South Africa	Incubation Period			Range	Healthcare workers	Mid outbreak
Knust2015	Uganda	Time In Care	14.3	Mean	Range	General population	Post outbreak	Other
Bausch2006	Democratic Republic of the Congo	Time Symptom To Careseeking	4.5	Median	Range	Other	Mid outbreak
Gear1975	South Africa	Time Symptom To Careseeking	4.0	Other		Other	Start outbreak
Knust2015	Uganda	Time Symptom To Careseeking	4.0	Mean		General population
Ajelli2012	Angola	Time Symptom To Outcome	7.0	Median	Range	General population	Mid outbreak	Death
Ajelli2012	Angola	Time Symptom To Outcome	9.0	Median	Range	General population	Mid outbreak	Death
Gear1975	South Africa	Time Symptom To Outcome	9.0			Other	Start outbreak	Other
Knust2015	Uganda	Time Symptom To Outcome	9.0	Mean	Range	General population	Post outbreak	Death
Knust2015	Uganda	Time Symptom To Outcome	22.0	Mean	Range	General population		Other

Figure 3 (B): Delay parameters, stratified into five categories: Generation Time, Incubation Period, Time in Care, Time from Symptom to Careseeking and Time from Symptom to Outcome as indicated by different colours.

Seroprevalence

Table 4: Overview of seroprevalence estimates as reported in the included studies. Estimates were primarily reported as percentages, as shown here. Associated uncertainty and sample sizes are provided where these were reported. Where available, additional information regarding the location and timing of the estimates, the antibody being tested for, the target population, the timing in relation to any ongoing outbreak and the availability of disaggregated data is also summarised.

Article	Country	Parameter type*	Seroprevalence (%)	Uncertainty type	Number Seropositive	Sample size	Population Group	Timing of survey	Disaggregated data available
Gonzalez1989	Cameroon,Central African Republic,Chad,Republic of the Congo,Equatorial Guinea,Gabon	Unspecified	0.39		20	5,070	General population		Region
Johnson1993	Central African Republic	IFA			3	427	Outdoor workers
Johnson1993	Central African Republic	IFA	3.20	Range	137	4,295	General population	Pre outbreak	Age, Region, Sex
Gonzalez2000	Central African Republic	IgG	2.40	Range	33	1,340
Borchert2005	Democratic Republic of the Congo	IFA	0.00	Range	0	300		Post outbreak
Bausch2003	Democratic Republic of the Congo	IgG	2.00		15	912		Mid outbreak
Borchert2006	Democratic Republic of the Congo	IgG	1.65	CI95%			Household contacts of survivors	Post outbreak
Borchert2007	Democratic Republic of the Congo	IgG	2.10				Healthcare workers	Post outbreak
Ivanoff1982	Gabon	IFA	0.00		0	197
Ivanoff1982	Gabon	IFA	0.00		0	28	Pregnant women
Ivanoff1982	Gabon	IFA	0.00		0	28	Other
Becker1992	Germany	IgG	2.60				Other
Johnson1983	Kenya	IFA			8	1,899			Region
Johnson1983	Kenya	IFA	0.00		0	741	General population	Post outbreak
Martini1973	Kenya	Unspecified			0	79	Other	Post outbreak
Smith1982	Kenya	Unspecified			2	186	Persons under investigation	Post outbreak
Smith1982	Kenya	Unspecified			3	100	Healthcare workers	Post outbreak
Smith1982	Kenya	Unspecified			0	224	General population	Post outbreak
Smith1982	Kenya	Unspecified			0	63	Other	Post outbreak
Smith1982	Kenya	Unspecified			0	79	Other	Post outbreak
Smith1982	Kenya	Unspecified			0	44	Outdoor workers	Post outbreak
Van der Waals1986	Liberia	IFA	1.30		3	225	Other	Other	Other, Region
Mathiot1989	Madagascar	Unspecified	0.00		0	381		Other	Region
Tomori1988	Nigeria	IFA	1.70		29	1,677	General population
Moyen2015	Republic of the Congo	IgG	0.50					Pre outbreak
Schoepp2014	Sierra Leone	IgM	3.60				Persons under investigation	Other
Rugarabamu2022	Tanzania	IgM	0.30		1	308		Other	Region
Rodhain1989	Uganda	HAI/HI	4.50		6	132
Evans2018	Uganda	IgG			0	331	Other
*HAI/HI: Hemagglutination Inhibition Assay; IFA: Indirect Fluorescent Antibody assay; IgG: Immunoglobulin G; IgM: Immunoglobulin M; Unspecified assay.

Molecular evolutionary rates

Figure 3 (C): Evolutionary rates. Colours indicate different genome types; points represent central estimates. Solid lines represent an uncertainty interval associated with the point estimate while ribbons indicate a parameter value +/- standard error with a minimum value of 0.

Risk Factors

Table 3: Aggregated information on risk factors associated with MVD infection and seropositivity. Risk factors were mapped onto our risk factor classification (see Supplement) by interpreting the authors’ descriptions. Adjusted refers to whether estimates are adjusted (i.e. from a multivariate analysis) or not (i.e. from a univariate analysis), with unknown showing that this information is not clearly stated in the original study. Statistical significance was determined according to the original authors’ statistical approaches when specified, or using a p-value of 0.05 otherwise. The numbers in the significant and not significant columns represent the total sample size included in the analyses for this risk factor and outcome category.

Outcome	Risk factor	Adjusted	Significant	Not significant
Infection	Contact with animal	Unknown	0
Infection	Gathering	Unknown	128
Infection	Household contact	Unknown	102
Infection	Occupation - Funeral and burial services	Unknown	102
Infection	Other	Unknown	102	26
Infection	Sex	Unknown		26
Seropositivity	Contact with animal	Adjusted		912
Seropositivity	Contact with animal	Unknown		300
Seropositivity	Gathering	Unknown		300
Seropositivity	Hospitalisation	Adjusted	915
Seropositivity	Household contact	Adjusted		912
Seropositivity	Occupation - Funeral and burial services	Adjusted		912

Figure S4: More detailed table of risk factor data from the extracted studies, giving countries, times and contexts of surveys and non-aggregated information on each risk factor assessed in the four relevant studies.

Article	Country	Outcome	Risk factor	Significant	Adjusted	Sample size	Population sample type	Population group	Timing of survey
Borchert2005	Angola	Infection	Household contact	Significant	Unknown	102	Hospital based	Persons under investigation	Mid outbreak
Borchert2005	Angola	Infection	Other	Significant	Unknown	102	Hospital based	Persons under investigation	Mid outbreak
Borchert2005	Angola	Infection	Gathering	Significant	Unknown	102	Hospital based	Persons under investigation	Mid outbreak
Borchert2005	Angola	Infection	Occupation - Funeral and burial services	Significant	Unknown	102	Hospital based	Persons under investigation	Mid outbreak
Amman2012	Uganda	Infection	Contact with animal	Significant	Unknown
Amman2012	Uganda	Infection	Other	Significant	Unknown
Knust2015	Uganda	Infection	Gathering	Significant	Unknown	26	Community based	General population	Post outbreak
Knust2015	Uganda	Infection	Other	Not significant	Unknown	26	Community based	General population	Post outbreak
Knust2015	Uganda	Infection	Sex	Not significant	Unknown	26	Community based	General population	Post outbreak
Bausch2003	Democratic Republic of the Congo	Serology	Contact with animal	Not significant	Adjusted	912	Population based
Bausch2003	Democratic Republic of the Congo	Serology	Household contact	Not significant	Adjusted	912	Population based
Bausch2003	Democratic Republic of the Congo	Serology	Occupation - Funeral and burial services	Not significant	Adjusted	912	Population based
Bausch2003	Democratic Republic of the Congo	Serology	Hospitalisation	Significant	Adjusted	915	Population based
Borchert2005	Democratic Republic of the Congo	Serology	Contact with animal	Not significant	Unknown	300	Community based		Post outbreak
Borchert2005	Democratic Republic of the Congo	Serology	Gathering	Not significant	Unknown	300	Community based		Post outbreak

Quality Assessment

Figure S3: (A) Count of papers for each quality assessment question scoring Yes, No or not applicable. (B) Quality Assessment Score defined as proportion of Yes votes for each paper relative to sum of Yes and No answers, removing NAs. The time trend is fitted using Local Polynomial Regression Fitting.

Latest update: 2024-06-26