Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
DOI 10.1186/s12879-016-2045-6
RESEARCH ARTICLE
Open Access
How severe and prevalent are Ebola and
Marburg viruses? A systematic review and
meta-analysis of the case fatality rates and
seroprevalence
Luke Nyakarahuka1,2,5* , Clovice Kankya2, Randi Krontveit3, Benjamin Mayer4, Frank N. Mwiine2, Julius Lutwama5
and Eystein Skjerve1
Abstract
Background: Ebola and Marburg virus diseases are said to occur at a low prevalence, but are very severe diseases
with high lethalities. The fatality rates reported in different outbreaks ranged from 24–100%. In addition, sero-surveys
conducted have shown different seropositivity for both Ebola and Marburg viruses. We aimed to use a meta-analysis
approach to estimate the case fatality and seroprevalence rates of these filoviruses, providing vital information
for epidemic response and preparedness in countries affected by these diseases.
Methods: Published literature was retrieved through a search of databases. Articles were included if they reported
number of deaths, cases, and seropositivity. We further cross-referenced with ministries of health, WHO and
CDC databases. The effect size was proportion represented by case fatality rate (CFR) and seroprevalence.
Analysis was done using the metaprop command in STATA.
Results: The weighted average CFR of Ebola virus disease was estimated to be 65.0% [95% CI (54.0–76.0%),
I2 = 97.98%] whereas that of Marburg virus disease was 53.8% (26.5–80.0%, I2 = 88.6%). The overall seroprevalence of
Ebola virus was 8.0% (5.0%–11.0%, I2 = 98.7%), whereas that for Marburg virus was 1.2% (0.5–2.0%, I2 = 94.8%). The most
severe species of ebolavirus was Zaire ebolavirus while Bundibugyo Ebolavirus was the least severe.
Conclusions: The pooled CFR and seroprevalence for Ebola and Marburg viruses were found to be lower than usually
reported, with species differences despite high heterogeneity between studies. Countries with an improved
health surveillance and epidemic response have lower CFR, thereby indicating need for improving early detection
and epidemic response in filovirus outbreaks.
Keywords: Ebola virus disease, Marburg virus disease, Case fatality rate, Meta-analysis, Systematic review,
Seroprevalence
Background
Ebola virus disease (EVD) and Marburg virus disease
(MVD) are caused by filoviruses in the family Filoviridae
and are both associated with high case fatality rates
(CFR). The World Health organization (WHO) reports
that the CFR of EVD ranges from 25.0 to 90.0% while
that of MVD ranges from 24.0 to 88.0% [1]. In the early
* Correspondence: nyakarahuka@gmail.com; n3luke@covab.mak.ac.ug
1
Norwegian University of Life Sciences, Oslo, Norway
2
Makerere University, Kampala, Uganda
Full list of author information is available at the end of the article
phases of a major Ebola outbreak in West Africa,
CFR was reported to be 70.8% [2]. The CFR of EVD
seems to be species dependent with Ebola Zaire and
Ebola Sudan species being most pathogenic (with a
reported CFR of 100%), while Ebola Bundibugyo appears to have a lower CFR at 34% [3]. A recent study
by Lefebvre et al. that used data from WHO database
estimated the CFR of EVD to be 65.4% irrespective of
the Ebola virus species [4]. A few studies have tried
to pool the CFR of EVD and MVD, but did not use
the meta-analysis approach [5].
© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
Although EVD is known to be very severe, there are
some species of Ebola virus that cause less serious disease. For example, Taï Forest ebolavirus, formerly known
as Côte d’Ivoire ebolavirus, has not been associated with
any fatality and the only case ever reported recovered
from the disease [6]. While there have been some reports of EVD being associated with a CFR of 100%, this
CFR is attributed to only a single case fatality that did
not result into transmission of the virus to other individuals [7, 8]. It seems that CFR differs from species to species, however, both Ebola Sudan and Ebola Zaire have
shown a CFR of 100% [1]. Also, the CFR of the MVD
outbreak that occurred in Uganda in 2014 was reported
to be 100%, but again only one person was diagnosed
and died from the disease [9]. The largest MVD outbreak was in Angola in 2004 with CFR of 90% [10] and
in Democratic Republic of Congo (DRC) in 1998 with
CFR of 83% [11].
There is evidence that a substantial proportion of infected humans in Central Africa seem to recover without
being detected by the health care system, and apparently
healthy individuals have been found to be seropositive
for Ebola and Marburg viruses [12–15]. Furthermore,
Marburg virus has been found in apparently healthy
cave-dwelling fruit bats of species rousettus aegyptiacus,
which are believed to be reservoirs for Marburg virus,
and responsible for the spill over into human populations [16–19]. Because of the variations in the reported
CFR and the presence of seropositive individuals, it is
important to determine the severity and prevalence of
these viral haemorrhagic fevers. This is important for
forecasts and risk analysis especially during outbreaks
for epidemic preparedness and response by affected
countries. This will help to estimate how many infected
people with EVD or MVD are likely to die from the disease during outbreaks. Whereas there are few studies
that have estimated CFR of EVD [4, 5], these did not use
a meta-analysis approach and no meta-analysis has been
performed on CFR of EVD, MVD, seroprevalence of
Ebola and Marburg viruses. Therefore, our aim was to
determine the overall weighted estimate (effect size) of
the CFR and seroprevalence of EVD and MVD using
available published literature on outbreak reports, WHO
and CDC databases and population based studies for
seroprevalence of filoviruses (Marburg and Ebola viruses). We also explored whether CFR and seroprevalence of these filoviruses differs according to virus
species and country.
Methods
Procedures for systematic reviews and meta-analysis
have been developed to summarize scientific evidence
from the literature. This work was done following the
guidelines published in the PRISMA statement [20]
Page 2 of 14
and MOOSE guidelines for observational studies [21]
as follows.
Literature search strategy
A detailed literature search was conducted by the authors in PubMed (as well as Medline), Web of Science
and Google Scholar until 5th October 2015. In cases
where there was no peer-reviewed publication for a
known outbreak, data was retrieved from websites of
WHO and CDC. The following key words were used;
“ebola”, “ebolavirus”, “viral haemorrhagic fevers”, “marburg virus disease”, “marburg haemorrhagic fever”, “marburg virus outbreak”, “ebola virus disease outbreak”,
“marburg virus”, “ebola outbreak”, “seroprevalence of
ebola virus”, “seroprevalence of marburg virus” and “risk
factors of viral haemorrhagic fevers”. The search included all articles and outbreak reports about EVD and
MVD and cross-referencing of primary articles was done
to obtain the original articles. Since the number of outbreaks of EVD and MVD are known and few, efforts
were made to obtain all information about these outbreaks from WHO and CDC websites and Ministries of
health of respective countries.
Study selection criteria
Studies were included in the meta-analysis if they reported the total number of cases and total number of
deaths from the outbreak of EVD or MVD. Also studies
that were reporting CFR and sero-prevalence in percentages were included. Studies or reports that did not include total number of deaths or cases were excluded as
well as studies that did not report original data (Fig. 1).
We also excluded studies that reported outbreaks of
Ebola species that are not pathogenic to humans and
those species that have not caused mortality in humans.
In cases where there were multiple publications, we used
the one with the most complete data or the most recent
one. In cases where there was controversy on the number of cases and deaths between studies, we crossreferenced with the respective ministries of health,
WHO or CDC databases to reconcile these discrepancies. Seroprevalence studies included were only those
that were population based and comprised apparently
healthy individuals. We excluded articles that reported
sero-prevalence during outbreaks or in sick individuals.
Data extraction
LN compiled a list of articles and discrepancies were discussed and resolved by consensus between FM, CK and
JL. We used a standardized data extraction form and the
following information was extracted for each qualifying
study and outbreak report: i) author; ii) Country; iii) number of cases; iv) number of deaths; v) CFR (if reported); vi)
month and year of outbreak; vii) year of publication viii)
Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
Page 3 of 14
Fig. 1 Flow diagram for search strategy and article selection process from the literature databases
and species involved. For population-based seroprevalence studies, the following additional information was retrieved: i) sample size and ii) number of
seropositive samples.
(prevalence) = ln [prevalence/ (1 − prevalence)] Variance
(logit) =1/ (np) +1/[n (1 − p)] [25]. The Begg’s and
Egger’s tests were used in combination with a funnel
plot to assess potential publication bias and visualised
using funnel plots [24, 26].
Statistical analysis
Data were collected in a Microsoft Excel® spreadsheet
and outcome measures were calculated. CFR was calculated as number of deaths divided by reported cases
whereas seroprevalence was calculated as number of individuals seropositive divided by total sample size in
each study. Our effect size (ES), the principal summary
measure, was the proportion represented by CFR and
seroprevalence. We used the newly developed metaprop
command [22] for performing meta-analysis of binomial
data in STATA (StataCorp, College Station, TX, USA).
The metaprop command was preferred to metan command because it implements procedures that are specific
to binomial data and is appropriate for dealing with proportions close to or at the margins and also uses the
Freeman-Tukey double arcsine transformations to
stabilize the variances [22]. The meta-analysis of CFR
was stratified by country and species where possible.
The following parameters were estimated: Cochran’s Q
indicating differences in true ESs, an estimate of the true
variance of ESs between studies (our estimate of τ2) and
Higgins I2 which is an estimate of what proportion of
the observed variance that reflects real differences in ES.
If I2 is close to 0, then almost all the observed variation
is spurious, and there is nothing to explain. If I2 is large,
then reasons for the observed variance should be evaluated [23, 24]. Sensitivity analysis was done by excluding
studies that reported very few numbers or zero deaths
or no seropositives. A meta-regression procedure was
done to assess if factors such as species, country, year
and month of outbreak influence CFR of both EVD and
MVD using the traditional logit-transformation: Logit
Results
Literature search result
Results from the literature search are illustrated in Fig. 1.
The literature search yielded 7551 articles. Of these, 4898
were excluded as duplicates. After reviewing the titles and
the abstract, only 153 articles were retrieved for detailed
evaluation. After full evaluation of retrieved publications,
72 articles were included in this study. Of those included in the study, 23 reported outbreaks of EVD
(Table 1) [3, 8, 27–41, 7, 42, 43], 12 reported outbreaks of
MVD (Table 2) [10, 11, 42, 44–51], 26 reported seroprevalence of Ebola virus (Table 3) [8, 12–14, 28, 31,
52–54, 29, 55–70] and 11 reported sero-prevalence of
Marburg virus (Table 4) [14, 15, 57, 61–64, 67, 71–73].
Most of the sero-prevalence studies reported both Marburg and Ebola viruses.
Two more outbreaks have occurred without human
mortalities namely Ebola Reston [74, 75] and another
caused by Taï Forest virus [6]. Zaire ebolavirus species was responsible for most of the outbreaks with
14/23 (60.9%) [8, 28, 30–32, 34–36, 39, 40, 41, 37,
76] followed by Sudan ebolavirus with 30.3% (7/23)
outbreaks [27, 29, 38, 7, 42, 77] and lastly Bundibugyo ebolavirus 8.7% (2/23) [3, 42]. Most articles reported DRC (7/23) [8, 28, 32, 39, 40, 42, 76] and Uganda
(5/23) [3, 33, 7, 42] as countries most affected by EVD
outbreaks. Other countries reported include Gabon (4/23)
[31, 34, 36, 78], Republic of Congo (3/23) [35, 37, 41],
South Sudan (3/23) [27, 29, 38] and multiple countries in
West Africa associated with the recent single outbreak
[79–82]. Interestingly, most of the EVD outbreaks
Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
Page 4 of 14
Table 1 Summary of the studies included in a systematic review and meta-analysis describing case fatality rate for Ebola virus
disease in Africa
Author and Year of Publication
Deaths
Cases
Country
Year and month of outbreak
WHO International Study Team, 1978 [27]
151
284
South Sudan
1976, June–November
International Commission, 1978 [28]
280
318
DRC
1976, Sept–Oct
Heymann et al., 1980 [8]
1
1
DRC
1977, June
Baron et al., 1983 [29]
22
34
South Sudan
1979, June–Oct
Amblard et al., 1997 [30]
30
49
Gabon
1994, November
Khan et al., 1999 [32]
255
315
DRC
1995, May
Georges et al., 1999 [31]
21
31
Gabon
1996, May
Milleliri et al., 2004 [34]
45
60
Gabon
1996, May
Okware et al., 2002 [33]
224
425
Uganda
2000, October
Nkoghe et al., 2005 [36]
97
124
Gabon
2000, December
Rouquet et al. (2005) [37]
128
143
ROC
2003, December
Boumandouki et al., 2005 [35]
29
35
ROC
2003, Oct–Dec
Onyango et al., 2007 [38]
7
17
South Sudan
2004, April–June
Nkoghe et al., 2011 [41]
10
12
ROC
2005, April–May
Leroy et al., 2009 [39]
186
264
DRC
2007, May and November
Wamala et al., 2010 [3]
39
116
Uganda
2007, August
Grard et al., 2011 [40]
15
32
DRC
2008, Jan
Shoemaker et al., 2012 [7]
1
1
Uganda
2011, May
Albariño et al., 2013 [42]
4
11
Uganda
2012, July
Albariño et al., 2013 [42]
3
6
Uganda
2012, Nov
Albariño et al., 2013 [42]
13
36
DRC
2012, August
Maganga et al., 2014 [43]
49
69
DRC
2014, July
WHO, 2016 [79, 90]
11323
28646
West Africa
March, 2014
DRC Democratic Republic of Congo, ROC Republic of Congo
Table 2 Summary of studies included in a systematic review and meta-analysis describing case fatality rate for Marburg virus from
searched literature globally
Author and Year of Publication
Deaths
Cases
Country
Year & Month of outbreak
Siegert, 1972 [44, 45]
7
31
Germany and Yugoslavia
1967, August
Gear et al., 1975 [91]
1
3
Johannesburg, South Africa
1975, February
Smith et al., 1982 [92]
1
2
Kenya
1980, January
Johnson et al., 1996 [49]
1
1
Kenya
1987, August
Nikiforov et al., 1994 [48]
1
1
Russia
1990
Bausch et al., 2006 [11]
128
154
DRC
1998, October
Towner et al., 2006 [10]
227
252
Angola
2004, October
Adjemian et al., 2011 [51]
1
4
Uganda
2007, June
Centers for Disease & Prevention, 2009 [50]
0
1
USA from Uganda
2008, January
Timen et al., 2009 [93]
1
1
Netherlands from Uganda
2008, July
Albarino et al., 2013 [42, 94]
4
15
Uganda
2012, October
WHO, 2015 [95]
1
1
Uganda
2014, October
DRC Democratic Republic of Congo
Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
Page 5 of 14
Table 3 Summary of studies included in a systematic review and meta-analysis describing sero-prevalence of Ebola virus from literature
Author and Year of Publication
Sample size
Seropositive
Country
Van der Groen and Pattyn 1979 [96]
251
43
DRC
Saluzzo, Gonzalez et al. 1980 [97]
499
17
CAR
Bouree & Bergmann, 1983 [55]
1517
147
Cameroon
Johnson et al., 1983 [56]
741
8
Kenya
Van der Waals, Pomeroy et al. 1986 [57]
225
30
Liberia
Meunier et al., 1987 [58]
1528
319
CAR
Paix et al., 1988 [59]
375
4
Cameroon
Tomori, Fabiyi et al. 1988 [60]
1,677
30
Nigeria
Gonzalez et al., 1989 [72]
5070
629
Central Africa
Mathiot, Fontenille et al. 1989 [61]
381
17
Madagascar
Johnson, Gonzalez et al.1993a [63]
427
75
CAR
Johnson, Gonzalez et al. 1993b [64]
4295
914
CAR
Busico et al., 1999 [66]
575
24
DRC
Nakounne, Selekon et al. 2000 [67]
1762
104
CAR
Heffernan et al., 2005 [69]
979
14
Gabon
Allela et al., 2005 [68]
439
64
Gabon
Lahm, Kombila et al. 2007 [70]
1147
14
Gabon
Becquart et al., 2010 [12]
4349
665
DRC
Heymann et al., 1980 [8]
1096
79
DRC
Burke et al., 1978 [28]
984
38
DRC
Baron et al., 1983 [29]
106
23
Sudan
Georges et al., 1999 [31]
441
58
Gabon
Becker, Feldmann et al. 1992 [62]
1288
11
Germany
Gonzalez, Nakoune et al. 2000 [14]
1331
71
CAR
Bertherat, Renaut et al. 1999 [65]
236
24
Gabon
Nkoghe, Padilla et al. 2011 [13]
4349
667
DRC
DRC Democratic Republic of Congo, ROC Republic of Congo, CAR Central African Republic
Table 4 Summary of studies included in a systematic review and meta-analysis describing sero-prevalence of Marburg disease from
published literature
Author and Year of Publication
Sample size
Seropositive
Country
Van der Waals, Pomeroy et al. 1986 [57]
225
3
Liberia
Gonzalez, Josse et al. 1989 [72]
5070
20
Central African countries
Johnson, Ocheng et al. 1983 [71]
1899
8
Kenya
Mathiot, Fontenille et al. 1989) [61]
384
0
Madagascar
Becker, Feldmann et al. 1992 [62]
1288
34
Germany
Johnson, Gonzalez et al. 199a [63]
427
5
CAR
Johnson, Gonzalez et al. 1993b [64]
4295
137
CAR
Gonzalez, Nakoune et al. 2000 [14]
1340
33
CAR
Nakounne, Selekon et al. 2000 [67]
1762
35
CAR
Bausch, Borchert et al. 2003 [15]
912
15
DRC
Borchert, Mulangu et al. 2006 [73]
300
0
DRC
DRC Democratic Republic of Congo, CAR Central African Republic
Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
Page 6 of 14
large West African EVD outbreak that affected multiple countries had an even lower CFR at 40% (39–
40%). The pooled ES for Ebola virus seroprevalence
was 8% [5–11%) with substantial between-study variance (I2 = 98.7%) (Fig. 4).
occurred during months of May, June and July and no
outbreaks were reported in the month of February.
Meta-analysis and meta-regression of CFR and
seroprevalence of EVD
The weighted CFR of EVD from 23 outbreaks was 65%
(95% CI: 54–76%) (Fig. 2). There was a substantial
between-study variance indicating heterogeneity in the
overall CFR of EVD, I2 = 97.98%. On stratification by
Ebola virus species, the CFR for Sudan ebolavirus was
53%, Bundibugyo ebolavirus was 34%, whereas that of
Zaire ebolavirus was 75%. From the meta-regression, the
CFR for Zaire ebolavirus was higher compared to other
Ebola species (=0.006, Coefficient = 0.19, 95% CI = 0.063
- 0.588). In sub-analysis analysis by country, the highest
CFR for EVD was observed in Republic of Congo
(89.0%, 84.0–93.0%) whereas the lowest was found in
Uganda (43.0%, 27.0–61.0%) (Fig. 3). However, the
Meta-analysis and meta-regression of CFR and
seroprevalence of MVD
The MVD CFR was lower than that of EVD (61%)
(Fig. 5). There was no significant difference between
CFR of MVD and different variables in the metaregression model (P = 0.637). The pooled seroprevalence
of Marburg virus was lower than that of Ebola virus at
1.2% (0.5–2%) (Fig. 6).
Publication bias
In the funnel plots, asymmetry was evident which gives
rise to suspected publication bias (Fig. 7). Egger’s test
Study
ES (95% CI)
Sudan ebolavirus
WHO International Study Team(1978)
Baron et al(1983)
Okware et al(2002)
Onyango et al(2007)
Shoemaker et al(2012)
Albarino et al(2013)
Albarino et al(2013)
Subtotal (I^2 = 0.00%, p = 0.58)
0.53 (0.47, 0.59)
0.65 (0.48, 0.79)
0.53 (0.48, 0.57)
0.41 (0.22, 0.64)
1.00 (0.21, 1.00)
0.36 (0.15, 0.65)
0.50 (0.19, 0.81)
0.53 (0.49, 0.58)
Zaire ebolavirus
International Commission(1978)
Heymann et al( 1980)
Amblard et al(1997)
Khan et al(1999)
Georges et al(1999)
Milleliri et al( 2004)
Nkoghe et al(2005)
Rouquet et al(2005)
Boumandouki et al(2005)
Nkoghe et al(2011)
Leroy et al(2009)
Grard et al(2011)
Maganga et al(2014)
WHO(2015)
Subtotal (I^2 = 98.75%, p = 0.00)
0.88 (0.84, 0.91)
1.00 (0.21, 1.00)
0.61 (0.47, 0.74)
0.81 (0.76, 0.85)
0.68 (0.50, 0.81)
0.75 (0.63, 0.84)
0.78 (0.70, 0.85)
0.90 (0.83, 0.94)
0.83 (0.67, 0.92)
0.83 (0.55, 0.95)
0.70 (0.65, 0.76)
0.47 (0.31, 0.64)
0.71 (0.59, 0.80)
0.40 (0.39, 0.40)
0.75 (0.58, 0.89)
Bundibugyo ebolavirus
Wamala et al(2010)
Albarino et al(2013)
Subtotal (I^2 = .%, p = .)
0.34 (0.26, 0.43)
0.36 (0.22, 0.52)
0.34 (0.27, 0.42)
Heterogeneity between groups: p = 0.000
Overall (I^2 = 97.98%, p = 0.00);
0.65 (0.54, 0.76)
.25
.5
.75
1
Proportion CFR
Fig. 2 Forest plot showing stratified meta-analysis of CFR of Ebola Virus Disease by virus species estimated by the random effects model
(I2 = Higgins statistic, ES = Effect size, CI = Confidence Interval)
Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
Page 7 of 14
ES (95% CI)
Study
South Sudan
WHO International Study Team(1978)
0.53 (0.47, 0.59)
Baron et al(1983)
0.65 (0.48, 0.79)
Onyango et al(2007)
0.41 (0.22, 0.64)
Subtotal (I^2 = .%, p = .)
0.54 (0.46, 0.63)
DRC
International Commission(1978)
0.88 (0.84, 0.91)
Heymann et al( 1980)
1.00 (0.21, 1.00)
Khan et al(1999)
0.81 (0.76, 0.85)
Leroy et al(2009)
0.70 (0.65, 0.76)
Grard et al(2011)
0.47 (0.31, 0.64)
Albarino et al(2013)
0.36 (0.22, 0.52)
Maganga et al(2014)
0.71 (0.59, 0.80)
Subtotal (I^2 = 92.08%, p = 0.00)
0.72 (0.58, 0.84)
Gabon
Amblard et al(1997)
0.61 (0.47, 0.74)
Georges et al(1999)
0.68 (0.50, 0.81)
Milleliri et al( 2004)
0.75 (0.63, 0.84)
Nkoghe et al(2005)
0.78 (0.70, 0.85)
Subtotal (I^2 = 45.67%, p = 0.14)
0.72 (0.64, 0.80)
Uganda
Okware et al(2002)
0.53 (0.48, 0.57)
Wamala et al(2010)
0.34 (0.26, 0.43)
Shoemaker et al(2012)
1.00 (0.21, 1.00)
Albarino et al(2013)
0.36 (0.15, 0.65)
Albarino et al(2013)
0.50 (0.19, 0.81)
Subtotal (I^2 = 73.55%, p = 0.00)
0.43 (0.27, 0.61)
ROC
Rouquet et al(2005)
0.90 (0.83, 0.94)
Boumandouki et al(2005)
0.83 (0.67, 0.92)
Nkoghe et al(2011)
0.83 (0.55, 0.95)
Subtotal (I^2 = .%, p = .)
0.89 (0.84, 0.93)
West Africa
0.40 (0.39, 0.40)
WHO(2015)
Heterogeneity between groups: p = 0.000
0.65 (0.54, 0.76)
Overall (I^2 = 97.98%, p = 0.00);
.25
.5
.75
1
Proportion CFR
Fig. 3 Forest plot showing stratified meta-analysis of CFR of Ebola virus disease by country estimated by the random effects model (I2 = Higgins
statistic, ES = Effect size, CI = Confidence Interval, DRC = Democratic Republic of Congo, ROC = Republic of Congo)
Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
Page 8 of 14
Study
ES (95% CI)
Van der Groen and Pattyn(1979)
Saluzzo, Gonzalez et al(1980)
Bouree & Bergmann(1983)
Johnson et al(1983)
Van der Waals et al(1986)
Meunier et al( 1987)
Paix et al(1988)
Tomori, Fabiyi et al(1988)
Gonzalez et al(1989)
Mathiot et al(1989)
Johnson et al(1993a)
Johnson et al(1993b)
Busico et al(1999)
Nakounne et al(2000)
Heffernan et al(2005)
Allela et al(2005)
Lahm et al(2007)
Becquart et al(2010)
Heymann et al(1980)
Burke et al(1978)
Baron et al( 1983)
Georges et al(1999)
Becker et al( 1992)
Gonzalez et al(2000)
Bertherat et al(1999)
Nkoghe et al(2011)
Overall (I^2 = 98.85%, p = 0.00)
0.17 (0.13, 0.22)
0.03 (0.02, 0.05)
0.10 (0.08, 0.11)
0.01 (0.01, 0.02)
0.13 (0.10, 0.18)
0.21 (0.19, 0.23)
0.01 (0.00, 0.03)
0.02 (0.01, 0.03)
0.12 (0.12, 0.13)
0.04 (0.03, 0.07)
0.18 (0.14, 0.21)
0.21 (0.20, 0.23)
0.04 (0.03, 0.06)
0.06 (0.05, 0.07)
0.01 (0.01, 0.02)
0.15 (0.12, 0.18)
0.01 (0.01, 0.02)
0.15 (0.14, 0.16)
0.07 (0.06, 0.09)
0.04 (0.03, 0.05)
0.22 (0.15, 0.30)
0.13 (0.10, 0.17)
0.01 (0.00, 0.02)
0.05 (0.04, 0.07)
0.10 (0.07, 0.15)
0.15 (0.14, 0.16)
0.08 (0.05, 0.11)
0
.1
.2
.3
Prevalence
Fig. 4 Forest plot for the meta-analysis of sero-prevalence studies of Ebola virus (I2 = Higgins statistic, ES = Effect size, CI = Confidence Interval)
was significant for studies reporting CFR and seroprevalence of EVD and MVD (P = 0.001, P < 0.001, p = 0.032,
and 0.046 respectively). However, the Begg’s bias test
was not significant for studies reporting CFR of EVD
and MVD (p = 0.091 and p = 0.293 respectively), seroprevalence of MVD (p = 0.95), but was significant for
studies reporting seroprevalence of EVD (p = 0.007).
Discussion
Our findings show that the overall pooled CFR of EVD
of 65% was lower than the previously reported CFR of
90% [83]. This indicates, despite substantial heterogeneity, that more than half of the individuals who contract
EVD are more likely to die. Although this CFR appears
to be high, it is lower than the exaggerated figure of
90%. This high CFR tends to cause fear and panic in the
general public and hence interferes with response mechanisms [84]. The CFR in our study is similar to that reported by Lefebvre et al. [4], who reported a CFR of 65%
in a study done using WHO database on EVD
outbreaks. Although there have been cases of EVD and
MVD with 100% CFR [8, 7], these were isolated single
cases that should not be generalized by scientific community to consider Ebola and Marburg viruses as highly
virulent diseases with CFR of up to 90%. There have
been reports with a higher CFR than our maximum of
76% [28, 35, 37, 41], but these either happened long time
ago [28] where there was little knowledge about the disease or happened in very remote places where health
care delivery systems are not robust.
The high CFR of EVD in Republic of Congo (89%)
compared to Uganda (43%) may be due to partly, differences in health care system and response mechanisms to
outbreaks, but also the severity of the species of Ebola
virus involved. For example, Uganda has developed a
robust surveillance system for detecting these viral
haemorrhagic fevers and epidemic response is started
within hours of a positive diagnosis at a CDC supported
laboratory in the country [85]. The well-established disease surveillance system and organised health care
Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
Page 9 of 14
Study
ES (95% CI)
Siegert(1972)
0.23 (0.11, 0.40)
Gear et al(1975)
0.33 (0.06, 0.79)
Smith et al(1982)
0.50 (0.09, 0.91)
Johnson et al( 1996)
1.00 (0.21, 1.00)
Nikiforov et al(1994)
1.00 (0.21, 1.00)
Bausch et al(2006)
0.83 (0.76, 0.88)
Towner et al( 2006)
0.90 (0.86, 0.93)
Adjemian et al(2011)
0.25 (0.05, 0.70)
CDC(2009)
0.00 (0.00, 0.79)
Timen et al(2009)
1.00 (0.21, 1.00)
Albarino et al( 2013)
0.27 (0.11, 0.52)
WHO(2015)
1.00 (0.21, 1.00)
Overall (I^2 = 88.63%, p = 0.00)
0.61 (0.32, 0.88)
0
.25
.5
.75
1
Proportion CFR
Fig. 5 Forest plot for a meta-analysis of CFR of Marburg virus disease estimated using a random effects model (I2 = Higgins statistic, ES = Effect
size, CI = Confidence Interval)
Fig. 6 Meta-analysis of seroprevalence of Marburg virus estimated using a random effects model (I2 = Higgins statistic, ES = Effect size,
CI = Confidence Interval)
Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
a
c
Page 10 of 14
b
d
Fig. 7 Funnel plots assessing publication bias in studies reporting case fatality rate and seroprevalence of Ebola virus disease and Marburg virus
disease. a Funnel plot of the point estimates of the logit CFR of EVD, b Funnel plot of the point estimates of the logit prevalence of EVD,
c Funnel plot of the point estimates of the logit CFR of MVD, d Funnel plot of the point estimates of the logit prevalence of MVD
delivery in endemic areas might explain the lower CFR for
EVD observed in Uganda. But it is also important to note
that Uganda has been affected by the less pathogenic species of Ebola virus (Sudan ebolavirus and Bundibugyo ebolavirus) as compared to DRC and West African countries
that have experienced Zaire ebolavirus Also, it is important to look at the denominators and numerators when
interpreting the CFR. In this analysis, we see that CFR of
EVD in a large outbreak in West Africa that affected multiple countries is at CFR of 40% using WHO data, but this
alone would be misleading if the real numbers of deaths
and cases were not looked at. As of 30th March 2016,
there were 11323 deaths and 28646 cases due to EVD
from all countries affected by that outbreak.
Another significant finding of our study was the variation in the severity and CFR among the pathogenic species of Ebola virus. Zaire ebolavirus (CFR, 75%) was
found to be the most severe followed by Sudan ebolavirus (CFR, 53%), while Bundibugyo ebolavirius (CFR,
34%) was the least severe species. This finding is
supported by McCormick et al., who described differences in severity and filovirus dynamics [86, 87]. The
reasons for severity of Zaire ebolavirus are unclear, thus
there is a need for further research to determine whether
genetic differences are responsible for the variation in
pathogenesis of these species. There was also heterogeneity within Zaire ebolavirus outbreaks (P < 0.001) meaning that these outbreaks, although caused by the same
species are not always similar. The heterogeneity could
further be explained by differences in outbreak investigation designs or approaches, location of the outbreak and
data collection methods. This is further supported by
the strains that have been found within Ebola Zaire species [40]. There was less heterogeneity in outbreak reports
for Bundibugyo ebolavirus and Sudan ebolavirus probably
due to few outbreaks that have been caused by these species. However, the meta-regression did not show any influence on CFR of EVD by country of outbreak (p = 0.249).
This is probably due to low power given the few number
of outbreaks that we have had globally.
Nyakarahuka et al. BMC Infectious Diseases (2016) 16:708
With the Metaprop command for meta-analysis of
marginal proportions [22], it was possible to estimate
the 95% confidence intervals for MVD as 61% (32–88%).
The CI was very wide because of the few outbreaks and
the number of cases involved in MVD outbreaks as
compared to EVD outbreaks. Dropping studies with
100% or 0% CFR for MVD, the CFR reduced from 61 to
53%. With few outbreaks of Marburg virus in different
countries, there is a high variation that would impact the
estimation of CFR for MVD, but this was not significant
from the meta-regression (p = 0.913).
We found that apparently healthy individuals in central
African countries, that are endemic for viral haemorrhagic
fevers, had a 5 and 1% chance of having antibodies against
Ebola and Marburg viruses, respectively. This finding suggests that some individuals who get infected with filoviruses make a full recovery without severe complications
and being documented by healthcare systems. Although
the sero-prevalence is low, it is important that these seropositive individuals are detected early enough because of
greater mortality and socio-economic implications associated with these infections. Because serological tests have
been reported to have low specificity and there is a lot of
cross-reactivity of filoviruses with other viral haemorrhagic fevers [88], this finding should be interpreted with
caution. It is important that specific and more accurate
tests are developed to accurately measure antibody response against filoviruses and progress in this direction
has been made due to the recently approved rapid diagnostic test for Ebola virus by WHO [89].
The limitation of our ES estimates was the heterogeneity that was observed between studies. Efforts to identify sources of heterogeneity were made, and many
unmeasured factors could have influenced CFR during
outbreaks. These reports had data that were collected
using different methods and hence combining them to
produce one effect was likely to produce high heterogeneity. Sensitivity analysis by dropping single cases with
100% mortality did not have substantial impact on the
result. Funnel plots and Beggs tests suggested that publication bias might have been present, meaning that studies
with negative results about Ebola and Marburg viruses are
less likely to be published hence affecting the estimate of
seroprevalence and CFR for EVD and MVD.
The fact that laboratory tests for Ebola and Marburg
viruses are expensive, used only in specific laboratories
and that serological tests are not specific might influence
the publication of studies done with these tests.
Conclusions
The CFR for Ebola and Marburg viruses is still moderately high but not as high as has been reported in
the media and other publications. The CFR of EVD
and MVD is higher in countries with poor disease
Page 11 of 14
surveillance systems. This calls for an improved surveillance system that will enhance early detection and
response to these filovirus outbreaks to avoid a pandemic. The presence of seropositive individuals in apparently health populations indicate that cases go
undetected by the health care system in affected
countries; further calling for robust surveillance for
Ebola and Marburg viruses.
Abbreviations
CDC: Centres for disease control and prevention USA; CFR: Case fatality rate;
CI: Confidence interval; DRC: Democratic Republic of Congo; EVD: Ebola virus
disease; MVD: Marburg virus disease; ROC: Republic of Congo; WHO: World
Health Organization
Acknowledgements
We thank Doreen Busingye from Monash University Australia and Doreen
Asiimwe Buhwa from Makerere Univesity Kampala for final edits and
formatting of this paper.
Funding
We are grateful for funding from Norwegian Agency for Development
Cooperation through the Norwegian Program for Capacity Building in
Higher Education and Research for Development project of Capacity
Building in Zoonotic Diseases Management using integrated approach to
Ecosystems health at the human-livestock–wildlife interface in Eastern and
Southern Africa.
Availability of data and materials
The dataset supporting the findings in this meta-analysis is included in the
article from Tables 1, 2, 3 and 4.
Authors’ contributions
Conceived and designed the protocol: LN, ES, CK. Execution of search
strategy and sifting: LN, JL, BM, MF, RK. Manuscript preparation: LN, CK, RK,
BM, MF, JL and ES. All authors read and approved the final manuscript.
Authors’ information
LN is an Epidemiologist with a background in Veterinary Medicine. He
has been working as a zoonotic disease Epidemiologist especially
focussing of Ebola and Marburg virus outbreaks in Uganda for the last
five years. He is currently pursuing a PhD in Epidemiology of Ebola and
Marburg viruses in Uganda at the Norwegian University of Life Sciences,
Oslo Norway.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
Author details
1
Norwegian University of Life Sciences, Oslo, Norway. 2Makerere University,
Kampala, Uganda. 3Norwegian Medicines Agency, Oslo, Norway. 4Ulm
University, Ulm, Germany. 5Uganda Virus Research Institute, Entebbe, Uganda.
Received: 9 June 2016 Accepted: 17 November 2016
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