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  • Research article
  • Open Access
  • Open Peer Review

How severe and prevalent are Ebola and Marburg viruses? A systematic review and meta-analysis of the case fatality rates and seroprevalence

BMC Infectious DiseasesBMC series – open, inclusive and trusted201616:708

https://doi.org/10.1186/s12879-016-2045-6

©  The Author(s). 2016

  • Received: 9 June 2016
  • Accepted: 17 November 2016
  • Published:
Open Peer Review reports

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 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].

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] 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 cross-referenced 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.
Fig. 1
Fig. 1

Flow diagram for search strategy and article selection process from the literature databases

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) and species involved. For population-based sero-prevalence studies, the following additional information was retrieved: i) sample size and ii) number of seropositive samples.

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 (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].

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 sero-prevalence 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.
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

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

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 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 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).
Fig. 2
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)

Fig. 3
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)

Fig. 4
Fig. 4

Forest plot for the meta-analysis of sero-prevalence studies of Ebola virus (I2 = Higgins statistic, ES = Effect size, CI = Confidence Interval)

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 meta-regression 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).
Fig. 5
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
Fig. 6

Meta-analysis of seroprevalence of Marburg virus estimated using a random effects model (I2 = Higgins statistic, ES = Effect size, CI = Confidence Interval)

Publication bias

In the funnel plots, asymmetry was evident which gives rise to suspected publication bias (Fig. 7). Egger’s test 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).
Fig. 7
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

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 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.

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 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

Declarations

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.

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Authors’ Affiliations

(1)
Norwegian University of Life Sciences, Oslo, Norway
(2)
Makerere University, Kampala, Uganda
(3)
Norwegian Medicines Agency, Oslo, Norway
(4)
Ulm University, Ulm, Germany
(5)
Uganda Virus Research Institute, Entebbe, Uganda

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