Ebola virus disease commonly causes symptoms such as high fever, vomiting, and diarrhoea. It may also cause muscle pain, headaches, and bleeding, and often leads to death.

There have been seven outbreaks of Ebola virus disease in the Democratic Republic of the Congo (DRC) since 1976. The DRC is the country that has had the most outbreaks of this disease in the world. The most recent outbreak in the DRC was in 2014; this was separate from the outbreak that started in West Africa in the same year. Rosello, Mossoko et al. have now compiled the data from all seven of the outbreaks in the DRC into a single dataset, which covers almost 1000 patients.

Analysing this data revealed that people between 25 and 64 years of age were most likely to be infected by the Ebola virus, possibly because most healthcare workers fall into this category. Age also affected how likely a patient was to die, with those aged under 5 and over 15 more likely to die than those aged between 5 and 15. Delaying going to hospital once symptoms had started, even by one day, also increased the likelihood of death.

Rosello, Mossoko et al. also examined the Ebola virus effective reproduction number, which indicates how many people, on average, an infected person passes the virus on to. Outbreaks that initially featured viruses with a reproduction number larger than three tended to be stemmed quickly. However, when the reproduction number was lower, national and international organisations were slower to respond to the signs of the outbreak, leading to outbreaks that lasted longer.

Further research is needed to understand why the likelihood of death is different for different age groups and to investigate the effect of the different routes of transmission of the virus on interventions such as vaccination.

Ebola virus disease (EVD) outbreaks are rare and knowledge of the transmission and clinical features of this disease is sparse. As of May 2015, the devastating outbreak in West Africa has resulted in more than ten times the number of cases reported in all previous outbreaks and will ultimately provide improved insights into EVD. Here, for the first time, all the databases from EVD outbreaks in the Democratic Republic of the Congo (DRC) have been cleaned and compiled into one anonymised individual-level dataset (See Supplementary file 1). The data provided are an invaluable addition to the West Africa data and will allow a more complete picture of the disease. The DRC is the country that has experienced the most outbreaks of EVD. Since the virus' discovery in 1976, there have been six major outbreaks (Yambuku 1976, Kikwit 1995, Mweka 2007, Mweka 2008/2009, Isiro 2012, and Boende 2014) and one minor outbreak (Tandala 1977) reported in the DRC, four in the northern Equateur and Orientale provinces and three in the southern provinces of Bandundu and Kasai-Occidental (Figure 1). Some of these have been described in the literature (World Health Organization, 1978; Heymann et al., 1980; Khan et al., 1999; Muyembe-Tamfum et al., 1999, 2012; Maganga et al., 2014). However, the individual-level data and corresponding lessons from these outbreaks have not been collated or made publicly available; by doing so, we aim to permit a more powerful statistical analysis and a fuller understanding of the disease. The end of the most recent outbreak in the DRC was declared on the 21st of November 2014. This provides an unparalleled opportunity to assemble all the information gathered about EVD in the DRC through almost four decades, learn from the Congolese experience with this disease, and compare the features of EVD in DRC with the epidemic that has had such a devastating effect in West Africa.

Figure 1

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During the last 38 years, 1052 cases of EVD have been reported in the DRC, of which 996 are reported in this dataset. The geographical context, historical timeline, and main characteristics of these outbreaks are depicted in Figure 1 and Table 1. A detailed account of the outbreaks can be found in Appendix 1, Section B. The early accounts of all outbreaks except for Mweka 2007 involved a healthcare facility. The direct epidemiological link between index cases (when known) and animal reservoirs has not been found for any of the outbreaks. The lack of systematic surveillance together with the presence of diseases with similar symptoms allows EVD cases to go unnoticed for long periods of time. A repository of the interventions that led to the control of the outbreaks is outlined in Supplementary file 2. Table 2 summarises the number of cases and deaths reported in each outbreak.

Case demographics

The number of cases and case-fatality ratios (CFRs) varied greatly between outbreaks (Table 2). It can also be observed that laboratory confirmation became more readily available over time. Across all outbreaks, 57% of cases were female (95% CI = 53.9–60.1). In the second Mweka outbreak and in the Isiro outbreak, more than 70% of cases were females. However, in the other outbreaks, the percentage of females was lower (53–59%). When comparing the probable and confirmed cases by age with the overall DRC population (Figure 2), we observed a high concentration of cases in the 25–64 age category compared to the baseline population. This might be because at this age individuals are more likely to be carers. The occupation was only recorded during three outbreaks: Kikwit, Boende and Isiro. During Kikwit, 23% (73/317) of cases were known healthcare workers (HCWs) and 0.6% (2/317) were possible HCWs. During Boende, the occupation was recorded for 85% (58/68) of cases. 14% (8) were known HCWs and 3% (2) were possible HCWs. During Isiro, occupation was reported for 94% (49/52) of cases. 27% (13) were HCW. Although occupation was not recorded on an individual level, during Yambuku, 13 of the 17 Yambuku Hospital workers contracted EVD (World Health Organization, 1978).

Figure 2

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Epidemic curves

The epidemic curves were plotted for the six major outbreaks (Figure 3). The date of infection was based on symptom onset when available (701/995). When it was not, hospitalisation dates were used (5/995). In cases where these were also absent (281/995), the notification dates were used as proxy. For Mweka 2007, the date of infection was mostly based on the notification date (98%), whereas in the other outbreaks, infection dates refer to onset of symptoms almost exclusively (>90%). In time, case definitions became more specific. With the exception of Kikwit, in which notification and the closure of healthcare facilities coincided closely in time, outbreaks seemed to peak before major interventions were initiated.

Figure 3

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Symptoms

The proportion of probable and confirmed cases reporting EVD symptoms is shown in Figure 4. Overall, the most commonly reported symptom was fever, which was reported by 95% of cases (95% CI = 92.6–97.3%) and at least 90% of cases in every outbreak. Reports of vomiting were also similarly common across all major outbreaks, reported by 75% of cases (95% CI = 69.3–79.2) and between 57% and 76% of cases for all major outbreaks. There was considerable variation in how frequently the remaining symptoms were reported for different outbreaks. In particular, hemorrhagic symptoms were present in 61% (95% CI = 51–71) of cases during Kikwit but only 10% (95% CI = 5–18) during Mweka 2007. The Bundibugyo ebolavirus (Isiro outbreak) did not present a symptom profile that was particularly different from that seen for the Zaire ebolavirus (all other outbreaks). However, this was difficult to conclude given the large variation between outbreaks.

Figure 4

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CFRs

The mean CFR overall was 79% (95% CI = 76.4–81.6), but there were significant differences between epidemics and within epidemics over time (Figure 5 and Figure 5—figure supplement 1). The highest average CFR was seen during the first outbreak in Yambuku (mean = 96%, 95% CI = 92.6–97.9 in our subset of 262/318 cases). Kikwit, Mweka 2007, and Boende had high average CFRs ranging from 74% to 78%. During the Isiro and Mweka 2008 outbreaks, the CFR was lower, at 54 and 44% (95% CI = 39.5–67.8 and 26.4–62.3), respectively.

Figure 5 with 2 supplements see all

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All EVD patients under 2 years of age died (N = 29, Figure 5—figure supplement 1). CFRs generally decreased during childhood and then increased again to plateau at around 70–80% in adulthood (Figure 5—figure supplement 2). This pattern was less readily observed for the CFRs in the Yambuku outbreak, which remained high and similar for all ages.

In the regression model that included the delay between symptom onset and hospitalisation as a factor but excluded three outbreaks for missing data (Table 3), the baseline CFR in individuals over 15 years of age during the first month of an EVD outbreak who were admitted to hospital after 0.3 days (the average time from symptom onset to admission to hospital) during the Boende outbreak was 74% (95% CI = 17.8–99.3). The CFR was similar during the Isiro outbreak but was significantly higher during the Kikwit outbreak (94%). The CFR in 0–5 year olds was 76%, and in 5–15 year olds, it was significantly lower at 36%. The odds of dying declined on average by 31% (95% CI = 3.1–52.0%) each month after the start of an outbreak and increased by 11% (95% CI = 1.8–20.7%) per day that a symptomatic person is not hospitalised (Table 3).

Table 3

	OR	2.50%	97.50%
Outbreak Isiro 2012	0.89	0.31	2.59
Outbreak Kikwit 1995	5.44	1.43	21.87
Age [0, 5)	1.12	0.23	6.63
Age [5, 15)	0.2	0.05	0.7
Months since first case	0.69	0.48	0.97
Delay onset to hospitalisation	1.11	1.02	1.21

1. EVD, Ebola virus disease.

2. Estimated through binomial regression with age group and year of outbreak as factorial covariates and the number of months since the start of the outbreak and the delay from symptom onset to hospitalisation as continuous covariates.

In the regression model that included all major outbreaks, the CFR for individuals over 15 years of age during the first month of the outbreak during the Boende outbreak was estimated at 79% (95% CI = 25.8–99.5). The Yambuku, Kikwit, and Mweka 2007 outbreaks had significantly higher CFRs (96%, 94% and 93%) and the Mweka 2008 outbreak had a significantly lower CFR (48%). 0–5 year olds had significantly higher CFRs (90%) than those over 15 years of age. For the 5–15 year olds, the CFR was significantly lower (57%). The odds of dying declined on average by 35% (95% CI = 22.6–45.9) each month after the start of each outbreak (Table 4).

Table 4

Covariates	OR	2.50%	97.50%
Outbreak Isiro 2012	0.67	0.29	1.55
Outbreak Kikwit 1995	4.63	1.96	10.94
Outbreak Mweka 2007	3.83	1.61	9.18
Outbreak Mweka 2008	0.25	0.1	0.63
Outbreak Yambuku 1976	7.11	3.13	16.75
Age [0, 5)	2.49	1.12	6.34
Age [5, 15)	0.36	0.21	0.63
Months since first case	0.65	0.54	0.77

1. EVD, Ebola virus disease.

2. Estimated through binomial regression with age group and year of outbreak as factorial covariates and the number of months since the start of the outbreak as continuous covariate.

Reproduction numbers through time

Changes in the effective reproduction number, R, over the course of the outbreaks were plotted in Figure 6. In Yambuku, Mweka 2008, and Boende 2014, R dropped below one within 3–5 weeks after the initial case and the outbreak was rapidly brought under control. In these settings, the spread of EVD during the first 2 weeks had been high (R > 3). By contrast, in Kikwit 1995, Mweka 2007, and Isiro 2012, where the initial transmission rate was lower, spread of EVD was sustained for more than 13 weeks. Overall, we can see that R declines before the major interventions occurred, which could point to behavioural changes that occurred spontaneously in the populations.

Figure 6

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Delays in case detection

The delay distributions from onset of symptoms to notification, from onset of symptoms to hospitalisation, from onset of symptoms to death, length of hospital stay, and from hospitalisation to death were plotted for each outbreak (when available) in Figure 7. The largest delays between symptom onset to notification and to hospitalisation were seen during the Kikwit outbreak (12.9 days and 5.0 days, respectively). The largest delay between symptom onset and death and the longest duration of hospitalisation were seen during the Isiro outbreak (11.4 and 8.0 days, respectively). However, this was only recorded for the Kikwit, Mweka 2008, and Isiro outbreaks. The longest delay between hospitalisation and death was observed during the Mweka 2008 outbreak (11.0 days) (Table 5).

Figure 7

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Table 5

Outbreak	Delay	Mean	SD
1995 Kikwit	Onset to notification	12.91	7.72
1995 Kikwit	Onset to hospitalisation	5.02	3.91
1995 Kikwit	Onset to death	9.47	4.44
1995 Kikwit	Length of hospital stay	5.72	5.67
1995 Kikwit	Hospitalisation to death	4.5	3.69
2007 Mweka	Onset to notification	0	0
2008 Mweka	Onset to notification	10.04	8.47
2008 Mweka	Onset to hospitalisation	0	0
2008 Mweka	Onset to death	7.62	4.44
2008 Mweka	Length of hospital stay	1.5	2.12
2008 Mweka	Hospitalisation to death	11	9.85
2012 Isiro	Onset to notification	8.83	8.29
2012 Isiro	Onset to hospitalisation	4	3.27
2012 Isiro	Onset to death	11.37	5.41
2012 Isiro	Length of hospital stay	8	6.56
2012 Isiro	Hospitalisation to death	7.59	5.52
2014 Boende	Onset to notification	6.23	5.08
2014 Boende	Onset to hospitalisation	4.95	3.32
2014 Boende	Onset to death	9.39	5.67
2014 Boende	Hospitalisation to death	4.86	4.23

1. Delays distributions (delay from onset of symptoms to notification, onset of symptoms to hospitalisation, onset of symptoms to death, length of hospitalisation and date of hospitalisation to death).

This article provides for the first time a description and a line list for all outbreaks that have occurred in the DRC. This represents almost 40 years of surveillance data, seven outbreaks, and 996 suspected, probable, or confirmed cases. It is an invaluable resource for studying the epidemiology and clinical features of EVD. We highlight the importance of reducing the delay between symptom onset and hospitalisation, as the odds of dying increase by 11% per day that a patient is not hospitalised. We also observe higher incidence in those between 25 and 64 years of age and a higher CFR in patients under 5 or over 15 years of age than in those between 5 and 15 years old. These trends mirror those observed during the West African outbreak, where cumulative incidence was highest in those between 16 and 44 years of age and CFR progressively dropped from 89.5% in those under 1 year of age to 52.1% in those between 10 and 15 years, to rise again to 78.7% in those over 45 years old (WHO Ebola Response Team et al., 2015a). These distinctions could inform the choice of target age groups for interventions such as vaccination.

Another important finding is that during outbreaks with an initially lower reproduction number, R, (≤3) national and international response was slower, outbreaks took longer to control, and (with the exception of Yambuku, where the virus was first discovered) were larger outbreaks than those with initially high R. This occurred during the current outbreak in West Africa, where the basic reproduction numbers for Guinea, Sierra Leone, and Liberia have been estimated at 1.51, 2.53, and 1.59, respectively, and indicates the need for any future EVD to be met with rapid national and international response (Althaus, 2014).

Our estimates largely coincide with those recently reviewed in the literature (Van Kerkhove et al., 2015). The basic reproduction numbers reported for the Kikwit outbreak (3.00) is comprised in the range found by other studies (1.36–3.65) (Chowell et al., 2004; Ferrari et al., 2005; Lekone and Finkenstädt, 2006; Legrand et al., 2007; Forsberg White and Pagano, 2008; Ndanguza et al., 2011), and our estimate for the Yambuku outbreak (5.00) is similar to that reported by Camacho et al. (4.71, range = 3.92–5.66) (Camacho et al., 2014). The mean delay of onset of symptoms to hospitalisation and to death estimated here for Kikwit (5.0 and 9.5, respectively) was also similar to that found by other authors (4–5 [Khan et al., 1999; Rowe et al., 1999] and 9.6–10.1 [Bwaka et al., 1999; Khan et al., 1999]). Our estimated mean delay of onset of symptoms to death during the Boende outbreak (9.4) was slightly lower than that found by other authors (11.3) but included in their reported range (1–30) (Maganga et al., 2014). The delay between hospitalisation and death during the Kikwit outbreak found in the literature (4.6) coincided with our estimate (4.5) (Khan et al., 1999). In addition, our estimates of the overall CFR for Kikwit and Boende (78% and 74%, respectively) coincided with other estimates reported in the literature (74–81% [Muyembe and Kipasa, 1995; Khan et al., 1999; Ndambi et al., 1999; Sadek et al., 1999; Chowell et al., 2004] and 74% [Maganga et al., 2014], respectively). The remaining outbreak estimates have not been studied by other authors and are reported here for the first time.

Overall, CFRs and delays between symptom onset and hospitalisation, symptom onset and death, and hospitalisation and death reported in our study do not differ substantially with those reported for the current outbreak (WHO Ebola Response Team et al., 2015b). The data presented were originally collected for the containment of the outbreaks rather than for providing the basis of an epidemiological study of the disease. As such, variables are not recorded consistently across all outbreaks and there are missing data. This dataset does not take into consideration undetected cases. A surveillance study carried out in northwestern DRC between 1981 and 1985, through clinical records and serological testing, detected 21 cases likely to be EVD, suggesting that sporadic cases do occur (Jezek et al., 1999). Another serosurvey carried out in Yambuku after the outbreak suggested that that 17% of the population in the village was infected asymptomatically (Breman et al., 1978). Under-reporting may differ between and during outbreaks and may impact the calculated estimates such as CFRs, which limits the validity of direct comparisons of values between outbreaks. Other limitations include the different case definitions employed in different outbreaks and that the method used to calculate the effective reproduction numbers is susceptible to changes in reporting during the outbreak (as most methods are). However, it is robust if the extent of underreporting remains constant during each outbreak. Moreover, it is robust to different reporting sensitivity between outbreaks.

The regular re-emergence of EVD in human hosts is likely to be connected to the presence of the virus in animal reservoirs, such as bats and monkeys (Leroy et al., 2009; Muyembe-Tamfum et al., 2012). The presence of vast tropical rainforests covering entire regions of the DRC and the strong link existing between local economies and the forest makes a re-emergence of the virus in the country in the near future very likely (Pigott et al., 2014). Although the Mweka 2007 outbreak has been linked to the consumption of fruit bats that migrate to the area (Leroy et al., 2000), the epidemiological link between index cases (when known) and animal reservoirs has not been found for any of these outbreaks.

All outbreaks except for the 2007 Mweka outbreak have involved hospital transmission during the early part of the outbreak, illustrating the amplifying effect that poor infection control can have on EVD epidemics. A study of the 1976 outbreak has highlighted the importance of community infection to transmission (Camacho et al., 2014). Traditional burials are an important mechanism of transmission of EVD. Funeral data can help inform mathematical models that explore the importance of this route of transmission and can help guide resource allocation. This will be explored in subsequent analysis.

Mweka 2008 was the shortest and smallest outbreak with the lowest CFR. This could be due to the short delay between the first notification and the opening of the isolation centre (10 days). The low CFR during Isiro could be due to infection by a less virulent type of virus (B. ebolavirus) and is in line with what has been reported for this virus in other outbreaks (Van Kerkhove et al., 2015).

In most outbreaks, major interventions arrived when the reproduction number, R, was less than one and the epidemic was already under control. This suggests an important role of other factors, such as changes in contact behaviour, in shaping the changes of R. For example, there is evidence that an increase in the proportion of patients admitted to hospital was associated with a reduction in the size of EVD transmission chains in Guinea in 2014 (Faye et al., 2015) and the community acceptance of EVD control measures in West Africa improved dramatically over the course of the epidemic, which led to better infection control (Dhillon and Kelly, 2015).

The Boende outbreak began whilst the West African outbreak was gaining international importance. This much smaller outbreak, with an initial R of five, which consisted of 68 cases, lasted only 10 weeks. The more remote setting, a background antibody presence in the area and a greater preparedness to EVD (that led to its notification 3 weeks after the first case and the opening of the first isolation centre a month later) could have contributed to the avoidance of a larger outbreak (Heymann et al., 1980; Busico et al., 1999; Maganga et al., 2014).

The high number of EVD cases between 25 and 64 years of age compared to the background demographics, the high CFR in children under five, the decrease in CFRs in those 5 to 15, and the subsequent increase in CFR during adulthood are phenomena that warrant further investigation. The variation in symptoms reported during different outbreaks is also a matter for further research.

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Author details

1. Alicia Rosello

   1. Public Health England, London, United Kingdom
   2. University College London, London, United Kingdom

   Contribution

   AR, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Mathias Mossoko

   For correspondence

   alicia.rosello.13@ucl.ac.uk

   Competing interests

   The authors declare that no competing interests exist.

2. Mathias Mossoko

   Direction de lutte contre la maladie, Kinshasa, Democratic Republic of the Congo

   Contribution

   MM, Acquisition of data, Drafting or revising the article

   Contributed equally with

   Alicia Rosello

   Competing interests

   The authors declare that no competing interests exist.

3. Stefan Flasche

   London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   SF, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Albert Jan Van Hoek

   London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   AJVH, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Placide Mbala

   Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo

   Contribution

   PM, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Anton Camacho

   London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   AC, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Sebastian Funk

   London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   SF, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Adam Kucharski

   London School of Hygiene and Tropical Medicine, London, United Kingdom

   Contribution

   AK, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Benoit Kebela Ilunga

   Direction de lutte contre la maladie, Kinshasa, Democratic Republic of the Congo

   Contribution

   BKI, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. W John Edmunds

    London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    WJE, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Peter Piot

    London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    PP, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Marc Baguelin

    1. Public Health England, London, United Kingdom
    2. London School of Hygiene and Tropical Medicine, London, United Kingdom

    Contribution

    MB, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    marc.baguelin@lshtm.ac.uk

    Competing interests

    The authors declare that no competing interests exist.

13. Jean-Jacques Muyembe Tamfum

    Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo

    Contribution

    J-JMT, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

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