Incubation Period of Ebola Hemorrhagic Virus Subtype Zaire

Article information

Osong Public Health Res Perspect. 2011;2(1):3-7
Publication date (electronic) : 2011 April 12
doi :
aDepartment of Medical Biometry, University of Tübingen, Tübingen, Germany
bCenters for Disease Control and Prevention, Atlanta, Georgia, USA
Corresponding author.
Received 2011 February 28; Accepted 2011 May 2.



Ebola hemorrhagic fever has killed over 1300 people, mostly in equatorial Africa. There is still uncertainty about the natural reservoir of the virus and about some of the factors involved in disease transmission. Until now, a maximum incubation period of 21 days has been assumed.


We analyzed data collected during the Ebola outbreak (subtype Zaire) in Kikwit, Democratic Republic of the Congo, in 1995 using maximum likelihood inference and assuming a log-normally distributed incubation period.


The mean incubation period was estimated to be 12.7 days (standard deviation 4.31 days), indicating that about 4.1% of patients may have incubation periods longer than 21 days.


If the risk of new cases is to be reduced to 1% then 25 days should be used when investigating the source of an outbreak, when determining the duration of surveillance for contacts, and when declaring the end of an outbreak.

1 Introduction

Ebola hemorrhagic fever, caused by the Ebola virus (EBOV) is a severe and often fatal disease. The first known outbreak occurred in Zaire (now the Democratic Republic of Congo) in 1976. Since then, single cases and large epidemics have recurred in equatorial Africa. Apart from Zaire, Sudan, Gabon, Ivory Coast, Uganda, Kenya, Angola and the Republic of the Congo have all been affected [1]. Until now, more than 1850 cases and about 1300 deaths have been reported [2]; the latest are from an outbreak in Uganda. Marburg virus, a close relative of EBOV [3], also causes severe outbreaks of hemorrhagic fever in Africa [4]. There are four known subtypes of EBOV named after the place of their first appearance: Zaire, Sudan, Ivory Coast, and Reston in Virginia, USA. The former three cause severe forms of the disease in humans. Subtype Reston is believed not to be dangerous in humans, but it can be fatal in non-human primates [5–7] and pigs [8]. A new strain of EBOV with the proposed name Bundibugyo EBOV was discovered in a Ugandan epidemic in November 2007 [9]. The subtypes have different mortality rates (about 90% for Zaire and 50% for Sudan) and may also have different incubation periods [10,11].

The natural reservoir of the virus remains unknown [12,13]; bats and fruit bats are suspected to be hosts [14,15], and monkeys could be a vector in transmission to humans [16–18]. The two most important routes for person-to-person transmission seem to be direct physical contact and contact with body fluids. It is very likely that droplets or aerosols play little or no role [6,19,20]. When no symptoms are visible, EBOV-infected persons do not seem to be contagious [21,22]. The incubation period is generally assumed to range from 2 to 21 days [10,23–25]; for EBOV most calculations put it at 6 to 10 days [23,26,27]. Knowledge of the incubation period will aid research on EBOV transmission and is fundamental in determining for how long people should be monitored after having had contact with cases. This knowledge is also essential when investigating the animal source that might have triggered an epidemic. In this paper, we investigate the incubation period of EBOV using data collected during an outbreak in Kikwit, Democratic Republic of the Congo, in 1995 [22]. This data set is especially useful because it contains precise information on the contact behavior of 173 persons. We apply an estimation method that was used to study smallpox [28], but has never been used for Ebola hemorrhagic fever.

2 Materials and Methods

2.1 Description of the epidemic

In 1995, when the outbreak took place, Kikwit had about 200,000 inhabitants and only two hospitals that lacked electricity and running water [29]; protective equipment like gloves was in short supply [30]. On 6 January 1995, a forest worker fell ill with hemorrhagic symptoms and died some days later. In mid-April, a nosocomial outbreak began, leading to the spread of the disease within and between families in Kikwit and nearby areas. On 3 May, containment measures were implemented. One week later, EBOV was confirmed by the Centers for Disease Control and Prevention (Atlanta, USA) [31,32]. Having killed 81% of 315 infected people, the epidemic stopped on 16th July 1995 [5,19,33].

2.2 Data set

The data that we used were collected between 17 May and 3 June 1995 [22]. All members of every household from which a primary case had died or was discharged from hospital between 1 January and 7 May 1995 were interviewed. The resultant data set contains information on 27 households with a total of 23 primary cases and 173 contacts persons. It also contains detailed exposure information for each household member. We used this data to classify the contacts into strong contacts (contact with body fluids), weak contacts (direct physical contact but no contact with body fluids) and non-relevant contacts (only non-physical contact).

2.3 Model description and methods

Because the exact time of infection is not known, the duration of the incubation period could only be derived from the duration and intensity of contact with the index case. We were also forced to estimate the rates at which contacts were infected during the different periods of the disease in the index cases. Because the available information was not enough to allow us to estimate separate rates for the case’s stay in the hospital and for the funeral, we used a common rate of infection for both periods. To distinguish between the effects of strong and weak contact and between contact at home and contact in hospital and during the funeral, and to keep the number of parameters to a minimum, we proposed three different contact models (Table 1). In Model 1, the infection rates caused by weak contact are the same at home and in hospital, whereas the infection rates produced by strong contact differ in the two places. In Model 2, the infection rates for weak contact differ at home and in hospital, and the infection rates for strong contact are obtained by adding a constant to the infection rate of the corresponding weak contact. In Model 3, the infection rates produced by strong contact differ in the two settings, and the infection rates produced by weak contact are obtained by multiplying the infection rate of the corresponding strong contact with a constant.

Infection rates λ(t) for different contact types and disease periods for the Ebola outbreak in Kikwit 1995. a, b and c are parameters which are used to construct the infection rate for each day on which a contact between a case and a household member is reported

The incubation period is assumed to be lognormally distributed with mean μ and standard deviation (SD) σ. This type of distribution is frequently used to describe incubation periods of acute infectious diseases [34,35]. Having parameterized the distribution with mean μ and SD σ, the density function of the lognormal density is given by

where x is the duration of the incubation period.

We used maximum likelihood inference to estimate the parameters. This method is similar to the procedure proposed by Eichner and Dietz for smallpox incubation periods [25]. The likelihood contribution of each household member who escaped infection, is

and the likelihood contribution of each household member who developed the disease, is
where t0 is the time when the index case first developed symptoms and t1 is the time when the household contact became ill. λ(t) is the infection rate at time t (which depends on the type of contact and the stage of the disease in the index case; see Table 1). f(t1 − t) is the density of the incubation period (see f(x) above) for the delay t1 − t between the household member becoming infected (unknown time t) and the onset of the disease (time t1). Because of lack of information, we assumed that all household members who contracted the infection were infected by the index case in that household.

3 Results

Of the three models examined, the best was Model 1 with c = 0 (Table 1). The results of the maximum likelihood estimates and the supported ranges are given in Table 2.

Maximum likelihood estimates and supported ranges for mean μ and standard deviation σ of the incubation period and for transmission possibilities of Ebola hemorrhagic fever in Kikwit in 1995

The mean incubation period μ was estimated to be 12.7 days with a SD σ of 4.31 days. The force of infection a for all types of contact during hospitalization and the funeral, and for weak contact at home, was 0.0254 per day. This indicates that the probability of escaping infection for one week was 0.84 in spite of having weak contact with the index case at home or at the hospital. The force of infection for strong contacts at home is a + b = 0.161 per day, indicating that the probability of escaping infection for 1 week is, in this case, only 0.32.

4 Discussion

In this study, the mean incubation period for EBOV hemorrhagic fever was estimated to be 12.7 days (SD 4.31 days). Earlier estimates for EBOV mostly indicated shorter incubation periods ranging from 6 days to 10 days [23,26,27]. Bwaka et al reported a longer incubation period for human-to-human transmission compared to the incubation period for infection caused by a needle prick [26]. In our study, infections were transmitted directly and not by needle prick [36], which may partly explain the longer incubation period estimate. A similar longer mean incubation period of 11.7 days was detected in EBOV infected gorillas [37]. For the Ebola-Sudan strain epidemic in Uganda in 2001, the biggest Ebola epidemic ever reported, the mean incubation period was calculated to range from 6.3 days to 12 days [38]. Bwaka et al and Breman et al estimated a mean incubation period for EBOV of 6.2 and 6.3 days, respectively, but both estimates were based on small data sets [23,26]. Lekone et al calculated a mean incubation period of 10.1 days for the same Kikwit 1995 epidemic [27] for which our data were collected; however, their estimate is not based on the same data set as ours and the authors assumed an exponential distribution for the incubation period. If our parameter estimations were also based on an exponentially distributed incubation period instead of a lognormal one, the estimated mean incubation period would drop to 12.0 days. A more empirical computation that distinguished between a minimum and a maximum incubation period was carried out by Dowell et al [22]. The minimum incubation period was calculated from the death of the index case to the onset of fever in the secondary case. The mean came out to be 7 days, and the range was from 1 day to 15 days. The maximum incubation period was calculated from the onset of fever in the index case to the onset of fever in the secondary case and the mean came out to be 17 days with the range going from 9 days to 25 days. Because infections occur between the onset of fever and the day of death, the mean incubation period should lie between 7 and 17 days.

Our study had a number of limitations: (i) when estimating the parameters, 31 of the 200 household members had to be excluded from the data set because important contact information was missing (two households with four and 19 members, respectively, that included seven secondary cases) or because the possibility that tertiary cases had occurred could not be ruled out (one household with eight members that included three secondary cases); (ii) because we had to estimate the time of infection, there may be more variability in our estimate than if the time of infection had been known precisely; and (iii) different strains of the EBOV behave differently and the number of cases exposed via needles and via direct contact varies in different epidemics, making it difficult to extrapolate from historical data to future outbreaks.

However, our results suggest that a longer incubation period than previously assumed should be taken into consideration when exploring Ebola transmission possibilities and when searching for the original source of the infection that triggered an epidemic. In the past, EBOV transmission occurred when hunters [39] or scientists [40] came into contact with monkeys that were retrospectively assumed to be infected with EBOV. EBOV was propagated in human populations when needles and syringes were reused [19] or when health care workers or family members cared for Ebola cases [22,41]. Important tools in Ebola hemorrhagic fever containment are the surveillance of persons who have been in contact with Ebola-infected patients and the monitoring of convalescent patients before and after they have been discharged from hospital. The length of time these two measures should be carried out and the announcement of the end of an epidemic depend on the length of the incubation period. For all three scenarios that we modeled, a maximum incubation period of 21 days is normally assumed [19,33,36,38,42–44], meaning that in practice surveillance usually stops three weeks after the last contact. Epidemiologists have suggested that two consecutive incubation periods (a total of 42 days) must elapse before an outbreak is declared to be controlled [45,46]. By combining a rigorously collected data set with a new approach for calculating infectious disease incubation periods, our results suggest that the standard assumption of a maximum incubation period of 21 days may be incorrect. Our density function supports durations longer than 21 days. The risk of developing the disease after the 21st day is 4.1 %. To reduce the risk to 1%, 25 days should be taken as the incubation period. A surveillance duration of this length could be an acceptable compromise because: (i) the direct costs (surveillance) and indirect costs (economic cost of quarantines and lost livelihoods) for each day of observation are relatively small; (ii) there is minimal risk of further cases after the 25th day, and (iii) vigilance for Ebola infections by the population and the public health system should be encouraged so that new cases are detected early enough.


1. Feldmann H., Geisbert T.W.. Ebola haemorrhagic fever. Lancet 377(9768)2011 Mar 5;:849–862. 15 Nov 2010 [Epub ahead of print]. 21084112.
2. WHO. Ebola haemorrhagic fever — fact sheet revised in May 2004 [monograph on the Internet]. Available at: 2004. World Health Organization. Geneva: [Date accessed: 17 December 2008].
3. Chlibek R., Smetana J., Vackova M.. Ebola and Marburg fever — outbreaks of viral haemorrhagic fever. Klin Mikrobiol Infekc Lek 12(6)2006 Dec;:217–223. 17230375.
4. Marburg hemorrhagic fever — Uganda (03). August 10, 2007. Available at: archive 20070810.2609 [Date accessed: 08 December 2008].
5. CDC. Ebola Hemorrhagic Fever information packet [monograph on the Internet]. Available at: 2002. CDC, Special Pathogens Branch — Division of Viral and Rickettsial Diseases. Atlanta: [cited 2008 Dec 8, Date accessed: 08 December 2008].
6. Klenk H.D., Feldmann H.. Ebola and Marburg viruses: molecular and cellular biology. 2004. Horizon Bioscience. Norfolk, UK:
7. McCormick J.B.. Ebola virus ecology. J Infect Dis 190(11)2004 Dec 1;:1893–1894. 15529250.
8. Normile D.. Emerging infectious diseases. Scientists puzzle over Ebola-Reston virus in pigs. Science 233(5913)2009 Jan 23;:451. 19164717.
9. Towner J.S., Sealy T.K., Khristova M.L.. Newly discovered ebola virus associated with hemorrhagic fever outbreak in Uganda. PLoS Pathog 4(11)2008 Nov;:e1000212. 19023410.
10. MacNeil A., Farnon E.C., Wamala J.. Proportion of deaths and clinical features in Bundibugyo Ebola virus infection, Uganda. Emerg Inf Dis 16(12)2010 Dec;:1969–1972.
11. Chowell G., Hengartner N.W., Castillo-Chavez C.. The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda. J Theor Biol 229(1)2004 Jul 7;:119–126. 15178190.
12. Feldmann H., Wahl-Jensen V., Jones S.M.. Ebola virus ecology: a continuing mystery. Trends Microbiol 12(910)2004 Oct;:433–437. 15381189.
13. Leirs H., Mills J.N., Krebs J.W.. Search for the Ebola virus reservoir in Kikwit, Democratic Republic of the Congo: reflections on a vertebrate collection. J Infect Dis 179(Suppl. 1)1999 Feb;:S155–S163. 9988179.
14. Hayman D.T.S., Emmerich P., Yu M.. Long-term survival of an urban fruit bat seropositive for Ebola and Lagos bat viruses. PLoS One 5(8)2010 Aug 4;:e11978. 20694141.
15. Leroy E.M., Epelboin A., Mondonge V.. Human Ebola outbreak resulting from direct exposure to fruit bats in Luebo, Democratic Republic of Congo. Vector-borne and Zoonotic Diseases 9(6)2009 Dec;:723–728. 19323614.
16. Leroy E.M., Kumulungui B., Pourrut X.. Fruit bats as reservoirs of Ebola virus. Nature 438(7068)2005 Dec 1;:575–576. 16319873.
17. Leroy E.M., Rouquet P., Formenty P.. Multiple Ebola virus transmission events and rapid decline of central African wildlife. Science 303(5656)2004 Jan 15;:387–390. 14726594.
18. Biek R., Walsh P.D., Leroy E.M.. Recent common ancestry of Ebola Zaire virus found in a bat reservoir. PLoS Pathog 2(10)2006 Oct;:e90. 17069458.
19. Khan A.S., Tshioko F.K., Heymann D.L.. The reemergence of Ebola hemorrhagic fever, Democratic Republic of the Congo, 1995. Commission de Lutte contre les Epidemies a Kikwit. J Infect Dis 179(Suppl. 1)1999 Feb;:S76–S86. 9988168.
20. Baron R.C., McCormick J.B., Zubeir O.A.. Ebola virus disease in southern Sudan: hospital dissemination and intrafamilial spread. Bull World Health Organ 61(6)1983;:997–1003. 6370486.
21. Bennett D., Brown D.. Ebola virus. BMJ 310(6991)1995 May;:1344–1345. 7787519.
22. Dowell S.F., Mukunu R., Ksiazek T.G.. Transmission of Ebola hemorrhagic fever: a study of risk factors in family members, Kikwit, Democratic Republic of the Congo, 1995. Commission de Lutte contre les Epidemies a Kikwit. J Infect Dis 179(Suppl. 1)1999 Feb;:S87–S91. 9988169.
23. Breman J.G., Piot P., Johnson K.M.. The epidemiology of Ebola hemorrhagic fever in Zaire, 1976. In : Pattyn S., ed. Ebola Virus Haemorrhagic Fever. Proceedings of the international colloquium on Ebola virus infections and other haemorrhagic fevers, 1977 Dec 6–8, Antwerpen, Belgien 1978. Elsevier/North Holland Biomedical Press. Amsterdam:
24. Geisbert T.W., Jahrling P.B.. Exotic emerging viral diseases: progress and challenges. Nat Med 10(Suppl. 12)2004 Dec;:S110–S121. 15577929.
25. Nyamathi A.M., Fahey J.L., Sands H.. Ebola virus: immune mechanisms of protection and vaccine development. Biol Res Nurs 4(4)2003 Apr;:276–281. 12698920.
26. Bwaka M.A., Bonnet M.J., Calain P.. Ebola hemorrhagic fever in Kikwit, Democratic Republic of the Congo: clinical observations in 103 patients. J Infect Dis 179(Suppl. 1)1999 Feb;:S1–S7. 9988155.
27. Lekone P.E., Finkenstädt B.F.. Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study. Biometrics 62(4)2006 Dec;:1170–1177. 17156292.
28. Eichner M., Dietz K.. Transmission potential of smallpox: estimates based on detailed data from an outbreak. Am J Epidemiol 158(2)2003 Jul 15;:110–117. 12851223.
29. Hewlett B.L., Hewlett B.S.. Providing care and facing death: nursing during Ebola outbreaks in central Africa. J Transcult Nurs 16(40)2005 Oct;:289–297. 16160191.
30. Heymann D.L., Barakamfitiye D., Szczeniowski M.. Ebola hemorrhagic fever: lessons from Kikwit, Democratic Republic of the Congo. J Infect Dis 179(Suppl. 1)1999 Feb;:S283–S286. 9988197.
31. Guimard Y., Bwaka M.A., Colebunders R.. Organization of patient care during the Ebola hemorrhagic fever epidemic in Kikwit, Democratic Republic of the Congo, 1995. J Infect Dis 179(Suppl. 1)1999 Feb;:S268–S273. 9988194.
32. Muyembe-Tamfum J.J., Kipasa M., Kiyungu C.. Ebola outbreak in Kikwit, Democratic Republic of the Congo: discovery and control measures. J Infect Dis 179(Suppl. 1)1999 Feb;:S259–S262. 9988192.
33. IVS. Viral haemorrhagic fevers. Euro surveill 72002;:31–54. 12631941.
34. Horowitz M.L., Cohen N.D., Takai S.. Application of Sartwell’s model (lognormal distribution of incubation periods) to age at onset and age at death of foals with Rhodococcus equi pneumonia as evidence of perinatal infection. J Vet Intern Med 15(93)2001 May–Jun;:171–175. 11380023.
35. Sartwell P.E.. The distribution of incubation periods of infectious disease. Am J Hyg 51(3)1950 May;:310–318. 15413610.
36. Centres for Diseases Control and Prevention. Update: Outbreak of Ebola hemorrhagic fever — Zaire. MMWR Morb Mortal Wkly Rep 44(19)1995 May 19;:381–382. 7739512.
37. Bermejo M., Rodriguez-Teijeiro J.D., Illera G.. Ebola outbreak killed 5000 gorillas. Science 314(5805)2006 Dec 8;:1564. 17158318.
38. Okware S.I., Omaswa F.G., Zaramba S.. An outbreak of Ebola in Uganda. Trop Med Int Health 7(12)2002 Dec;:1068–1075. 12460399.
39. Wolfe N.D., Prosser T.A., Carr J.K.. Exposure to nonhuman primates in rural Cameroon. Emerg Infect Dis 2004 Dec;Available at:–0062.htm. [serial online, cited 2008 Dec 19, Date accessed: 08 December 2008].
40. WHO . WHO recommended Guidelines for Epidemic Preparedness and Response: Ebola Haemorrhagic Fever (EHF) [monograph on the Internet]. Available at: 1997. World Health Organization. Geneva: [cited 2008 Nov 25, Date accessed: 08 December 2008].
41. Sadek R.F., Khan A.S., Stevens G.. Ebola hemorrhagic fever, Democratic Republic of the Congo, 1995: determinants of survival. J Infect Dis 179(Suppl. 1)1999 Feb;:S24–S27. 9988161.
42. Francesconi P., Yoti Z., Declich S.. Ebola hemorrhagic fever transmission and risk factors of contacts, Uganda. Emerg Infect Dis 2003 Nov;Available at:–0339.htm. [serial online, cited 2008 Nov 25, Date accessed: 08 December 2008].
43. Lloyd E.S., Zaki S.R., Rollin P.E.. Long-term disease surveillance in Bandundu region, Democratic Republic of the Congo: a model for early detection and prevention of Ebola hemorrhagic fever. J Infect Dis 179(Suppl. 1)1999 Feb;:S274–S280. 9988195.
44. WHO. Outbreak(s) of Ebola haemorrhagic fever in the Republic of the Congo, January–April 2003. Wkly Epidemiol Rec 782003;:285–296. 14509121.
45. Hopp M.. Ebola Hemorrhagic Fever — Congo DR (11): WHO. ProMed. Available at: October 3, 2007. archive 20071003.3270 [Date accessed: 08 December 2008].
46. Mason C.. The strains of Ebola. CMAJ 178(10)2008 May 6;:1266–1267. 18458253.


This work was supported by the EU project INFTRANS (FP6 STREP; contract no. 513715), by the MODELREL project funded by DG SANCO (no. 2003206-SI 2378802) and by the German Ministry of Health. We thank to Mrs. Mariana Nold for her expert help with data handling and parameter inference.


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Table 1

Infection rates λ(t) for different contact types and disease periods for the Ebola outbreak in Kikwit 1995. a, b and c are parameters which are used to construct the infection rate for each day on which a contact between a case and a household member is reported

At home
During hospitalization and funeral
Weak contacta Strong contactb Weak contacta Strong contactb
Model 1 a a + b a a + c
Model 2 a a + b c c + b
Model 3 a × b b a × c c

Weak contact refers to direct physical contact.


Strong contact additionally involves contact with body fluids. For a detailed explanation, see the text.

Table 2

Maximum likelihood estimates and supported ranges for mean μ and standard deviation σ of the incubation period and for transmission possibilities of Ebola hemorrhagic fever in Kikwit in 1995

Parameter MLE Lower supported range Upper supported range
μ 12.7 d 10.1 d 16.1 d
σ 4.31 d 2.56 d 8.60 d
a 0.0254/d 0.0118/d 0.0466/d
b 0.136/d 0.00550/d 0.370/d