Transmission dynamics and control of Ebola virus disease (EVD): a review
© Chowell and Nishiura; licensee BioMed Central Ltd. 2014
Received: 3 September 2014
Accepted: 30 September 2014
Published: 10 October 2014
The complex and unprecedented Ebola epidemic ongoing in West Africa has highlighted the need to review the epidemiological characteristics of Ebola Virus Disease (EVD) as well as our current understanding of the transmission dynamics and the effect of control interventions against Ebola transmission. Here we review key epidemiological data from past Ebola outbreaks and carry out a comparative review of mathematical models of the spread and control of Ebola in the context of past outbreaks and the ongoing epidemic in West Africa. We show that mathematical modeling offers useful insights into the risk of a major epidemic of EVD and the assessment of the impact of basic public health measures on disease spread. We also discuss the critical need to collect detailed epidemiological data in real-time during the course of an ongoing epidemic, carry out further studies to estimate the effectiveness of interventions during past outbreaks and the ongoing epidemic, and develop large-scale modeling studies to study the spread and control of viral hemorrhagic fevers in the context of the highly heterogeneous economic reality of African countries.
KeywordsEbola Virus Disease Transmission model Control interventions Basic reproduction number West Africa Incubation Serial interval Case fatality ratio Isolation Behavior change
A complex epidemic of Zaire ebolavirus (EBOV) has been affecting West Africa since approximately December 2013, with the first cases likely occurring in southern Guinea . The causative Ebola strain is closely related to a strain associated with past EBOV outbreaks in Central Africa  and could have been circulating in West Africa for about a decade . However, the current epidemic was not identified until March 2014 , which facilitated several transmission chains to progress essentially unchecked in the region and to cross porous borders with neighboring Sierra Leone and Liberia and seed a limited outbreak in Nigeria via commercial airplane on 20 July 2014 . The World Health Organization declared the Ebola epidemic in West Africa a Public Health Emergency of International Concern on 8 August 2014 , with exponential dynamics characterizing the growth in the number of new cases in some areas -. Economic and sociocultural factors together with the delay in identifying the outbreak in urban settings have hindered a timely and effective implementation of control efforts in the region ,. Remarkably, the current size of the ongoing EBOV epidemic far surpasses the total number of cases reported for all previous Ebola outbreaks combined. A total of 6,553 cases, with 3,083 deaths, have been reported to the World Health Organization as of 23 September 2014.
A serious shortage of timely resources in the region is the key factor responsible for the onset and disproportionate scale of the ongoing epidemic in West Africa . In particular, the epidemic is unfolding in a region characterized by limited public health infrastructure including: (1) a lack of essential supplies to implement infection control measures in health care settings; (2) scarcity of health care workers and staff to manage a growing case burden and carry out essential contact tracing activities to find new cases quickly so that these can be effectively isolated ; and (3) the absence of epidemiological surveillance for the timely identification of case clusters ,. Containing the ongoing epidemic poses an unprecedented challenge as the virus has moved from Guinea to reach urban areas after crossing the unprotected borders of neighboring Liberia and Sierra Leone. A major coordinated operation on the ground is needed to limit the geographic extension of the epidemic.
The causative agent of Ebola virus disease (EVD) is an RNA virus of the family Filoviridae and genus Ebolavirus. Five different Ebolavirus strains have been identified, namely Zaire ebolavirus (EBOV), Sudan ebolavirus (SUDV), Tai Forest ebolavirus (TAFV), Bundibugyo ebolavirus (BDBV) and Reston ebolavirus (RESTV), with fruit bats considered as the most likely reservoir host . The great majority of past Ebola outbreaks in humans have been linked to three Ebola strains: EBOV, SUDV and BDBV . The Ebola virus, EBOV, (formerly designated Zaire ebolavirus), the deadliest of the five Ebolavirus strains, was first identified in 1976 in Zaire (now the Democratic Republic of Congo) and its name was derived from the Ebola River located near the source of the first outbreak. Past Ebola outbreaks have been reported on average every 1.5 years , with a total of 7 prior outbreaks generating over 100 reported cases . A recent study has estimated 22 million people distributed in areas of Central and West Africa to be at risk of Ebola .
Ebola is characterized by a high case fatality ratio which was nearly 90% in a past outbreak . After an incubation period mostly ranging from 2 to 21 days, nonspecific symptoms appear, including sudden onset of fever, weakness, vomiting, diarrhea, headache and a sore throat. A fraction of patients may later develop severe internal and external hemorrhagic manifestations and experience multiple organ failures . Except for RESTV, all other Ebola strains are pathogenic to humans. Human outbreaks may stem from direct human exposure to fruit bats or intermediate infected hosts that primarily comprise non-human primates (that is, gorillas, chimpanzees and monkeys). Human epidemics subsequently take off by direct human-to-human contact via bodily fluids or indirect contact with contaminated surfaces. Hence, stopping Ebola transmission should be feasible when the cases are detected early and managed properly, because this virus is not transmitted through the air or water . Nevertheless, Ebola has been shown to spread through the air under carefully controlled laboratory conditions . Hence, amplification of human-to-human transmission can result in the presence of suboptimal infection control measures in healthcare settings -. Unsafe burials that involve direct contact with Ebola-infected bodies also pose a major infection risk .
A review of key epidemiological parameters of EVD and our current understanding of the transmission dynamics and the effect of basic control interventions against this disease would be useful for guiding and assessing the potential effectiveness of control interventions during Ebola outbreaks. Specifically, here we review epidemiological data from past Ebola outbreaks including the basic reproduction number, the serial interval and the case fatality ratio. Subsequently, we carry out a comparative review of mathematical models of the spread and control of Ebola in the context of past and the ongoing epidemic in West Africa. We show that mathematical modeling offers useful insights into the risk of a major epidemic of EVD and the assessment of the impact of basic public health measures on disease spread. We illustrate the effects of demographic characteristics, such as the effective population size, size of spillover event (for example, details of initial conditions), baseline infection control measures in health care settings, and the timing of initiation of control interventions including enhancing the effectiveness of isolating infectious individuals, contact tracing to bring infectious individuals into isolation and social distancing interventions in the community.
Natural history parameters of EVD
Due to the relatively few past Ebola outbreaks, available epidemiological data to infer the natural history parameters of EVD remain limited. Moreover, past outbreaks have been caused by different virus strains, making it difficult to judge whether a certain observed epidemiological characteristic is unique to the causative strain. Here, we extract published evidence and review Ebola epidemiological parameters from the literature, integrating estimates of the basic reproduction number, the asymptomatic ratio, the incubation period, the latent period, the symptomatic period, the infectious period, the serial interval and the case fatality ratio.
The basic reproduction number, R0
The basic reproduction number, R 0, is interpreted as the average number of secondary cases caused by a typical infected individual throughout its entire course of infection in a completely susceptible population and in the absence of control interventions ,. In the context of a partially susceptible population owing to prior exposure or vaccination, the (effective) reproduction number, R, quantifies the potential for infectious disease transmission. If R <1, transmission chains are not self-sustaining and are unable to generate a major epidemic. By contrast, an epidemic is likely to occur whenever R >1. When measured over time t, the effective reproduction number R t , can be helpful to quantify the time-dependent transmission potential and evaluate the effect of control interventions in almost `real time’ . In summary, R 0 is regarded as a summary measure of the transmissibility of infectious diseases, playing a key role in determining the required control effort (for example, intensity of quarantine and isolation strategies). R 0 could also be useful for guiding the numbers of antivirals and vaccines that would be needed to achieve control whenever these are available.
Restimates for prior Ebola outbreaks in Central Africa
R 0 has been estimated for prior EVD outbreaks in Central Africa using mathematical modeling and epidemiological data for two Ebola outbreaks, namely the 1995 outbreak in Democratic Republic of Congo and the 2000 Uganda outbreak, respectively ,. Unlike the ongoing epidemic in West Africa, past outbreaks in Central Africa have been confined to relatively rural and isolated areas without spreading to urban sectors which facilitated the effective implementation of control interventions. Using a homogenous mixing SEIR (Susceptible-Exposed-Infectious-Removed) model that accounted for a gradual decay in the transmission rate at the start of interventions, Chowell et al.  estimated R 0 at 1.83 for Congo and 1.34 for Uganda. Using the same epidemic model but employing a Bayesian estimation method, Lekone and Finkenstadt  estimated slightly lower values at 1.33 to 1.35 for the outbreak in Uganda. Legrand et al. employed a different modeling approach : while allowing for homogeneous mixing, the study took into account three different transmission settings, that is, transmissions in community, hospital settings and during funerals. R 0 was estimated at 2.7 for Congo, 1995 and 2.7 for Uganda, 2000, but estimates showed substantial uncertainty. Transmission from burials alone accounted for 1.8 secondary transmissions in Congo while community transmission in Uganda accounted for 2.6 secondary transmissions. Variability in R 0 estimates across studies can be attributed to differences in model structure and underlying assumptions.
An assessment of R0 based on the growth rate of the 2014 Ebola epidemic in West Africa
Mathematical modeling studies of the 2014 Ebola epidemic in West Africa
Recent studies have started to shed light on the transmission potential of the ongoing EVD epidemic. Specifically, three studies have estimated the basic reproduction number of EVD in the range of 1.5 to 2.5 ,,. Althaus  employed an SEIR model with the time-dependency of the reproduction number to capture effects of control interventions, following the model by Chowell et al. ; analyzing the country-specific data independently for each country, the estimates were 1.5 for Guinea, 2.5 for Sierra Leone and 1.6 for Liberia . Gomes et al.  explicitly accounted for the risk of international spread, and the basic reproduction number ranged from 1.5 to 2.0. More importantly, this study employed a global epidemic model with mobility data, indicating that the short-term risk of international spread to outside Africa is small and that the expansion of the ongoing epidemic is more likely to occur in African countries . Moreover, Fisman et al. estimated R 0 at 1.8 using a two-parameter mathematical model that describes the epidemic growth and control .
Real-time estimation of the effective (time-dependent) reproduction number revealed estimates in line with R 0 estimates derived from other studies. For instance, by measuring temporal variations in the epidemic growth rate during periods of epidemic growth, the reproduction number was approximated based on a classic formula of R 0 for the SEIR model, which provided estimates in the range of 1.4 to 1.9 . A different modeling study accounted for both local transmission and transnational spread across severely affected countries using a multivariate renewal process model which allowed the derivation of global and country-specific estimates of the reproduction number . This study indicated that the effective reproduction number Rt from June to August 2014 ranged from 1.4 to 1.7 in Sierra Leone and Liberia. Hence, control could be reached by halting over half of the secondary transmissions per primary case whenever the reproduction number is below 2 . Moreover, it is worth noting that the exponential growth in Ebola incidence is placing great pressure on healthcare facilities, which could affect time- and space-dependent variations in transmission dynamics and the surveillance system . The analysis of available data using mathematical modeling should, therefore, carefully assess the quality and consistency of the surveillance system employed to collect epidemiological data. Hence, mathematical models should ideally be tied to characteristics of the surveillance system as much as possible to avoid potential bias .
Comparing Rwith other infectious diseases
For comparison with other filoviruses, the R 0 for the 2005 Marburg Fever Outbreak in Angola has been consistently estimated at 1.6 using two different statistical modeling approaches ,. For comparison with other infectious diseases transmitted by direct contact, R 0 has been estimated at 2.6 for an outbreak of acute hemorrhagic conjunctivitis in Mexico . In contrast, for respiratory infections, the reproduction number has been estimated for the SARS outbreaks in 2003 in the range 2.2 to 3.7 based on fitting transmission models to the progression of weekly cases prior to the start of control interventions ,, in the range 1.2 to 1.6 for seasonal influenza , 1.4 to 5.2 for influenza pandemics -, 15 for pertussis, 17 for measles  and 1.2 to 1.3 for meningococcal meningitis .
Asymptomatic infection and incubation period
The serial interval
The serial interval defined as the time from illness onset in the primary case to illness onset in the secondary case , has been relatively well observed for EVD based on household or contact-tracing studies. A household study during the outbreak in DRC indicated that the minimum serial interval was 7 days, while the maximum was 17 days . Findings based on contact tracing data for the outbreak in Uganda in 2000 were roughly consistent with those derived from household data : mean (SD) and median (quartiles) estimates for the serial interval were 12.0 (5.2) and 11.5 (8 to 17) days, respectively. Figure 3b shows the serial-interval distribution along with a fitted Weibull distribution with scale and shape parameters estimated at 13.6 (95% CI: 11.4, 16.1) and 2.6 (95% CI: 1.8, 3.5), respectively. The Cramér-von Mises goodness-of-fit test did not reveal significant deviations between the observed data and fitted model distribution (W2 = 0.05, P =0.25). This estimate is in good agreement with that derived from data of the first 9 months of the ongoing epidemic in West Africa, which has been estimated at 15.3 ± (SD =9.3) days . This distribution is key to quantifying the reproduction number using the exponential growth rate of cases during the early stage of an epidemic, because the conversion from the growth rate of cases to the reproduction number requires estimates of the generation time distribution  which is known to be informed by the serial interval and the incubation period .
The latent and infectious periods
Other parameters associated with the time course of EVD have not been rigorously ascertained. However, according to Bayesian model-based estimates from a past Ebola outbreak , the mean latent and infectious periods have been estimated at 9.4 and 5.7 days, respectively, using a vague prior and 10.1 and 6.5 days, respectively, for an informative prior. These exponential distributions based on a mathematical modeling study are the only available empirical evidence for these two time periods. The mean length of time from illness onset to death is approximately 10 days ,, but the transmissibility from the deceased from Ebola may account for a certain fraction of secondary transmissions . Hence, the infectious period could be longer than the observable time to death if the burial is extended.
The case fatality ratio
Summary of empirical estimates of epidemiological parameters for Ebola virus disease (EVD)
12.7 days (mean)
10.1 days (mean)
6.5 days (mean)
12.0 days (mean)
16.6 days (mean)
Time from illness onset to death
10 days (mean)
Case fatality ratio
41% to 65% (Sudan)
61% to 89% (Zaire)
Models of Ebola transmission dynamics and control
More detailed epidemiological data and information about the contributions of different settings to transmission could guide the design of more elaborate models that could be helpful to quantify the effects of more specific intervention strategies. Legrand et al.  developed a structured transmission model to describe Ebola epidemics with contributions to the force of infection from the community, funerals and healthcare settings. The most distinctive feature of this model is that transmission during burial rituals is modeled by accounting for the duration of the burial and the intensity of transmission with infectious bodies. This model is comprised by six epidemiologically relevant states and thirteen parameters. The model was calibrated to data of the Ebola outbreaks in the Republic of Congo in 1995 and Uganda in 2000 by fitting three transmission rate parameters, one for each transmission setting and one parameter to quantify the effectiveness of interventions. The full model can be applied to the West African epidemic particularly for Guinea, Sierra Leone and Liberia where burial practices involve the touching of bodies of the deceased . But this feature is believed to be less influential in transmission in the context of Nigeria where a limited outbreak developed. To illustrate the effects of control interventions during Ebola outbreaks, here we only account for transmission in the community and in healthcare settings by adjusting baseline transmission rates, diagnostic rates and enhancement of infection-control measures (for example, strict use of protective equipment by health-care workers and effective isolation of infectious individuals) (see for example, ,,,,,). In this simpler setting, the population is divided into five categories: susceptible individuals (S); exposed individuals (E); infectious and symptomatic individuals (I); hospitalized individuals (H); and removed individuals after recovery or disease-induced death (R).
where R comm = γ 0 /(γ a0 + γ I) and
R hosp = γ 0 l 0 (1/γ r)(γ a0 /(γ a0 + γ I)).
Initial transmission dynamics
The natural reservoir hosts of the Ebola virus have yet to be confirmed ,, but laboratory studies point to fruit bats as the most likely culprit harboring the Ebola virus in the natural habitat -. Ebola outbreaks among humans have been associated with direct exposure to fruit bats and mortality among other wild animals, which tend to succumb to the infection -. Epidemiological data support the notion that spillover events of Ebola virus from a natural reservoir (that is, fruit bats) or an intermediate host, such as non-human primates, into human populations occur with a certain frequency (for example, ,), but only a small number of those introductions are ever correctly diagnosed and reported or successfully unfold human-to-human transmission chains that lead to outbreaks. This hinders our understanding of the frequency of spillover events as a function of time (for example, season) and its relationship with variation in climatological or socioeconomic variables. We note that two studies have associated the onset of Ebola outbreaks with climatological variables ,. Specifically, Pinzon et al. reported evidence that Ebola outbreaks are correlated with drastic shifts from dry to wet conditions  while a more recent study by Ng et al. found lower temperature and higher absolute humidity associated with the onset of EVD outbreaks during 1976 to 2014 .
Delays in outbreak detection
Lack of public health infrastructure
Socio-cultural factors have not only contributed significantly to Ebola spread, but have also complicated the implementation of control interventions. Specifically, cultural practices involving touching the body of the deceased naturally (and greatly) contribute to the dissemination of the Ebola virus . In particular, the potential for transmission to neighboring and distant areas by exposed funeral attendants could facilitate the development of major epidemics ,. Moreover, the lack of prior experience or knowledge of the disease can lead communities to deny its existence and to associate illness with witchcraft or conspiracy theories presumably created by governments to gain control of populations or attract resources from the international community ,. For instance, during the ongoing epidemic in West Africa, a group of individuals looted equipment and potentially contaminated materials in an isolation facility in a quarantined neighborhood . Finally, the stigma carried by Ebola survivors and family members of Ebola victims could exacerbate disease spread. In particular, uninformed families tend to hide relatives and friends infected with Ebola to avoid being shunned by their own communities, which enhances transmission rates . The problem is compounded by the high case fatality ratio of EVD whereby misinformed communities tend to associate case isolation with a death sentence.
Future directions and conclusions
The ongoing epidemic in West Africa offers a unique opportunity to improve our current understanding of the transmission characteristics of EVD in humans, including the duration of immunity among Ebola survivors and the case fatality ratio in the presence or absence of supportive therapy ,, as well as the effectiveness of various control interventions . For this purpose, there is a critical need to collect detailed epidemiological data in real-time during the ongoing epidemic through the establishment of efficient epidemiological surveillance systems in the affected areas. In addition, we cannot overemphasize the importance of collecting data relating to population behaviors influencing disease spread and control and how these have changed over time. It would also be important to record the level of adoption of preventive and social distancing measures in the community and adherence to infection control measures in health care settings. Detailed data regarding control interventions would also be critical to assess their effectiveness in reducing secondary transmissions including information on the changing numbers of isolation and treatment centers, healthcare workers, intensity of contact tracing activities and awareness campaigns in the community.
There is a scarcity of empirical studies quantifying transmission and the effects of control interventions implemented during past Ebola outbreaks ,. Further work is also needed to quantify the effects of various interventions put in place during the ongoing epidemic in West Africa. Specifically, careful mathematical and statistical modeling studies could help ascertain the role of social distancing interventions (for example, school closures and cancellation of mass gathering events), infection control measures in health care settings (for example, isolation and other infection control measures among health care workers) and contact tracing and quarantine efforts ,,,-. In addition to individual epidemiological data, the timing of such interventions should be recorded along with the scale and extent of interventions (for example, closure of class rooms or entire schools). Intervention studies could reveal, for instance, whether effective infection control mechanisms in hospital settings could suffice to bring an epidemic under control or whether a combination of control strategies would be critical to ensure epidemic control (for example, R <1).
While a significant number of computational models have been developed to inform preparedness plans against pandemic influenza -, comprehensive modeling studies to examine the spread and control of viral hemorrhagic fevers, including Ebola, in the context of the highly heterogeneous economic reality of African countries are yet to be developed. The shortage of modeling efforts could be explained by the fact that large Ebola outbreaks affecting large population settings were largely unexpected until now. To start filling this gap, datasets comprising detailed demographic, socio-economic, contact rates and population mobility estimates in the region (for example, commuting networks, air traffic) need to be integrated. Given that the disease is highly fatal, dynamic features of contact and mobility should also be closely investigated. Modeling studies with local demographic characteristics and human movement could be useful not only to assess the likelihood of major epidemics and carry out sensible projections on epidemic outcomes, but also to guide control efforts in the field, such as the estimation of the number, size and location of isolation facilities, the number of health workers and staff and essential supplies that would be needed to respond to a particular outbreak scenario as well as to quantify the effects of potential quarantine efforts in certain areas, border closures and air travel restrictions.
Proven treatments or vaccines against Ebola are still not available. Hence, our current working toolbox available to control the spread of Ebola still hinges on supportive medical care to increase the survival of those infected and basic non-pharmaceutical public health measures  to prevent transmission, namely: 1) infection control measures including standard precautions in health care settings; 2) rapid contact tracing and isolation of infectious individuals; and 3) social distancing interventions in the community which may include the dissemination of awareness campaigns to inform the population on how to avoid contracting the disease, quarantining individuals potentially exposed to infectious individuals and restricting the movement of communities exhibiting local transmission to prevent onward transmission. These actions must be conducted in close collaboration with local community leaders to effectively reach the population at large. With the ongoing epidemic in West Africa, the development of treatments and vaccines against Ebola is accelerating ,. For instance, emergency use of a trickle of doses of an experimental drug with unknown efficacy or safety record in humans has been initiated during the outbreak . Recent experiments in monkeys provide promising evidence that this experimental drug could have a significant impact on mortality burden during Ebola outbreaks . Furthermore, a promising bivalent Ebola vaccine against the Zaire and Sudan Ebola strains is entering human safety trials in September 2014  with an initial goal of building a stockpile of 10,000 doses by November 2014. Nevertheless, apart from pharmaceutical effects on the prognosis of infection, we have yet to examine how medication changes the transmission dynamics. Hence, careful studies could be useful for assessing the impacts of treatment on contact, transmission and diagnosis as well as on the disease burden . If an Ebola vaccine is developed successfully, one could assess the effectiveness of pre-emptive and reactive treatment and vaccination plans in the context of limited stockpiles. Finally, it is worth noting that our efforts to prepare against current and future infectious disease threats should also include potential deliberate attempts to trigger epidemics, which are largely unexpected events but could pose high impact on public health and global economic activities.
GC and HN drafted and revised the manuscript. Both authors read and approved the final manuscript.
GC acknowledges financial support from the NSF grant 1414374 as part of the joint NSF-NIH-USDA Ecology and Evolution of Infectious Diseases program, UK Biotechnology and Biological Sciences Research Council grant BB/M008894/1, NSF grant 1318788. III: Small: Data Management for Real-Time Data Driven Epidemic Spread Simulations, the Division of International Epidemiology and Population Studies, The Fogarty International Center, US National Institutes of Health, and the Visiting Foreign Scholar program of the Heiwa Nakajima Foundation for supporting a sabbatical research visit at the University of Tokyo. HN received funding support from the Japanese Society for the Promotion of Science (JSPS) KAKENHI Grant Number 26700028, St Luke's Life Science Institute Research Grant for Clinical Epidemiology Research 2014 and the Commissioned Research program of the Ministry of Health, Labour and Welfare, Japan (H26-ShinkoJitsuyoka- General-016).
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