Introduction: Although many studies have investigated the probability of Ebola virus disease (EVD) outbreaks while other studies have simulated the size and speed of EVD outbreaks, few have investigated the environmental and population-level predictors of Ebola transmission once an outbreak is underway. Identifying strong predictors of transmission could help guide and target limited public health resources during an EVD outbreak. We examined several environmental and population-level demographic predictors of EVD risk from the West African epidemic. Methods: We obtained district-level estimates from the World Health Organization EVD case data, demographic indicators obtained from the Demographic and Health surveys, and satellite-derived temperature, rainfall, and land cover estimates. A Bayesian hierarchical Poisson model was used to estimate EVD risk and to evaluate the spatial variability explained by the selected predictors. Results: We found that districts had greater risk of EVD with increasing proportion of households not possessing a radio (RR 2.79, 0.90-8.78; RR 4.23, 1.16-15.93), increasing rainfall (RR 2.18; 0.66-7.20; 5.34, 1.20-23.90), and urban land cover (RR 4.87, 1.56-15.40; RR 5.74, 1.68-19.67). Discussion: The finding of radio ownership and reduced EVD transmission risk suggests that the use of radio messaging for control and prevention purposes may have been crucial in reducing the EVD transmission risk in certain districts, although this association requires further study. Future research should examine the etiologic relationships between the identified risk factors and human-to-human transmission of EVD with a focus on factors related to population mobility and healthcare accessibility, which are critical features of epidemic propagation and control.
Background: This qualitative study was designed to assess health care volunteers’ experiences and psychosocial impacts associated with deployment to the West Africa Ebola epidemic.
Methods: In 2015, using snowball sampling, 16 US health care volunteers who had recently returned from West Africa were recruited for this study. Semi-structured interviews were conducted to collect information associated with each phase of deployment (pre, peri, and post).
Results: Participants reported that they were motivated to volunteer because of a sense of responsibility and feelings of empathy and altruism. Immediately prior to deployment, most reported fear of contagion and death, as well as doubts regarding the adequacy of their training. Family members and close friends expressed high levels of concern regarding participants’ decisions to volunteer. During the deployment, participants were fearful of exposure and reported feeling emotionally and physically exhausted. They also reported feeling frustrated by extreme resource limitations, poor management of the mission, lack of clearly defined roles and responsibilities, and inability to provide high quality care. Upon return home, participants felt a sense of isolation, depression, stigmatization, interpersonal difficulties, and extreme stress.
Conclusion: Preparedness of volunteers was suboptimal at each stage of deployment. All stakeholders, including volunteers, sponsoring organizations, government agencies, and professional organizations have a shared responsibility in ensuring that volunteers to medical missions are adequately prepared. This is especially critical for high risk deployments. Effective policies and practices need to be developed and implemented in order to protect the health and well-being of health care volunteers to the fullest extent possible.
Introduction: The first case of Ebola Virus Disease (EVD) in Nigeria was imported on 20th July 2014, by an air traveller. On 8th August, 2014, WHO declared the Ebola outbreak in West Africa a Public Health Emergency of International Concern (PHEIC). This study aimed at assessing the knowledge, perception and attitude of secondary school students towards EVD and adopting disease preventive behaviour.
Methods: A descriptive cross sectional study of 440 students from a mixed secondary school in Owo, Ondo State was done. Data was collected in October 2014 when Nigeria was yet to be declared EVD free.Simple random sampling was used to select the school while Systematic random sampling was used in the selection of participants. A semi-structured, interviewer administered questionnaire was used to collect data. Data was analyzed with SPSS version 21. Descriptive statistics and Chi-square test were done, level of statistical significant was 5%.
Results: Mean age of respondents was 13.7±1.9 years. Females were 48.2%. Most of the respondents had heard of Ebola Virus Disease (95.4%). Female respondents (51.3%), those who were 15 years and above (51.1%) and in the senior class (54.1%), and had good general knowledge of EVD and across all domains. Being in the senior secondary class and seeking for health care in the hospital were positively associated with good general knowledge (p-value: 0.029, and <0.001 respectively). Three commonest modes of spread of EVD mentioned were contact between infected animals and men (74.8%), touching body fluids of a person who is sick of EVD (57.0%), and contact (55.2%). The top three signs of EVD mentioned were abnormal bleeding from any part of the body (56.10%), vomiting (47.0%) and fever (42.3%).
Conclusion: Our results revealed suboptimal EVD-related knowledge, attitude and practice among the students. Promotion of health messages and training of students on prevention of EVD to effectively control past and future outbreaks of EVD in Nigeria was immediately initiated in schools in Ondo State.
In the three West African countries most affected by the recent Ebola virus disease (EVD) outbreak, resistance to public health measures contributed to the startling speed and persistence of this epidemic in the region. But how do we explain this resistance, and how have people in these communities understood their actions? By comparing these recent events to historical precedents during Cholera outbreaks in Europe in the 19th century we show that these events have not been new to history or unique to Africa. Community resistance must be analysed in context and go beyond simple single-variable determinants. Knowledge and respect of the cultures and beliefs of the afflicted is essential for dealing with threatening disease outbreaks and their potential social violence.
The West Africa Ebola virus epidemic now appears to be coming to an end. In the proposed model, we simulate changes in population behavior that help to explain the observed transmission dynamics. We introduce an EVD transmission model accompanied by a model of social mobilization. The model was fit to Lofa County, Liberia through October 2014, using weekly counts of new cases reported by the US CDC. In simulation studies, we analyze the dynamics of the disease transmission with and without population behavior change, given the availability of beds in Ebola treatment units (ETUs) estimated from observed data. Only the model scenario that included individuals’ behavioral change achieved a good fit to the observed case counts. Although the capacity of the Lofa County ETUs greatly increased in mid-August, our simulations show that the expansion was insufficient to alone control the outbreak. Modeling the entire outbreak without considering behavior change fit the data poorly, and extrapolating from early data without taking behavioral changes into account led to a prediction of exponential outbreak growth, contrary to the observed decline. Education and awareness-induced behavior change in the population was instrumental in curtailing the Ebola outbreak in Lofa County and is likely playing an important role in stopping the West Africa epidemic altogether.
In this commentary, we consider the relationship between early outbreak changes in the observed reproductive number of Ebola in West Africa and various media reported interventions and aggravating events. We find that media reports of interventions that provided education, minimized contact, or strengthened healthcare were typically followed by sustained transmission reductions in both Sierra Leone and Liberia. Meanwhile, media reports of aggravating events generally preceded temporary transmission increases in both countries. Given these preliminary findings, we conclude that media reported events could potentially be incorporated into future epidemic modeling efforts to improve mid-outbreak case projections.
The magnitude of the Ebola outbreak in West Africa is unprecedented. Liberia, Guinea, and Sierra Leone are in the bottom ten countries in the Human Development Index, but all had made gains in child survival prior to the outbreak. With closure of healthcare facilities and the loss of health workers secondary to the outbreak, the region risks reversing survival gains achieved in maternal and newborn health.
Anonymized service utilization data were downloaded from the Liberia District Health Information Software (DHIS) 2 for selected maternal health services at PHC facilities in Margibi and Bong Counties from March 2014, when the first case of Ebola was reported in Liberia, through December 2014. Absolute numbers are provided instead of percentage measures because of the lack of a population-based denominator.
Overall, the data show a decrease in absolute utilization from the start of the outbreak, followed by a slow recovery after October or November. In Bong County, totals were less than 14% of the peak numbers during the outbreak for number of antenatal visits and pregnant women receiving intermittent preventive treatment for malaria in pregnancy (IPTp). For total deliveries, utilization was less than 33% of the highest month. In Margibi County, during what now appears to be the height of the outbreak, numbers dropped to less than 9% of peak utilization for antenatal care visits and 4% for IPTp. Total health facility deliveries dropped to less than 9% of peak utilization.
It is clear that Bong and Margibi Counties in Liberia experienced a large drop in utilization of maternal health care services during what now appears to be the peak of the Ebola outbreak. As the health of women and their babies is being promoted in the post-2015 sustainable development agenda, it is critical that the issue of maternal and newborn survival in humanitarian emergency settings, like the Ebola outbreak, is prioritized.
Background: The first ever outbreak of Ebola virus disease (EVD) in Nigeria was declared in July, 2014. Level of public knowledge, perception and adequacy of information on EVD were unknown. We assessed the public preparedness level to adopt disease preventive behavior which is premised on appropriate knowledge, perception and adequate information.
Methods: We enrolled 5,322 respondents in a community-based cross-sectional study. We used interviewer-administered questionnaire to collect data on socio-demographic characteristics, EVD–related knowledge, perception and source of information. We performed univariate and bivariate data analysis using Epi-Info software setting p-value of 0.05 as cut-off for statistical significance.
Results: Mean age of respondents was 34 years (± 11.4 years), 52.3% were males. Forty one percent possessed satisfactory general knowledge; 44% and 43.1% possessed satisfactory knowledge on mode of spread and preventive measures, respectively. Residing in EVD cases districts, male respondents and possessing at least secondary education were positively associated with satisfactory general knowledge (p-value: 0.01, 0.001 and 0.000004, respectively). Seventy one percent perceived EVD as a public health problem while 61% believed they cannot contract the disease. Sixty two percent and 64% of respondents will not shake hands and hug a successfully treated EVD patient respectively. Only 2.2% of respondents practice good hand-washing practice. Television (68.8%) and radio (55.0%) are the most common sources of information on EVD.
Conclusions: Gaps in EVD-related knowledge and perception exist. Targeted public health messages to raise knowledge level, correct misconception and discourage stigmatization should be widely disseminated, with television and radio as media of choice.
We developed an agent-based model to investigate the epidemic dynamics of Ebola virus disease (EVD) in Liberia and Sierra Leone from May 27 to December 21, 2014. The dynamics of the agent-based simulator evolve on small-world transmission networks of sizes equal to the population of each country, with adjustable densities to account for the effects of public health intervention policies and individual behavioral responses to the evolving epidemic. Based on time series of the official case counts from the World Health Organization (WHO), we provide estimates for key epidemiological variables by employing the so-called Equation-Free approach. The underlying transmission networks were characterized by rather random structures in the two countries with densities decreasing by ~19% from the early (May 27-early August) to the last period (mid October-December 21). Our estimates for the values of key epidemiological variables, such as the mean time to death, recovery and the case fatality rate, are very close to the ones reported by the WHO Ebola response team during the early period of the epidemic (until September 14) that were calculated based on clinical data. Specifically, regarding the effective reproductive number Re, our analysis suggests that until mid October, Re was above 2.3 in both countries; from mid October to December 21, Re dropped well below unity in Liberia, indicating a saturation of the epidemic, while in Sierra Leone it was around 1.9, indicating an ongoing epidemic. Accordingly, a ten-week projection from December 21 estimated that the epidemic will fade out in Liberia in early March; in contrast, our results flashed a note of caution for Sierra Leone since the cumulative number of cases could reach as high as 18,000, and the number of deaths might exceed 5,000, by early March 2015. However, by processing the reported data of the very last period (December 21, 2014-January 18, 2015), we obtained more optimistic estimates indicative of a remission of the epidemic in Sierra Leone, as reflected by the derived Re (~0.82, 95% CI: 0.81-0.83).
Background: An EVD outbreak may reduce life expectancy directly (due to high mortality among EVD cases) and indirectly (e.g., due to lower utilization of healthcare and subsequent increases in non-EVD mortality). In this paper, we investigated the direct effects of EVD on life expectancy in Liberia, Sierra Leone and Guinea (LSLG thereafter).
Methods: We used data on EVD cases and deaths published in situation reports by the World Health Organization (WHO), as well as data on the age of EVD cases reported from patient datasets. We used data on non-EVD mortality from the most recent life tables published prior to the EVD outbreak. We then formulated three scenarios based on hypotheses about a) the extent of under-reporting of EVD cases and b) the EVD case fatality ratio. For each scenario, we re-estimated the number of EVD deaths in LSLG and we applied standard life table techniques to calculate life expectancy.
Results: In Liberia, possible reductions in life expectancy resulting from EVD deaths ranged from 1.63 year (low EVD scenario) to 5.56 years (high EVD scenario), whereas in Sierra Leone, possible life expectancy declines ranged from 1.38 to 5.10 years. In Guinea, the direct effects of EVD on life expectancy were more limited (<1.20 year).
Conclusions: Our high EVD scenario suggests that, due to EVD deaths, life expectancy may have declined in Liberia and Sierra Leone to levels these two countries had not experienced since 2001-2003, i.e., approximately the end of their civil wars. The total effects of EVD on life expectancy may however be larger due to possible concomitant increases in non-EVD mortality during the outbreak.
Background: Between August and November 2014, the incidence of Ebola virus disease (EVD) rose dramatically in several districts of Sierra Leone. As a result, the number of cases exceeded the capacity of Ebola holding and treatment centres. During December, additional beds were introduced, and incidence declined in many areas. We aimed to measure patterns of transmission in different regions, and evaluate whether bed capacity is now sufficient to meet future demand.
Methods: We used a mathematical model of EVD infection to estimate how the extent of transmission in the nine worst affected districts of Sierra Leone changed between 10th August 2014 and 18th January 2015. Using the model, we forecast the number of cases that could occur until the end of March 2015, and compared bed requirements with expected future capacity.
Results: We found that the reproduction number, R, defined as the average number of secondary cases generated by a typical infectious individual, declined between August and December in all districts. We estimated that R was near the crucial control threshold value of 1 in December. We further estimated that bed capacity has lagged behind demand between August and December for most districts, but as a consequence of the decline in transmission, control measures caught up with the epidemic in early 2015.
Conclusions: EVD incidence has exhibited substantial temporal and geographical variation in Sierra Leone, but our results suggest that the epidemic may have now peaked in Sierra Leone, and that current bed capacity appears to be sufficient to keep the epidemic under-control in most districts.
A differential equations model is developed for the 2014 Ebola epidemics in Sierra Leone and Liberia. The model describes the dynamic interactions of the susceptible and infected populations of these countries. The model incorporates the principle features of contact tracing, namely, the number of contacts per identified infectious case, the likelihood that a traced contact is infectious, and the efficiency of the contact tracing process. The model is first fitted to current cumulative reported case data in each country. The data fitted simulations are then projected forward in time, with varying parameter regimes corresponding to contact tracing efficiencies. These projections quantify the importance of the identification, isolation, and contact tracing processes for containment of the epidemics.
Background: The ongoing outbreak of Ebola Virus Disease in West Africa requires immediate and sustained input from the international community in order to curb transmission. The CDC has produced a model that indicates that to end the outbreak by pushing the reproductive number below one, 25% of the patients must be placed in an Ebola Treatment Unit (ETC) and 45% must be isolated in community settings in which risk of disease transmission is reduced and safe burials are provided. In order to provide firmer targets for the international response in Sierra Leone, we estimated the national and international personnel and treatment capacity that may be required to reach these percentages.
Methods: We developed a compartmental SEIR model that was fitted to WHO data and local data allowing the reproductive number to change every 8 weeks to forecast the progression of the EVD epidemic in Sierra Leone. We used the previously estimated 2.5x correction factor estimated by the CDC to correct for underreporting. Number of personnel required to provide treatment for the predicted number of cases was estimated using UNMEER and UN OCHA requests for resources required to meet the CDC target of 70% isolation.
Results: As of today (2014-12-04), we estimate that there are 810 (95% CI=646 to 973) EVD active cases in treatment, with an additional 3751 (95% CI=2778 to 4723) EVD cases unreported and untreated. To reach the CDC targets today, we need 1140 (95% CI=894 to 1387) cases in ETCs and 2052 (95% CI=1608 to 2496) at home or in a community setting with a reduced risk for disease transmission. In 28 days (2015-01-01), we will need 1309 (95% CI=804 to 1814) EVD cases in ETCs and 2356 (95% CI=1447 to 3266) EVD cases at reduced risk of transmission. If the current transmission rate is not reduced, up to 3183 personnel in total will be required in 56 days (2015-01-29) to operate ETCs according to our model.
Conclusions: The current outbreak will require massive input from the international community in order to curb the transmission through traditional containment mechanisms by breaking the chains of transmission in Sierra Leone. If sufficient treatment facilities, healthcare workers and support personnel are not rapidly deployed, the increasing number of cases will be overwhelming.In addition to supporting isolation and treatment mechanisms, other viable control options, such as the development of an effective vaccine, should be supported.
While many infectious disease epidemics are initially characterized by an exponential growth in time, we show that district-level Ebola virus disease (EVD) outbreaks in West Africa follow slower polynomial-based growth kinetics over several generations of the disease.
We analyzed epidemic growth patterns at three different spatial scales (regional, national, and subnational) of the Ebola virus disease epidemic in Guinea, Sierra Leone and Liberia by compiling publicly available weekly time series of reported EVD case numbers from the patient database available from the World Health Organization website for the period 05-Jan to 17-Dec 2014.
We found significant differences in the growth patterns of EVD cases at the scale of the country, district, and other subnational administrative divisions. The national cumulative curves of EVD cases in Guinea, Sierra Leone, and Liberia show periods of approximate exponential growth. In contrast, local epidemics are asynchronous and exhibit slow growth patterns during 3 or more EVD generations, which can be better approximated by a polynomial than an exponential function.
The slower than expected growth pattern of local EVD outbreaks could result from a variety of factors, including behavior changes, success of control interventions, or intrinsic features of the disease such as a high level of clustering. Quantifying the contribution of each of these factors could help refine estimates of final epidemic size and the relative impact of different mitigation efforts in current and future EVD outbreaks.
Background: The indirect effects of the Ebola epidemic on health service function may be significant but is not known. The aim of this study was to quantify to what extent admission rates and surgery has changed at health facilities providing such care in Sierra Leone during the time of the Ebola epidemic.
Methods: Weekly data on facility inpatient admissions and surgery from admission and surgical theatre register books were retrospectively retrieved during September and October. 21 Community Health Officers enrolled in a surgical task-shifting program personally visited the facilities. The study period was January 6 (week 2) to October 12, (week 41) 2014.
Results: Data was retrieved from 40 out of 55 facilities. A total of 62,257 admissions and 12,124 major surgeries were registered for the study period.
Total admissions in the week of the first Ebola case were 2,006, median 40 (IQR 20-76) compared to 883, median 12 (IQR 4-30) on the last week of the study. This equals a 70% drop in median number of admissions (p=0.005) between May and October. Total number of major surgeries fell from 342, median 6 (IQR 2-14) to 231, median 3 (IQR 0-6) in the same period, equal 50% reduction in median number of major surgeries (p=0.014).
Conclusions: Inpatient health services have been severely affected by the Ebola outbreak. The dramatic documented decline in facility inpatient admissions and major surgery is likely to be an underestimation. Reestablishing such care is urgent and must be a priority.
Objectives: To assess the risk of Ebola importation to Australia during the first six months of 2015, based upon the current outbreak in West Africa.
Methodology: We assessed the risk under two distinct scenarios: (i) assuming that significant numbers of cases of Ebola remain confined to Guinea, Liberia and Sierra Leone, and using historic passenger arrival data into Australia; and, (ii) assuming potential secondary spread based upon international flight data. A model appropriate to each scenario is developed, and parameterised using passenger arrival card or international flight data, and World Health Organisation case data from West Africa. These models were constructed based on WHO Ebola outbreak data as at 17 October 2014 and 3 December 2014. An assessment of the risk under each scenario is reported. On 27 October 2014 the Australian Government announced a policy change, that visas from affected countries would be refused/cancelled, and the predicted effect of this policy change is reported.
Results: The current probability of at least one case entering Australia by 1 July 2015, having travelled directly from West Africa with historic passenger arrival rates into Australia, is 0.34. Under the new Australian Government policy of restricting visas from affected countries (as of 27 October 2014), the probability of at least one case entering Australia by 1 July 2015 is reduced to 0.16. The probability of at least one case entering Australia by 1 July 2015 via an outbreak from a secondary source country is approximately 0.12.
Conclusions: Our models suggest that if the transmission of Ebola remains unchanged, it is possible that a case will enter Australia within the first six months of 2015, either directly from West Africa (even when current visa restrictions are considered), or via secondary outbreaks elsewhere. Government and medical authorities should be prepared to respond to this eventuality. Control measures within West Africa over recent months have contributed to a reduction in projected risk of a case entering Australia. A significant further reduction of the rate at which Ebola is proliferating in West Africa, and control of the disease if and when it proliferates elsewhere, will continue to result in substantially lower risk of the disease entering Australia.
Background: The 2014 West African Ebola outbreak has evolved into an epidemic of historical proportions and catastrophic scope. Prior outbreaks have been contained through the use of personal protective equipment, but such an approach has not been rapidly effective in the current epidemic. Several candidate vaccines have been developed against the Ebola virus, and are undergoing initial clinical trials.
Methods: As removal of population-level susceptibility through vaccination could be a highly impactful control measure for this epidemic, we sought to estimate the number of vaccine doses and timing of vaccine administration required to reduce the epidemic size. Our base model was fit using the IDEA approach, a single equation model that has been successful to date in describing Ebola growth. We projected the future course of the Ebola epidemic using this model. Vaccination was assumed to reduce the effective reproductive number. We evaluated the potential impact of vaccination on epidemic trajectory under different assumptions around timing of vaccine availability.
Results: Using effective reproductive (Re) number estimates derived from this model, we estimate that 3-4 million doses of vaccine, if available and administered, could reduce Re to 0.9 in the interval from January-March 2015. Later vaccination would be associated with a progressively diminishing impact on final epidemic size; in particular, vaccination to the same Re at or after the epidemic is projected to peak (April-May 2015) would have little impact on final epidemic size, though more intensive campaigns (e.g., Re reduced to 0.5) could still be effective if initiated by summer 2015. In summary, there is a closing window of opportunity for the use of vaccine as a tool for Ebola epidemic control.
Conclusions: Effective vaccination, used before the epidemic peaks, would be projected to prevent tens of thousands of deaths; this does not minimize the ethical challenges that would be associated with wide-scale application of vaccines that have undergone only limited evaluation for safety and efficacy.
An unprecedented epidemic of Zaire ebolavirus (EBOV) has affected West Africa since approximately December 2013, with intense transmission on-going in Guinea, Sierra Leone and Liberia and increasingly important international repercussions. Mathematical models are proving instrumental to forecast the expected number of infections and deaths and quantify the intensity of interventions required to control transmission; however, calibrating mechanistic transmission models to an on-going outbreak is a challenging task owing to limited availability of epidemiological data and rapidly changing interventions. Here we project the trajectory of the EBOV epidemic in Liberia by fitting logistic growth models to the cumulative number of cases. Our model predictions align well with the latest epidemiological reports available as of October 23, and indicates that the exponential growth phase is over in Liberia, with an expected final attack rate of ~0.1-0.12%. Our results indicate that simple phenomenological models can provide complementary insights into the dynamics of an outbreak and capture early signs of changes in population behavior and interventions. In particular, our results underscore the need to treat the effective size of the susceptible population as a dynamic variable rather than a fixed quantity, due to reactive changes in transmission throughout the outbreak. We show that predictions from the logistic model are more variable in the earlier stages of an epidemic (such as the EBOV epidemics in Sierra Leone and Guinea). More research is warranted to compare the performances of mechanistic and phenomenological approaches for disease forecasts, before such predictions can be fully used by public health authorities.
Background: In mid-October 2014, the number of cases of the West Africa Ebola virus epidemic in Guinea, Sierra Leone and Liberia exceeded 9,000 cases. The early growth dynamics of the epidemic has been qualitatively different for each of the three countries. However, it is important to understand these disparate dynamics as trends of a single epidemic spread over regions with similar geographic and cultural aspects, with likely common parameters for transmission rates and reproduction number R0.
Methods: We combine a discrete, stochastic SEIR model with a three-scale community network model to demonstrate that the different regional trends may be explained by different community mixing rates. Heuristically, the effect of different community mixing rates may be understood as the observation that two individuals infected by the same chain of transmission are more likely to share the same contacts in a less-mixed community. Local saturation effects occur as the contacts of an infected individual are more likely to already be exposed by the same chain of transmission.
Results: The effects of community mixing, together with stochastic effects, can explain the qualitative difference in the growth of Ebola virus cases in each country, and why the probability of large outbreaks may have recently increased. An increase in the rate of Ebola cases in Guinea in late August, and a local fitting of the transient dynamics of the Ebola cases in Liberia, suggests that the epidemic in Liberia has been more severe, and the epidemic in Guinea is worsening, due to discrete seeding events as the epidemic spreads into new communities.
Conclusions: A relatively simple network model provides insight on the role of local effects such as saturation that would be difficult to otherwise quantify. Our results predict that exponential growth of an epidemic is driven by the exposure of new communities, underscoring the importance of limiting this spread.
With the Ebola epidemic raging out of control in West Africa, there has been a flurry of research into the Ebola virus, resulting in the generation of much genomic data.
In response to the clear need for tools that integrate multiple strands of research around molecular sequences, we have created the University of California Santa Cruz (UCSC) Ebola Genome Browser, an adaptation of our popular UCSC Genome Browser web tool, which can be used to view the Ebola virus genome sequence from GenBank and nearly 30 annotation tracks generated by mapping external data to the reference sequence. Significant annotations include a multiple alignment comprising 102 Ebola genomes from the current outbreak, 56 from previous outbreaks, and 2 Marburg genomes as an outgroup; a gene track curated by NCBI; protein annotations curated by UniProt and antibody-binding epitopes curated by IEDB. We have extended the Genome Browser’s multiple alignment color-coding scheme to distinguish mutations resulting from non-synonymous coding changes, synonymous changes, or changes in untranslated regions.
Our Ebola Genome portal at http://genome.ucsc.edu/ebolaPortal/ links to the Ebola virus Genome Browser and an aggregate of useful information, including a collection of Ebola antibodies we are curating.
Background: An Ebola outbreak of unparalleled size is currently affecting several countries in West Africa, and international efforts to control the outbreak are underway. However, the efficacy of these interventions, and their likely impact on an Ebola epidemic of this size, is unknown. Forecasting and simulation of these interventions may inform public health efforts.
Methods: We use existing data from Liberia and Sierra Leone to parameterize a mathematical model of Ebola and use this model to forecast the progression of the epidemic, as well as the efficacy of several interventions, including increased contact tracing, improved infection control practices, the use of a hypothetical pharmaceutical intervention to improve survival in hospitalized patients.
Findings: Model forecasts until Dec. 31, 2014 show an increasingly severe epidemic with no sign of having reached a peak. Modeling results suggest that increased contact tracing, improved infection control, or a combination of the two can have a substantial impact on the number of Ebola cases, but these interventions are not sufficient to halt the progress of the epidemic. The hypothetical pharmaceutical intervention, while impacting mortality, had a smaller effect on the forecasted trajectory of the epidemic.
Interpretation: Near-term, practical interventions to address the ongoing Ebola epidemic may have a beneficial impact on public health, but they will not result in the immediate halting, or even obvious slowing of the epidemic. A long-term commitment of resources and support will be necessary to address the outbreak.
Background: Several monoclonal antibodies (mAb) are being evaluated as treatment options for the current 2014 Ebola outbreak. But they were derived from and tested for protection against the older 1976 Mayinga or 1995 Kikwit Zaire Ebolaviruses (EBOV). The EBOV sequences reported for the current outbreak contain several mutations whose significance remained to be established.
Methods: We analyzed sequence and structural conservation of the Ebolavirus glycoprotein (GP) epitopes for all experimentally identified protective mAbs published to date.
Results: The conservancy analysis of protective mAb epitopes in the Ebolavirus glycoprotein sequences spanning all Ebola virus lineages since 1976 showed that conservancy within the Zaire EBOV lineage was high, with only one immunodominant epitope of mAb 13F6-1-2 acquiring two novel mutations in the 2014 outbreak that might potentially change the antibody specificity and neutralization activity. However, the conservation to other Ebola viruses was unexpectedly low.
Conclusion: Low conservancy of Zaire EBOV mAb epitopes to other EBOV lineages suggests that these epitopes are not indispensable for viral fitness, and that alternative mAbs could be developed to broadly target all EBOV. However, average percent sequence identity of the epitopes for mAbs used in current cocktails to the Zaire EBOV is high with only one epitope differing in the 2014 outbreak. These data bode well for general usefulness of these antibodies in the context of the current outbreak.
The current West African Ebola outbreak poses an unprecedented public health challenge for the world at large. The response of the global community to the epidemic, including deployment of nurses, doctors, epidemiologists, beds, supplies and security, is shaped by our understanding of the spatial-temporal extent and progression of the disease. Ongoing evaluation of the epidemiological characteristics and future course of the Ebola outbreak is needed to stay abreast of any changes to its transmission dynamics, as well as the success or failure of intervention efforts. Here we use observations, dynamic modeling and Bayesian inference to generate simulations and weekly forecasts of the outbreaks in Guinea, Liberia and Sierra Leone. Estimates of key epidemiological characteristics over time indicate continued epidemic growth in West Africa, though there is some evidence of slowing growth in Liberia. 6-week forecasts over successive weeks corroborate these findings; forecasts projecting no future change in intervention efficacy have been more accurate for Guinea and Sierra Leone, but have overestimated incidence and mortality for Liberia.
Background: The Ebola virus (EBOV) epidemic in Western Africa is the largest in recorded history and control efforts have so far failed to stem the rapid growth in the number of infections. Mathematical models serve a key role in estimating epidemic growth rates and the reproduction number (R0) from surveillance data and, recently, molecular sequence data. Phylodynamic analysis of existing EBOV time-stamped sequence data may provide independent estimates of the unobserved number of infections, reveal recent epidemiological history, and provide insight into selective pressures acting upon viral genes.
Methods: We fit a series mathematical models of infectious disease dynamics to phylogenies estimated from 78 whole EBOV genomes collected from distinct patients in May and June of 2014 in Sierra Leone, and perform evolutionary analysis on these genomes combined with closely related EBOV genomes from previous outbreaks. Two analyses are conducted with values of the latent period that have been used in recent modelling efforts. We also examined the EBOV sequences for evidence of possible episodic adaptive molecular evolution during the 2014 outbreak.
Results: We find evidence for adaptive evolution affecting L and GP protein coding regions of the EBOV genome, which is unlikely to bias molecular clock and phylodynamic analyses. We estimate R0=2.40 (95% HPD:1.54-3.87 ) if the mean latent period is 5.3 days, and R0=3.81, (95% HPD:2.47-6.3) if the mean latent period is 12.7 days. The estimated coefficient of variation (CV) of the number of transmissions per infected host is very high, and a large proportion of infections yield no transmissions.
Conclusions: Estimates of R0 are sensitive to the unknown latent infectious period which can not be reliably estimated from genetic data alone. EBOV phylogenies show significant evidence for superspreading and extreme variance in the number of transmissions per infected individual during the early epidemic in Sierra Leone.