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Despite more than 300,000 rVSVΔG-ZEBOV-glycoprotein (GP) vaccine doses having been administered during Ebola virus disease (EVD) outbreaks in the Democratic Republic of the Congo (DRC) between 2018 and 2020, seroepidemiologic studies of vaccinated Congolese populations are lacking. This study examines the antibody response at 21 d and 6 mo postvaccination after single-dose rVSVΔG-ZEBOV-GP vaccination among EVD-exposed and potentially exposed populations in the DRC. We conducted a longitudinal cohort study of 608 rVSVΔG-ZEBOV-GP-vaccinated individuals during an EVD outbreak in North Kivu Province, DRC. Participants provided questionnaires and blood samples at three study visits (day 0, visit 1; day 21, visit 2; and month 6, visit 3). Anti-GP immunoglobulin G (IgG) antibody titers were measured in serum by the Filovirus Animal Nonclinical Group anti-Ebola virus GP IgG enzyme-linked immunosorbent assay. Antibody response was defined as an antibody titer that had increased fourfold from visit 1 to visit 2 and was above four times the lower limit of quantification at visit 2; antibody persistence was defined as a similar increase from visit 1 to visit 3. We then examined demographics for associations with follow-up antibody titers using generalized linear mixed models. A majority of the sample, 87.2%, had an antibody response at visit 2, and 95.6% demonstrated antibody persistence at visit 3. Being female and of young age was predictive of a higher antibody titer postvaccination. Antibody response and persistence after Ebola vaccination was robust in this cohort, confirming findings from outside of the DRC., Competing Interests: The authors declare no competing interest., (Copyright © 2022 the Author(s). Published by PNAS.)

Background: Our understanding of the different effects of targeted versus nontargeted violence on Ebola virus (EBOV) transmission in Democratic Republic of the Congo (DRC) is limited., Methods: We used time-series data of case counts to compare individuals in Ebola-affected health zones in DRC, April 2018-August 2019. Exposure was number of violent events per health zone, categorized into Ebola-targeted or Ebola-untargeted, and into civilian-induced, (para)military/political, or protests. Outcome was estimated daily reproduction number (Rt) by health zone. We fit linear time-series regression to model the relationship., Results: Average Rt was 1.06 (95% confidence interval [CI], 1.02-1.11). A mean of 2.92 violent events resulted in cumulative absolute increase in Rt of 0.10 (95% CI, .05-.15). More violent events increased EBOV transmission (P = .03). Considering violent events in the 95th percentile over a 21-day interval and its relative impact on Rt, Ebola-targeted events corresponded to Rt of 1.52 (95% CI, 1.30-1.74), while civilian-induced events corresponded to Rt of 1.43 (95% CI, 1.21-1.35). Untargeted events corresponded to Rt of 1.18 (95% CI, 1.02-1.35); among these, militia/political or ville morte events increased transmission., Conclusions: Ebola-targeted violence, primarily driven by civilian-induced events, had the largest impact on EBOV transmission., (© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America.)

Introduction: As of April 2019, the current Ebola virus disease (EVD) outbreak in the Democratic Republic of the Congo (DRC) is occurring in a longstanding conflict zone and has become the second largest EVD outbreak in history. It is suspected that after violent events occur, EVD transmission will increase; however, empirical studies to understand the impact of violence on transmission are lacking. Here, we use spatial and temporal trends of EVD case counts to compare transmission rates between health zones that have versus have not experienced recent violent events during the outbreak., Methods: We collected daily EVD case counts from DRC Ministry of Health. A time-varying indicator of recent violence in each health zone was derived from events documented in the WHO situation reports. We used the Wallinga-Teunis technique to estimate the reproduction number R for each case by day per zone in the 2018-2019 outbreak. We fit an exponentially decaying curve to estimates of R overall and by health zone, for comparison to past outbreaks., Results: As of 16 April 2019, the mean overall R for the entire outbreak was 1.11. We found evidence of an increase in the estimated transmission rates in health zones with recently reported violent events versus those without (p = 0.008). The average R was estimated as between 0.61 and 0.86 in regions not affected by recent violent events, and between 1.01 and 1.07 in zones affected by violent events within the last 21 days, leading to an increase in R between 0.17 and 0.53. Within zones with recent violent events, the mean estimated quenching rate was lower than for all past outbreaks except the 2013-2016 West African outbreak., Conclusion: The difference in the estimated transmission rates between zones affected by recent violent events suggests that violent events are contributing to increased transmission and the ongoing nature of this outbreak., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: As of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak., Methods: For short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott's rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts., Results: During validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872-1054) and 955 cases by March 4 (95% prediction interval: 874-1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876-933) and 898 (95% prediction interval: 877-983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013-2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone., Conclusions: Our projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges., Competing Interests: The authors have declared that no competing interests exist.

Background: Clinical sequelae of Ebola virus disease (EVD) have not been described more than 3 years postoutbreak. We examined survivors and close contacts from the 1995 Ebola outbreak in Kikwit, Democratic Republic of Congo (DRC), and determined prevalence of abnormal neurological, cognitive, and psychological findings and their association with EVD survivorship., Methods: From August to September 2017, we conducted a cross-sectional study in Kikwit, DRC. Over 2 decades after the EVD outbreak, we recruited EVD survivors and close contacts from the outbreak to undergo physical examination and culturally adapted versions of the Folstein mini-mental status exam (MMSE) and Goldberg anxiety and depression scale (GADS). We estimated the strength of relationships between EVD survivorship and health outcomes using linear regression models by comparing survivors versus close contacts, adjusting for age, sex, educational level, marital status, and healthcare worker status., Results: We enrolled 20 EVD survivors and 187 close contacts. Among the 20 EVD survivors, 4 (20%) reported at least 1 abnormal neurological symptom, and 3 (15%) had an abnormal neurological examination. Among the 187 close contacts, 14 (11%) reported at least 1 abnormal neurologic symptom, and 9 (5%) had an abnormal neurological examination. EVD survivors had lower mean MMSE and higher mean GADS scores as compared to close contacts (MMSE: adjusted coefficient: -1.85; 95% confidence interval [CI]: -3.63, -0.07; GADS: adjusted coefficient: 3.91; 95% CI: 1.76, 6.04)., Conclusions: EVD survivors can have lower cognitive scores and more symptoms of depression and anxiety than close contacts more than 2 decades after Ebola virus outbreaks., (© The Author(s) 2018. Published by Oxford University Press for the Infectious Diseases Society of America.)

As of May 27, 2018, 6 suspected, 13 probable and 35 confirmed cases of Ebola virus disease (EVD) had been reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the total outbreak size and duration with and without vaccine use. We modeled Ebola virus transmission using a stochastic branching process model that included reproduction numbers from past Ebola outbreaks and a particle filtering method to generate a probabilistic projection of the outbreak size and duration conditioned on its reported trajectory to date; modeled using high (62%), low (44%), and zero (0%) estimates of vaccination coverage (after deployment). Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize the Thiel-Sen regression model predicting the outbreak size from the number of observed cases from April 4 to May 27. We used these techniques on probable and confirmed case counts with and without inclusion of suspected cases. Probabilistic projections were scored against the actual outbreak size of 54 EVD cases, using a log-likelihood score. With the stochastic model, using high, low, and zero estimates of vaccination coverage, the median outbreak sizes for probable and confirmed cases were 82 cases (95% prediction interval [PI]: 55, 156), 104 cases (95% PI: 58, 271), and 213 cases (95% PI: 64, 1450), respectively. With the Thiel-Sen regression model, the median outbreak size was estimated to be 65.0 probable and confirmed cases (95% PI: 48.8, 119.7). Among our three mathematical models, the stochastic model with suspected cases and high vaccine coverage predicted total outbreak sizes closest to the true outcome. Relatively simple mathematical models updated in real time may inform outbreak response teams with projections of total outbreak size and duration., Competing Interests: The authors have declared that no competing interests exist.