Search

Background Health care workers (HCW) are more likely to be exposed to Ebola virus (EBOV) during an outbreak compared to people in the general population due to close physical contact with patients and potential exposure to infectious fluids. However, not all will fall ill. Despite evidence of subclinical and paucisymptomatic Ebola virus disease (EVD), prevalence and associated risk factors remain unknown. Methods We conducted a serosurvey among HCW in Boende, Tshuapa Province, Democratic Republic of Congo. Human anti-EBOV glycoprotein IgG titers were measured using a commercially available ELISA kit. We assessed associations between anti-EBOV IgG seroreactivity, defined as ≥2.5 units/mL, and risk factors using univariable and multivariable logistic regression. Sensitivity analyses explored a more conservative cutoff, >5 units/mL. Results Overall, 22.5% of HCWs were seroreactive for EBOV. In multivariable analyses, using any form of personal protective equipment when interacting with a confirmed, probable, or suspect EVD case was negatively associated with seroreactivity (adjusted odds ratio, 0.23; 95% confidence interval, .07–.73). Discussion Our results suggest high exposure to EBOV among HCWs and provide additional evidence for asymptomatic or minimally symptomatic EVD. Further studies should be conducted to determine the probability of onward transmission and if seroreactivity is associated with immunity.

Indigenous populations often have poorer health outcomes than the general population. Marginalization, colonization, and migration from traditional lands have all affected traditional medicine usage, health access, and indigenous health equity. An in-depth understanding of health for specific populations is essential to develop actionable insights into contributing factors to poor indigenous health. To develop a more complete, nuanced understanding of indigenous health status, we conducted first-person interviews with both the indigenous Baka and neighboring Bantu villagers (the reference population in the region), as well as local clinicians in Southern Cameroon. These interviews elucidated perspectives on the most pressing challenges to health and assets to health for both groups, including access to health services, causes of illness, the uses and values of traditional versus modern medicine, and community resilience during severe health events. Baka interviewees, in particular, reported facing health challenges due to affordability and discrimination in public health centers, health effects due to migration from their traditional lands, and a lack of culturally appropriate public health services.

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., A time-series study of the 2018–2020 Ebola virus disease outbreak in the Democratic Republic of the Congo found both Ebola-targeted and untargeted violence increased transmission, though targeted violence, primarily driven by civilian-involved events, had the largest impact.

Background Varicella zoster virus (VZV) causes both varicella (chickenpox) and herpes zoster (shingles) and is associated with significant global morbidity. Most epidemiological data on VZV come from high-income countries, and to date there are limited data on the burden of VZV in Africa. Methods We assessed the seroprevalence of VZV antibodies among children in the Democratic Republic of Congo in collaboration with the 2013-2014 Demographic and Health Survey. Dried blood spot samples collected from children 6-59 months of age were run on Dynex™ Technologies Multiplier FLEX® chemiluminescent immunoassay platform to assess serologic response. Multivariate logistic regression was then used to determine risk factors for VZV seropositivity. Results Serologic and survey data were matched for 7,195 children 6-59 months of age, among whom 8% were positive and 2% indeterminate for VZV antibodies in weighted analyses. In multivariate analyses, the odds of seropositivity increased with increasing age, increasing socioeconomic status, mother's education level, rural residence, and province (South Kivu, North Kivu, Bandundu, Bas Congo had the highest odds of a positive test result compared with Kinshasa). Conclusion Our data suggest that VZV is circulating in DRC, and seropositivity is low among children 6-59 months. Seropositivity increased with age and varied by other sociodemographic factors, such as geographic location. This study provides the first nationally representative estimates of VZV infection among children in the DRC.

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.

BackgroundAs of May 27, 2018, 54 cases of Ebola virus disease (EVD) were reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the current outbreak size and duration with and without vaccine use.MethodsWe modeled Ebola virus transmission using a stochastic branching process model with a negative binomial distribution, using both estimates of reproduction number R declining from supercritical to subcritical derived from past Ebola outbreaks, as well as a particle filtering method to generate a probabilistic projection of the future course of the outbreak conditioned on its reported trajectory to date; modeled using 0%, 44%, and 62% estimates of vaccination coverage. Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize a regression model predicting the outbreak size from the number of observed cases from April 4 to May 27.ResultsWith the stochastic transmission model, we projected a median outbreak size of 78 EVD cases (95% credible interval: 52, 125.4), 86 cases (95% credible interval: 53, 174.3), and 91 cases (95% credible interval: 52, 843.5), using 62%, 44%, and 0% estimates of vaccination coverage. With the regression model, we estimated a median size of 85.0 cases (95% prediction interval: 53.5, 216.6).ConclusionsThis outbreak has the potential to be the largest outbreak in DRC since 2007. Vaccines are projected to limit outbreak size and duration but are only part of prevention, control, and care strategies.

Experimental studies have suggested a larger inoculum of monkeypox virus may be associated with increased rash severity; however, little data are available on the relationship between specific animal exposures and rash severity in endemic regions. Using cross-sectional data from an active surveillance program conducted between 2005 and 2007 in the Sankuru Province of the Democratic Republic of the Congo, we explored the possible relationship between rash severity and exposures to rodents and non-human primates among confirmed MPX cases. Among the 223 PCR-confirmed MPX cases identified during active surveillance, the majority of cases (n = 149) presented with mild rash (5–100 lesions) and 33% had a more serious presentation (> 100 lesions). No association between exposure to rodents and rash severity was found in the multivariable analysis. Those that self-reported hunting NHP 3 weeks prior to onset of MPX symptoms had 2.78 times the odds of severe rash than those that did not report such exposure (95% CI: 1.18, 6.58). This study provides a preliminary step in understanding the association between animal exposure and rash severity and demonstrates correlation with exposure to NHPs and human MPX presentation. Additional research exploring the relationship between rash severity and NHPs is warranted.

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. Keywords: Ebola virus disease, Outbreak, Mathematical modeling, Geospatial, Democratic Republic of Congo, Africa