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BackgroundThere is limited evidence to evaluate screening algorithms with rapid antigen testing and exposure assessments as identification strategies for paucisymptomatic or asymptomatic Ebola virus (EBOV) infection and unrecognized EBOV disease (EVD).MethodsWe used serostatus and self-reported postexposure symptoms from a cohort study to classify contact-participants as having no infection, paucisymptomatic or asymptomatic infection, or unrecognized EVD. Exposure risk was categorized as low, intermediate, or high. We created hypothetical scenarios to evaluate the World Health Organization (WHO) case definition with or without rapid diagnostic testing (RDT) or exposure assessments.ResultsThis analysis included 990 EVD survivors and 1909 contacts, of whom 115 (6%) had paucisymptomatic or asymptomatic EBOV infection, 107 (6%) had unrecognized EVD, and 1687 (88%) were uninfected. High-risk exposures were drivers of unrecognized EVD (adjusted odds ratio, 3.5 [95% confidence interval, 2.4-4.9]). To identify contacts with unrecognized EVD who test negative by the WHO case definition, the sensitivity was 96% with RDT (95% confidence interval, 91%-99%), 87% with high-risk exposure (82%-92%), and 97% with intermediate- to high-risk exposures (93%-99%). The proportion of false-positives was 2% with RDT and 53%-93% with intermediate- and/or high-risk exposures.ConclusionWe demonstrated the utility and trade-offs of sequential screening algorithms with RDT or exposure risk assessments as identification strategies for contacts with unrecognized EVD.

BackgroundTransmission by unreported cases has been proposed as a reason for the 2013-2016 Ebola virus (EBOV) epidemic decline in West Africa, but studies that test this hypothesis are lacking. We examined a transmission chain within social networks in Sukudu village to assess spread and transmission burnout.MethodsNetwork data were collected in 2 phases: (1) serological and contact information from Ebola cases (n = 48, including unreported); and (2) interviews (n = 148), including Ebola survivors (n = 13), to identify key social interactions. Social links to the transmission chain were used to calculate cumulative incidence proportion as the number of EBOV-infected people in the network divided by total network size.ResultsThe sample included 148 participants and 1522 contacts, comprising 10 social networks: 3 had strong links (>50% of cases) to the transmission chain: household sharing (largely kinship), leisure time, and talking about important things (both largely non-kin). Overall cumulative incidence for these networks was 37 of 311 (12%). Unreported cases did not have higher network centrality than reported cases.ConclusionsAlthough this study did not find evidence that explained epidemic decline in Sukudu, it excluded potential reasons (eg, unreported cases, herd immunity) and identified 3 social interactions in EBOV transmission.

BackgroundIt remains unclear if there is a dose-dependent relationship between exposure risk to Ebola virus (EBOV) and severity of illness.MethodsFrom September 2016 to July 2017, we conducted a cross-sectional, community-based study of Ebola virus disease (EVD) cases and household contacts of several transmission chains in Kono District, Sierra Leone. We analyzed 154 quarantined households, comprising both reported EVD cases and their close contacts. We used epidemiological surveys and blood samples to define severity of illness as no infection, pauci-/asymptomatic infection, unrecognized EVD, reported EVD cases who survived, or reported EVD decedents. We determine seropositivity with the Filovirus Animal Nonclinical Group EBOV glycoprotein immunoglobulin G antibody test. We defined levels of exposure risk from 8 questions and considered contact with body fluid as maximum exposure risk.ResultsOur analysis included 76 reported EVD cases (both decedents and survivors) and 421 close contacts. Among these contacts, 40 were seropositive (22 paucisymptomatic and 18 unrecognized EVD), accounting for 34% of the total 116 EBOV infections. Higher exposure risks were associated with having had EBOV infection (maximum risk: adjusted odds ratio [AOR], 12.1 [95% confidence interval {CI}, 5.8-25.4; trend test: P

BackgroundOur understanding of the different effects of targeted versus nontargeted violence on Ebola virus (EBOV) transmission in Democratic Republic of the Congo (DRC) is limited.MethodsWe 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.ResultsAverage 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.ConclusionsEbola-targeted violence, primarily driven by civilian-induced events, had the largest impact on EBOV transmission.

BackgroundOur understanding of the different effects of targeted versus nontargeted violence on Ebola virus (EBOV) transmission in Democratic Republic of the Congo (DRC) is limited.MethodsWe 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.ResultsAverage 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.ConclusionsEbola-targeted violence, primarily driven by civilian-induced events, had the largest impact on EBOV transmission.

IntroductionAs 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.MethodsWe 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.ResultsAs 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.ConclusionThe 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.

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.

Studies have shown that people suffering from food insecurity are at higher risk for infectious and noncommunicable diseases and have poorer health outcomes. No study, however, has examined the association between food insecurity and outcomes related to Ebola virus disease (EVD). We conducted a cross-sectional study in two Ebola-affected communities in Kono district, Sierra Leone, from November 2015 to September 2016. We enrolled persons who were determined to have been exposed to Ebola virus. We assessed the association of food insecurity, using an adapted version of the Household Food Insecurity Access Scale, a nine-item scale well validated across Africa, with having been diagnosed with EVD and having died of EVD, using logistic regression models with cluster-adjusted standard errors. We interviewed 326 persons who were exposed to Ebola virus; 61 (19%) were diagnosed with EVD and 45/61 (74%) died. We found high levels (87%) of food insecurity, but there was no association between food insecurity and having been diagnosed with EVD. Among EVD cases, those who were food insecure had 18.3 times the adjusted odds of death than those who were food secure (P = 0.03). This is the first study to demonstrate a potential relationship between food insecurity and having died of EVD, although larger prospective studies are needed to confirm these findings.

Studies have yet to include minimally symptomatic Ebola virus (EBOV) infections and unrecognized Ebola virus disease (EVD) in Ebola-related transmission chains and epidemiologic risk estimates. We conducted a cross-sectional, sero-epidemiological survey from October 2015 to January 2016 among 221 individuals living in quarantined households from November 2014 to February 2015 during the Ebola outbreak in the village of Sukudu, Sierra Leone. Of 48 EBOV-infected persons, 25% (95% confidence interval [CI], 14%-40%) had minimally symptomatic EBOV infections and 4% (95% CI, 1%-14%) were unrecognized EVD cases. The pattern of minimally symptomatic EBOV infections in the transmission chain was nonrandom (P < .001, permutation test). Not having lived in the same house as an EVD case was significantly associated with minimally symptomatic infection. This is the first study to investigate a chain of EBOV transmission inclusive of minimally symptomatic EBOV infections and unrecognized EVD. Our findings provide new insights into Ebola transmission dynamics and quarantine practices.

Introduction: The HIV pandemic disproportionately impacts young women. Worldwide, young women aged 15-24 are infected with HIV at rates twice that of young men, and young women alone account for nearly a quarter of all new HIV infections. The incommensurate HIV incidence in young--often poor--women underscores how social and economic inequalities shape the HIV epidemic. Confluent social forces, including political and gender violence, poverty, racism, and sexism impede equal access to therapies and effective care, but most of all constrain the agency of women. Methods: HIV prevalence data was compiled from the 2010 UNAIDS Global Report. Gender inequality was assessed using the 2011 United Nations Human Development Report Gender Inequality Index (GII). Logistic regression models were created with predominant mode of transmission (heterosexual vs. MSM/IDU) as the dependent variable and GII, Muslim vs. non-Muslim, Democracy Index, male circumcision rate, log gross national income (GNI) per capita at purchasing power parity (PPP), and region as independent variables. Results and discussion: There is a significant correlation between having a predominantly heterosexual epidemic and high gender inequality across all models. There is not a significant association between whether a country is predominantly Muslim, has a high/low GNI at PPP, has a high/low circumcision rate, and its primary mode of transmission. In addition, there are only three countries that have had a generalized epidemic in the past but no longer have one: Cambodia, Honduras, and Eritrea. GII data are available only for Cambodia and Honduras, and these countries showed a 37 and 34% improvement, respectively, in their Gender Inequality Indices between 1995 and 2011. During the same period, both countries reduced their HIV prevalence below the 1% threshold of a generalized epidemic. This represents limited but compelling evidence that improvements in gender inequality can lead to the abatement of generalized epidemics. Conclusions: Gender inequality is an important factor in the maintenance--and possibly in the establishment of--generalized HIV epidemics. We should view improvements in gender inequality as part of a broader public health strategy. Keywords: gender inequality; HIV; AIDS; structural interventions; political ecology., Introduction The HIV pandemic disproportionately impacts young women. Worldwide, women aged 15-24 are infected with HIV at rates twice that of young men, and young women alone account for nearly [...]