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BACKGROUND: Rubella vaccine has yet to be introduced into the national immunization schedule of the Democratic Republic of the Congo (DRC); the current burden of congenital rubella syndrome (CRS) is unknown and likely to be high. An important consideration prior to introducing rubella containing vaccine (RCV) is the potential inverse relationship between RCV coverage and CRS incidence. Increasing RCV coverage will also increase in the average age of infection. Cumulative infections across all age groups will decrease, but the number of infections in age groups vulnerable to CRS may increase. METHODS: Rubella transmission dynamics in the DRC were simulated using a stochastic agent-based model of transmission. Input parameter values for known properties, demographic variables, and interventions were fixed; infectivity was inferred from seropositivity profiles in survey data. RESULTS: Our simulations of RCV introduction for the DRC demonstrate that an increase in CRS burden is unlikely. Continued endemic transmission is only plausible when routine immunization coverage is less than 40% and follow-up supplemental immunization activities have poor coverage for decades. CONCLUSION: Increased vaccination coverage tends to increase the annual variability of CRS burden. Simulations examining low vaccination coverage and high mean CRS burden are outbreak prone, with multiple years of reduced burden followed by acute outbreaks. These outcomes contrast simulations with no vaccination coverage and high mean CRS burden, which have more consistent burden from year to year.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

Prenatal Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and Trichomonas vaginalis (TV) infections are associated with adverse birth outcomes. As rapid diagnostic tests become available, it is important to evaluate prenatal sexually transmitted infection (STI) prevalence, as well as the acceptability and feasibility of prenatal screening programs. We recruited 371 pregnant women from four clinics in Kisantu Health Zone, Democratic Republic of Congo (DRC) from October 2016 to March 2017. Trained clinicians collected cervical swabs, and samples were tested by nucleic acid amplification for CT, NG, and TV using a GeneXpert® system. Those testing positive for an STI were treated and asked to return after 4–8 weeks for tests-of-cure. Screening for STIs was widely accepted (99%). STI prevalence at baseline was: CT, 3.2%; NG, 1.5%; and TV, 14%; treatment completion was 97%. Symptoms were reported among 34% of STI-positive women at baseline, compared with 37% of STI-negative women. Upon first test-of-cure, 100% of returning women were cured of CT (n= 10) and NG (n= 5), but only 47% were cured of TV. This study demonstrates the feasibility of implementing diagnostic STI testing for case detection and treatment among expectant mothers in DRC, with implications for maternal and birth outcomes.

BackgroundIn order to prevent outbreaks from wild and vaccine-derived poliovirus, maintenance of population immunity in non-endemic countries is critical.MethodsWe estimated population seroprevalence using dried blood spots collected from 4893 children 6-59months olds in the 2013-2014 Demographic and Health Survey in the Democratic Republic of the Congo (DRC).ResultsPopulation immunity was 81%, 90%, and 70% for poliovirus types 1, 2, and 3, respectively. Among 6-59-month-old children, 78% reported at least one dose of polio in routine immunization, while only 15% had three doses documented on vaccination cards. All children in the study had been eligible for at least two trivalent oral polio vaccine campaigns at the time of enrollment; additional immunization campaigns seroconverted 5.0%, 14%, and 5.5% of non-immune children per-campaign for types 1, 2, and 3, respectively, averaged over relevant campaigns for each serotype.ConclusionsOverall polio immunity was high at the time of the study, though pockets of low immunity cannot be ruled out. The DRC still relies on supplementary immunization campaigns, and this report stresses the importance of the quality and coverage of those campaigns over their quantity, as well as the importance of routine immunization.

Schistosomiasis afflicts an estimated 10 million pregnant women in Africa annually. With mounting evidence of adverse impacts to reproductive health resulting from urogenital schistosomiasis, including increased transmission of HIV, further research on prenatal disease epidemiology is warranted, with implications for maternal and fetal health. Between October 2016 and March 2017, we conducted a cross-sectional study examining the prevalence of urogenital schistosomiasis and its association with sexually transmitted infections (STIs) other than HIV among pregnant women visiting antenatal clinics in Kisantu health zone, Democratic Republic of Congo. An extensive sociodemographic and clinical survey was administered to consenting participants, with urine samples and vaginal swabs collected to deduce active schistosomiasis and STIs, respectively. In total, 17.4% of expectant mothers were infected with Schistosoma haematobium, 3.1% with Chlamydia trachomatis (CT), 1.4% with Neisseria gonorrhoeae (NG), and 14.6% with Trichomonas vaginalis (TV). Women infected with urogenital schistosomiasis were at significantly increased odds of harboring a CT, NG, or TV infection (adjusted odds ratio = 3.0, 95% CI: 1.5, 6.0), but reports of clinical symptoms were low, ranging from 17.2% of schistosomiasis to 30.8% of TV cases. Laboratory confirmation of schistosomiasis and STIs provided objective evidence of disease in a cohort with low symptomology where syndromic management may not suffice. Shedding light on local risk factors and associated coinfections of urogenital schistosomiasis can identify unique intervention opportunities for prenatal care in trematode-endemic regions and aid in reducing adverse pregnancy outcomes.

IntroductionOne of the goals of the Global Measles and Rubella Strategic Plan is the reduction in global measles mortality, with high measles vaccination coverage as one of its core components. While measles mortality has been reduced more than 79%, the disease remains a major cause of childhood vaccine preventable disease burden globally. Measles immunization requires a two-dose schedule and only countries with strong, stable immunization programs can rely on routine services to deliver the second dose. In the Democratic Republic of Congo (DRC), weak health infrastructure and lack of provision of the second dose of measles vaccine necessitates the use of supplementary immunization activities (SIAs) to administer the second dose.MethodsWe modeled three vaccination strategies using an age-structured SIR (Susceptible-Infectious-Recovered) model to simulate natural measles dynamics along with the effect of immunization. We compared the cost-effectiveness of two different strategies for the second dose of Measles Containing Vaccine (MCV) to one dose of MCV through routine immunization services over a 15-year time period for a hypothetical birth cohort of 3 million children.ResultsCompared to strategy 1 (MCV1 only), strategy 2 (MCV2 by SIA) would prevent a total of 5,808,750 measles cases, 156,836 measles-related deaths and save U.S. $199 million. Compared to strategy 1, strategy 3 (MCV2 by RI) would prevent a total of 13,232,250 measles cases, 166,475 measles-related deaths and save U.S. $408 million.DiscussionVaccination recommendations should be tailored to each country, offering a framework where countries can adapt to local epidemiological and economical circumstances in the context of other health priorities. Our results reflect the synergistic effect of two doses of MCV and demonstrate that the most cost-effective approach to measles vaccination in DRC is to incorporate the second dose of MCV in the RI schedule provided that high enough coverage can be achieved.

BackgroundMeasles is a significant contributor to child mortality in the Democratic Republic of the Congo (DRC), despite routine immunization programs and supplementary immunization activities (SIA). Further, national immunization coverage levels may hide disparities among certain groups of children, making effective measles control even more challenging. This study describes measles vaccination coverage and reporting methods and identifies predictors of vaccination among children participating in the 2013-2014 DRC Demographic and Health Survey (DHS).MethodsWe examined vaccination coverage of 6947 children aged 6-59 months. A multivariate logistic regression model was used to identify predictors of vaccination among children reporting vaccination via dated card in order to identify least reached children. We also assessed spatial distribution of vaccination report type by rural versus urban residence.ResultsUrban children with educated mothers were more likely to be vaccinated (OR = 4.1, 95% CI: 1.6, 10.7) versus children of mothers with no education, as were children in wealthier rural families (OR = 2.9, 95% CI: 1.9, 4.4). At the provincial level, urban areas more frequently reported vaccination via dated card than rural areas.ConclusionsResults indicate that, while the overall coverage level of 70% is too low, socioeconomic and geographic disparities also exist which could make some children even less likely to be vaccinated. Dated records of measles vaccination must be increased, and groups of children with the greatest need should be targeted. As access to routine vaccination services is limited in DRC, identifying and targeting under-reached children should be a strategic means of increasing country-wide effective measles control.

•DRC's overall measles vaccination coverage level of 70% is too low to halt the spread of measles.•Socioeconomic variables and residence are associated with vaccination coverage disparities.•Vaccination coverage and data quality are linked, and as such, dated records must be increased.

The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.

By analyzing vesicle fluids and crusted scabs from 136 persons with suspected monkeypox, we identified 51 cases of monkeypox by PCR, sequenced the hemagglutinin gene, and confirmed 94% of cases by virus culture. PCR demonstrated chickenpox in 61 patients. Coinfection with both viruses was found in 1 additional patient.

By analyzing vesicle fluids and crusted scabs from 136 persons with suspected monkeypox, we identified 51 cases of monkeypox by PCR, sequenced the hemagglutinin gene, and confirmed 94% of cases by virus culture. PCR demonstrated chickenpox in 61 patients. Coinfection with both viruses was found in 1 additional patient.

BackgroundAs 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.MethodsFor 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.ResultsDuring 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.ConclusionsOur 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.

BackgroundAs 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.MethodsFor 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.ResultsDuring 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 ...

Despite increased vaccination rates, the burden, morbidity and mortality associated with vaccine preventable diseases remains high. In the Democratic Republic of the Congo (DRC), potentially unreliable data and geographically varied program provision call for a better understanding of vaccination coverage and its changes over time at the country and province level. To assess changes in the proportion of children who were fully vaccinated over time in the DRC, vaccination histories for children 12–59 months of age were obtained from both the 2007 and 2013–2014 Demographic and Health Surveys (DHS). Changes were assessed, both at the country- and province-levels, to identify potential geographic variations. Vaccination coverage improved 70% between the DHS waves: 26% compared to 44% of 12–59 month-old children met full vaccination criteria in 2007 and 2013–2014, respectively (n(2007) = 3032 and n(2013-14) = 6619). Similarly, there was an overall trend across both DHS waves where as year of birth increased, so did vaccination coverage. There was geographic variation in immunization changes with most central and eastern provinces increasing in coverage and most northern, western and southern provinces having decreased vaccination coverage at the second time point. Using nationally representative data, we identified significant changes over time in vaccination coverage which may help to inform future policy, interventions and research to improve vaccination rates among children in the DRC. This study is the first of its kind for the population of DRC and provides an important initial step towards better understanding trends in vaccination coverage over time.