Search

Studies of two SARS-CoV-2 mRNA vaccines suggested that they yield ∼95% protection from symptomatic infection at least short-term, but important clinical questions remain. It is unclear how vaccine-induced antibody levels quantitatively compare to the wide spectrum induced by natural SARS-CoV-2 infection. Vaccine response kinetics and magnitudes in persons with prior COVID-19 compared to virus-naı̈ve persons are not well-defined. The relative stability of vaccine-induced versus infection-induced antibody levels is unclear. We addressed these issues with longitudinal assessments of vaccinees with and without prior SARS-CoV-2 infection using quantitative enzyme-linked immunosorbent assay (ELISA) of anti-RBD antibodies. SARS-CoV-2-naı̈ve individuals achieved levels similar to mild natural infection after the first vaccination; a second dose generated levels approaching severe natural infection. In persons with prior COVID-19, one dose boosted levels to the high end of severe natural infection even in those who never had robust responses from infection, increasing no further after the second dose. Antiviral neutralizing assessments using a spike-pseudovirus assay revealed that virus-naı̈ve vaccinees did not develop physiologic neutralizing potency until the second dose, while previously infected persons exhibited maximal neutralization after one dose. Finally, antibodies from vaccination waned similarly to natural infection, resulting in an average of ∼90% loss within 90 days. In summary, our findings suggest that two doses are important for quantity and quality of humoral immunity in SARS-CoV-2-naı̈ve persons, while a single dose has maximal effects in those with past infection. Antibodies from vaccination wane with kinetics very similar to that seen after mild natural infection; booster vaccinations will likely be required.

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.

We conducted a serologic survey of 2,430 serum samples collected during 1997-2012 for various studies to determine the prevalence of the hemorrhagic fever virus Ebola virus (EBOV) in equatorial Africa. We screened serum samples for neutralizing antibodies by using a pseudotype microneutralization assay and a newly developed luciferase immunoprecipitation system assay. Specimens seroreactive for EBOV were confirmed by using an ELISA. Our results suggest a serologic prevalence of 2%-3.5% in the Republic of the Congo and the Democratic Republic of the Congo, which have reported outbreaks of infection with EBOV. In addition we detected a seroprevalence of 1.3% in southern Cameroon, which indicated a low risk for exposure in this region.

Background Sentiments of vaccine hesitancy and distrust in public health institutions have complicated the government-led coronavirus disease 2019 (COVID-19) vaccine control strategy in the United States. As the first to receive the vaccine, COVID-19 vaccine attitudes among frontline workers are consequential for COVID-19 control and public opinion of the vaccine. Methods In this study, we used a repeated cross-sectional survey administered at 3 time points between 24 September 2020 and 6 February 2021 to a cohort of employees of the University of California, Los Angeles Health and the Los Angeles County Fire Department. The primary outcome of interest was COVID-19 vaccination intent and vaccine uptake. Results Confidence in COVID-19 vaccines and vaccine uptake rose significantly over time. At survey 1, confidence in vaccine protection was 46.4% among healthcare workers (HCWs) and 34.6% among first responders (FRs); by survey 3, this had risen to 90.0% and 75.7%, respectively. At survey 1, about one-third of participants intended to receive a vaccine as soon as possible. By survey 3, 96.0% of HCWs and 87.5% of FRs had received a COVID-19 vaccine. Conclusions Attitudes toward vaccine uptake increased over the study period, likely a result of increased public confidence in COVID-19 vaccines, targeted communications, a COVID-19 winter surge in Los Angeles County, and ease of access from employer-sponsored vaccine distribution.

Adenoviruses (AdVs) are diverse pathogens of humans and animals, with several dozen bat AdVs already identified. Considering that over 100 human AdVs are known, and the huge diversity of bat species, many bat AdVs likely remain undiscovered. To learn more about AdV prevalence, diversity and evolution, we sampled and tested bats in Cameroon using several PCR assays for viral and host DNA. AdV DNA was detected in 14 % of the 671 sampled animals belonging to 37 different bat species. There was a correlation between species roosting in larger groups and AdV DNA detection. The detected AdV DNA belonged to between 28 and 44 different, mostly previously unknown, mastadenovirus species. The novel isolates are phylogenetically diverse and while some cluster with known viruses, others appear to form divergent new clusters. The phylogenetic tree of novel and previously known bat AdVs does not mirror that of the various host species, but does contain structures consistent with a degree of virus–host co-evolution. Given that closely related isolates were found in different host species, it seems likely that at least some bat AdVs have jumped species barriers, probably in the more recent past; however, the tree is also consistent with such events having taken place throughout bat AdV evolution. AdV diversity was highest in bat species roosting in large groups. The study significantly increased the diversity of AdVs known to be harboured by bats, and suggests that host behaviours, such as roosting size, may be what limits some AdVs to one species rather than an inability of AdVs to infect other related hosts.

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., Author summary COVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian velocity model and a machine learning algorithm (random forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

Two mRNA vaccines (BNT162b2 and mRNA-1273) against severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) are globally authorized as a two-dose regimen. Understanding the magnitude and duration of protective immune responses is vital to curbing the pandemic. We enrolled 461 high-risk health services workers at the University of California, Los Angeles (UCLA) and first responders in the Los Angeles County Fire Department (LACoFD) to assess the humoral responses in previously infected (PI) and infection naïve (NPI) individuals to mRNA-based vaccines (BNT162b2/Pfizer- BioNTech or mRNA-1273/Moderna). A chemiluminescent microparticle immunoassay was used to detect antibodies against SARS-CoV-2 Spike in vaccinees prior to (n = 21) and following each vaccine dose (n = 246 following dose 1 and n = 315 following dose 2), and at days 31–60 (n = 110) and 61–90 (n = 190) following completion of the 2-dose series. Both vaccines induced robust antibody responses in all immunocompetent individuals. Previously infected individuals achieved higher median peak titers (p = 0.002) and had a slower rate of decay (p = 0.047) than infection-naïve individuals. mRNA-1273 vaccinated infection-naïve individuals demonstrated modestly higher titers following each dose (p = 0.005 and p = 0.029, respectively) and slower rates of antibody decay (p = 0.003) than those who received BNT162b2. A subset of previously infected individuals (25%) required both doses in order to reach peak antibody titers. The biologic significance of the differences between previously infected individuals and between the mRNA-1273 and BNT162b2 vaccines remains uncertain, but may have important implications for booster strategies.

The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people (“source control”) with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

Beginning April 8, 2020, we enrolled 1787 frontline heath workers who were asymptomatic for COVID-19 into a longitudinal surveillance study. During that time 4 healthcare workers and 6 first responders tested positive for SARS-CoV-2 by RT-PCR. Additionally, 43 healthcare workers and 55 first responders had detectable IgG antibodies to SARS-CoV-2.

ImportanceHealthcare workers (HCW) are slated to be early recipients of SARS-CoV-2 vaccines due to increased risk of exposure to patients with COVID-19, and will be tasked with administering approved vaccines to the general population. As lynchpins of the vaccination effort, HCWs’ opinions of a vaccine’s safety and efficacy may affect both public perception and uptake of the vaccine. Therefore, it is crucial to understand and address potential hesitancy prior to vaccine administration.ObjectiveTo understand healthcare workers’ attitudes about vaccine safety, efficacy, and acceptability in the context of the COVID-19 pandemic, including acceptance of a novel coronavirus vaccine.Design, Setting, ParticipantsA cross-sectional survey was distributed to participants enrolled in a longitudinal cohort study surveilling SARS-CoV-2 infection among 1,093 volunteer sampled University of California, Los Angeles (UCLA) Health System employees. Surveys were completed online between September 24 and October 16, 2020. In total, 609 participants completed this supplemental survey.ResultsWe averaged a 9-statement Likert scale matrix scored from 1 (“strongly disagree”) to 5 (“strongly agree”) and found respondents overwhelmingly confident about vaccine safety (4.47); effectiveness (4.44); importance, self-protection, and community health (4.67). Notably, 47.3% of respondents reported unwillingness to participate in a coronavirus vaccine trial, and most (66.5%) intend to delay vaccination. The odds of reporting intent to delay coronavirus vaccine uptake were 4.15 times higher among nurses, 2.45 times higher among other personnel with patient contact roles, and 2.15 times higher among those without patient contact compared to doctors. Evolving SARS-CoV-2 science (76.0%), current political climate (57.6%), and fast-tracked vaccine development timeline (83.4%) were cited as primary variables impacting HCW decisions to undergo vaccination. Of note, these results were obtained prior to release of Phase III data from companies manufacturing vaccines in the U.S.Conclusions and RelevanceDespite overall confidence in vaccines, a majority of HCW expressed concerns over a novel coronavirus vaccine. A large proportion plan to delay vaccine uptake due to concerns about expedited development, emerging scientific discoveries, and the political climate. Forthcoming vaccination campaigns must address these unique points of coronavirus vaccine hesitancy in order to achieve adequate vaccine coverage.

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 For many emerging or re-emerging pathogens, cases in humans arise from a mixture of introductions (via zoonotic spillover from animal reservoirs or geographic spillover from endemic regions) and secondary human-to-human transmission. Interventions aiming to reduce incidence of these infections can be focused on preventing spillover or reducing human-to-human transmission, or sometimes both at once, and typically are governed by resource constraints that require policymakers to make choices. Despite increasing emphasis on using mathematical models to inform disease control policies, little attention has been paid to guiding rational disease control at the animal-human interface. Methods We introduce a modeling framework to analyze the impacts of different disease control policies, focusing on pathogens exhibiting subcritical transmission among humans (i.e. pathogens that cannot establish sustained human-to-human transmission). We quantify the relative effectiveness of measures to reduce spillover (e.g. reducing contact with animal hosts), human-to-human transmission (e.g. case isolation), or both at once (e.g. vaccination), across a range of epidemiological contexts. Results We provide guidelines for choosing which mode of control to prioritize in different epidemiological scenarios and considering different levels of resource and relative costs. We contextualize our analysis with current zoonotic pathogens and other subcritical pathogens, such as post-elimination measles, and control policies that have been applied. Conclusions Our work provides a model-based, theoretical foundation to understand and guide policy for subcritical zoonoses, integrating across disciplinary and species boundaries in a manner consistent with One Health principles.

Coronaviruses play an important role as pathogens of humans and animals, and the emergence of epidemics like SARS, MERS and COVID-19 is closely linked to zoonotic transmission events primarily from wild animals. Bats have been found to be an important source of coronaviruses with some of them having the potential to infect humans, with other animals serving as intermediate or alternate hosts or reservoirs. Host diversity may be an important contributor to viral diversity and thus the potential for zoonotic events. To date, limited research has been done in Africa on this topic, in particular in the Congo Basin despite frequent contact between humans and wildlife in this region. We sampled and, using consensus coronavirus PCR-primers, tested 3,561 wild animals for coronavirus RNA. The focus was on bats (38%), rodents (38%), and primates (23%) that posed an elevated risk for contact with people, and we found coronavirus RNA in 121 animals, of which all but two were bats. Depending on the taxonomic family, bats were significantly more likely to be coronavirus RNA-positive when sampled either in the wet (Pteropodidae and Rhinolophidae) or dry season (Hipposideridae, Miniopteridae, Molossidae, and Vespertilionidae). The detected RNA sequences correspond to 15 Alpha- and 6 Beta-coronaviruses, with some of them being very similar (>95% nucleotide identities) to known coronaviruses and others being more unique and potentially representing novel viruses. In seven of the bats, we detected RNA most closely related to sequences of the human common cold coronaviruses 229E or NL63 (>80% nucleotide identities). The findings highlight the potential for coronavirus spillover, especially in regions with a high diversity of bats and close human contact, and reinforces the need for ongoing surveillance.

The Democratic Republic of the Congo (DRC) has a history of nonhuman primate (NHP) consumption and exposure to simian retroviruses yet little is known about the extent of zoonotic simian retroviral infections in DRC. We examined the prevalence of human T-lymphotropic viruses (HTLV), a retrovirus group of simian origin, in a large population of persons with frequent NHP exposures and a history of simian foamy virus infection. We screened plasma from 3,051 persons living in rural villages in central DRC using HTLV EIA and western blot (WB). PCR amplification of HTLV tax and LTR sequences from buffy coat DNA was used to confirm infection and to measure proviral loads (pVLs). We used phylogenetic analyses of LTR sequences to infer evolutionary histories and potential transmission clusters. Questionnaire data was analyzed in conjunction with serological and molecular data. A relatively high proportion of the study population (5.4%, n = 165) were WB seropositive: 128 HTLV-1-like, 3 HTLV-2-like, and 34 HTLV-positive but untypeable profiles. 85 persons had HTLV indeterminate WB profiles. HTLV seroreactivity was higher in females, wives, heads of households, and increased with age. HTLV-1 LTR sequences from 109 persons clustered strongly with HTLV-1 and STLV-1 subtype B from humans and simians from DRC, with most sequences more closely related to STLV-1 from Allenopithecus nigroviridis (Allen’s swamp monkey). While 18 potential transmission clusters were identified, most were in different households, villages, and health zones. Three HTLV-1-infected persons were co-infected with simian foamy virus. The mean and median percentage of HTLV-1 pVLs were 5.72% and 1.53%, respectively, but were not associated with age, NHP exposure, village, or gender. We document high HTLV prevalence in DRC likely originating from STLV-1. We demonstrate regional spread of HTLV-1 in DRC with pVLs reported to be associated with HTLV disease, supporting local and national public health measures to prevent spread and morbidity., Author summary HTLV-1 is a human retrovirus of zoonotic simian origin that has spread globally causing inflammatory and carcinogenic disease. We previously showed that persons with high nonhuman primate (NHP) exposure in the Democratic Republic of the Congo (DRC) can be infected with another simian retrovirus, simian foamy virus (SFV), suggesting they are also at risk for infection with STLV-1. We conducted follow-up analysis of the same persons from rural villages of central DRC to determine exposure and transmission risks for STLV-1 and HTLV-1 infection. Most persons, especially women, reported high levels of NHP exposure. We identified possible introduction and spread of STLV-1 from a local monkey species across households, villages, and health zones in DRC. Most persons had HTLV-1 levels above those reported in previous studies for persons with disease. Our findings reveal that coordinated public health strategies are needed at both local and national levels to prevent further spread and morbidity of HTLV-1.

Smallpox eradication, coordinated by the WHO and certified 40 years ago, led to the cessation of routine smallpox vaccination in most countries. It is estimated that over 70% of the world's population is no longer protected against smallpox, and through cross-immunity, to closely related orthopox viruses such as monkeypox. Monkeypox is now a re-emerging disease. Monkeypox is endemic in as yet unconfirmed animal reservoirs in sub-Saharan Africa, while its human epidemiology appears to be changing. Monkeypox in small animals imported from Ghana as exotic pets was at the origin of an outbreak of human monkeypox in the USA in 2003. Travellers infected in Nigeria were at the origin of monkeypox cases in the UK in 2018 and 2019, Israel in 2018 and Singapore in2019. Together with sporadic reports of human infections with other orthopox viruses, these facts invite speculation that emergent or re-emergent human monkeypox might fill the epidemiological niche vacated by smallpox. An ad-hoc and unofficial group of interested experts met to consider these issues at Chatham House, London in June 2019, in order to review available data and identify monkeypox-related research gaps. Gaps identified by the experts included:The experts further agreed on the need for a better understanding of the genomic evolution and changing epidemiology of orthopox viruses, the usefulness of in-field genomic diagnostics, and the best disease control strategies, including the possibility of vaccination with new generation non-replicating smallpox vaccines and treatment with recently developed antivirals.

The science around the use of masks by the general public to impede COVID-19 transmission is advancing rapidly. Policymakers need guidance on how masks should be used by the general population to combat the COVID-19 pandemic. In this narrative review, we develop an analytical framework to examine mask usage, considering and synthesizing the relevant literature to inform multiple areas: population impact; transmission characteristics; source control; PPE; sociological considerations; and implementation considerations. A primary route of transmission of COVID-19 is via respiratory droplets, and is known to be transmissible from presymptomatic and asymptomatic individuals. Reducing disease spread requires two things: first, limit contacts of infected individuals via physical distancing and other measures, and second, reduce the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces the transmissibility per contact by reducing transmission of infected droplets in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. The decreased transmissibility could substantially reduce the death toll and economic impact while the cost of the intervention is low. Given the current shortages of medical masks we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory droplets become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask-wearing by infectious people ("source control") with benefits at the population-level, rather than mask-wearing by susceptible people, such as health-care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

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 nonlinear mixed 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 transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. 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 infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. 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. Author summary COVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian nonlinear mixed model for case velocity and a machine learning algorithm (random forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We also identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

Key Points Question What are the ophthalmic sequelae in survivors of the 2013 to 2016 Ebola epidemic? Findings In this cohort study, no new Ebola retinal lesions were observed and visual acuity was preserved at 2 years; however, associated retinal dark without pressure did change with regression and expansion of affected zones. New clinical toxoplasmosis chorioretinal lesions occurred in 2 survivors. Meaning Expanding dark without pressure might suggest an ongoing intraretinal stimulus, which may be associated with a viral infection; treatment strategies might take account of the possibility of toxoplasmosis chorioretinitis recurrence within survivors of Ebola., Importance The 2-year ophthalmic sequelae of Ebola virus disease (EVD) in survivors of the 2013 to 2016 epidemic is unknown and may have public health implications for future outbreaks. Objective To assess the potential for uveitis recurrence, the behavior of dark without pressure, and visual outcomes in a cohort of Sierra Leonean survivors of EVD 2 years following the 2013 to 2016 Ebola epidemic. Design, Setting, and Participants Prospective, 1-year observational cohort study performed between 2016 and 2017 at 34 Military Hospital, Freetown, Sierra Leone. Participants included survivors of EVD who reported ocular symptoms since Ebola treatment unit discharge and were participants of a previous case-control study. Participants were invited for ophthalmic reexamination and finger-prick blood sampling for immunoglobulin G (IgG) to Toxoplasma gondii and HIV. Exposures Ebola virus disease. Main Outcomes and Measures Primary outcome measure: comparative ultra-widefield retinal imaging. Secondary outcome measures: visual acuity and detection of IgG to T gondii and HIV. Results Of 57 survivors of EVD who underwent repeated ophthalmic evaluation, 37 were women (64.9%). Mean (SD) age was 31.9 (11.1) years. Median interval between first and last examination was 370 days (interquartile range [IQR], 365-397.5 days), and median time from discharge to last examination was 779 days (IQR, 732-821 days). Fifteen eyes of 10 survivors (17.5%) had retinal lesions secondary to EVD. No new EVD-associated retinal lesions were observed. Two survivors (3.5%) developed new posterior uveitis resembling toxoplasmosis chorioretinitis and 41 (73%) were seropositive for T gondii IgG. Areas of dark without pressure were observed either confined to the perimeter of Ebola retinal lesions (n = 7) and non-Ebola lesions (n = 2), involving extensive retinal areas adjacent to Ebola retinal lesions (n = 4) and non-Ebola lesions (n = 2) or in isolation (n = 6). Both expansion and regression of areas of dark without pressure were observed over the study period. Best eye–presenting visual acuity had mild or no visual impairment in 55 survivors (96.4%) 2 years following discharge. Conclusions and Relevance Vision was maintained in survivors of EVD 2 years following discharge. Evolving regions of dark without pressure may be associated with EVD retinal lesions and might suggest the presence of an ongoing intraretinal stimulus, which may be associated with infective etiology. Treatment strategies should account for the possibility of toxoplasmosis chorioretinitis recurrence within survivors of EVD., This cohort study assesses the potential for uveitis recurrence, the behavior of dark without pressure, and visual outcomes in a cohort of Sierra Leonean survivors of Ebola virus disease 2 years following the 2013 to 2016 Ebola epidemic.

Smallpox eradication, coordinated by the WHO and certified just 40 years ago, led to the cessation of routine smallpox vaccination in most countries. It is estimated that over 70% of the world’s population is no longer protected against smallpox, and through cross-immunity, to closely related orthopox viruses such as monkeypox. Monkeypox is endemic in as yet unconfirmed animal reservoirs in sub-Saharan Africa, while its human epidemiology appears to be changing. Monkeypox in small animals imported from Ghana as exotic pets was at the origin of an outbreak of human monkeypox in the USA in 2003. Travellers infected in Nigeria were at the origin of monkeypox cases in the UK in 2018 and 2019, Israel in 2018 and Singapore in 2019. Together with sporadic reports of human infections with other orthopox viruses, these facts invite speculation that emergent or re-emergent human monkeypox might fill the epidemiological niche vacated by smallpox. An ad-hoc and unofficial group of interested experts met to consider these issues at Chatham House, London in June 2019, in order to review available data and identify monkeypox-related research gaps. Gaps identified by the experts included: understanding of zoonotic hosts, reservoirs and vectors risks associated with transmission, and full description of the clinical spectrum and the natural history of infection including an estimation of the prevalence of monkeypox specific antibodies in humans living in areas of emergence. The experts further agreed on the need for a better understanding of the genomic evolution and changing epidemiology of orthopox viruses, the usefulness of in-field genomic diagnostics, and the best disease control strategies, including the possibility of vaccination with new generation non-replicating smallpox vaccines and treatment with recently developed antivirals. Funding Statement: Chatham House provided a seminar venue and logistic support. Bavarian Nordic provided support for international travel. Declaration of Interests: Authors Elsgaard, Hochrein, Maclennan and Powell are employees of Bavarian Nordic, manufacturer of a vaccine registered as Jynneos for smallpox and monkeypox indications in the USA. Simpson works as a consultant for Bavarian Nordic. Ethics Approval Statement: Not required.

Background Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and Trichomonas vaginalis (TV) infections during pregnancy are linked with adverse birth outcomes. However, few countries have prenatal CT, NG, or TV screening programs. In this study, we aimed to evaluate the acceptability and feasibility of CT, NG, and TV screening and treatment among pregnant women across 6 low- to middle-income countries. Methods A total of 1817 pregnant women were screened for CT, NG, and TV in Botswana, the Democratic Republic of Congo, Haiti, South Africa, and Vietnam. An additional 640 pregnant women were screened for CT in Peru. Screening occurred between December 2012 and October 2017. Acceptability of screening was evaluated at each site as the proportion of eligible women who agreed to participate in screening. Feasibility of treatment was calculated as the proportion of women who tested positive that received treatment. Results Acceptability of screening and feasibility of treatment was high across all 6 sites. Acceptability of screening ranged from 85% to 99%, and feasibility of treatment ranged from 91% to 100%. Discussion The high acceptability and feasibility of screening and treatment of CT, NG, and TV among pregnant women supports further research to evaluate the cost-effectiveness of prenatal CT, NG, and TV screening programs.

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.

Highlights • Serosurvey data suggest R0 values for rubella in the DRC on the range 3 to 8. • Supplementary immunization activities provide multi-decade reduction in burden. • Post-vaccine introduction, burden will likely be concentrated in outbreaks., 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.

While investigating a signal of adaptive evolution in humans at the gene LARGE, we encountered an intriguing finding by Dr. Stefan Kunz that the gene plays a critical role in Lassa virus binding and entry. This led us to pursue field work to test our hypothesis that natural selection acting on LARGE—detected in the Yoruba population of Nigeria—conferred resistance to Lassa Fever in some West African populations. As we delved further, we conjectured that the “emerging” nature of recently discovered diseases like Lassa fever is related to a newfound capacity for detection, rather than a novel viral presence, and that humans have in fact been exposed to the viruses that cause such diseases for much longer than previously suspected. Dr. Stefan Kunz’s critical efforts not only laid the groundwork for this discovery, but also inspired and catalyzed a series of events that birthed Sentinel, an ambitious and large-scale pandemic prevention effort in West Africa. Sentinel aims to detect and characterize deadly pathogens before they spread across the globe, through implementation of its three fundamental pillars: Detect, Connect, and Empower. More specifically, Sentinel is designed to detect known and novel infections rapidly, connect and share information in real time to identify emerging threats, and empower the public health community to improve pandemic preparedness and response anywhere in the world. We are proud to dedicate this work to Stefan Kunz, and eagerly invite new collaborators, experts, and others to join us in our efforts.

Background Ebola virus (EBOV) is a zoonotic filovirus spread through exposure to infected bodily fluids of a human or animal. Though EBOV is capable of causing severe disease, referred to as Ebola Virus Disease (EVD), individuals who have never been diagnosed with confirmed, probable or suspected EVD can have detectable EBOV antigen-specific antibodies in their blood. This study aims to identify risk factors associated with detectable antibody levels in the absence of an EVD diagnosis. Methodology Data was collected from September 2015 to August 2017 from 1,366 consenting individuals across four study sites in the DRC (Boende, Kabondo-Dianda, Kikwit, and Yambuku). Seroreactivity was determined to EBOV GP IgG using Zaire Ebola Virus Glycoprotein (EBOV GP antigen) ELISA kits (Alpha Diagnostic International, Inc.) in Kinshasa, DRC; any result above 4.7 units/mL was considered seroreactive. Among the respondents, 113 (8.3%) were considered seroreactive. Several zoonotic exposures were associated with EBOV seroreactivity after controlling for age, sex, healthcare worker status, location, and history of contact with an EVD case, namely: ever having contact with bats, ever having contact with rodents, and ever eating non-human primate meat. Contact with monkeys or non-human primates was not associated with seroreactivity. Conclusions This analysis suggests that some zoonotic exposures that have been linked to EVD outbreaks can also be associated with EBOV GP seroreactivity in the absence of diagnosed EVD. Future investigations should seek to clarify the relationships between zoonotic exposures, seroreactivity, asymptomatic infection, and EVD., Author summary Ebola virus (EBOV) is spread through exposure to infected bodily fluids of a human or animal. While EBOV can lead to a severe disease, Ebola Virus Disease (EVD), it is possible for individuals to have anti-EBOV antibodies without ever getting sick with EVD. Seroreactivity (the detection of antigen-specific antibodies) suggests that that person has been exposed to EBOV or a similar virus in the past. Our study looked for seroreactive individuals who have never received an EVD diagnosis in four sites across the Democratic Republic of the Congo. Then we checked if animal exposures previously linked to EVD were more common among seroreactive individuals than non-seroreactive individuals. Among respondents from all four sites, 113 (8.3%) were seroreactive to EBOV. Additionally, contact with bats, rodents, and eating non-human primate meat were associated with seroreactivity, indicating these factors may be predictors of undocumented EBOV exposure events. These findings show that some EVD risk factors may be associated with EBOV seroreactivity without EVD diagnosis. Future research is needed to clarify the relationships between zoonotic exposures, seroreactivity, asymptomatic infection, and EVD.

Coronaviruses play an important role as pathogens of humans and animals, and the emergence of epidemics like SARS, MERS and COVID-19 is closely linked to zoonotic transmission events primarily from wild animals. Bats have been found to be an important source of coronaviruses with some of them having the potential to infect humans, with other animals serving as intermediate or alternate hosts or reservoirs. Host diversity may be an important contributor to viral diversity and thus the potential for zoonotic events. To date, limited research has been done in Africa on this topic, in particular in the Congo Basin despite frequent contact between humans and wildlife in this region. We sampled and, using consensus coronavirus PCR-primers, tested 3,561 wild animals for coronavirus RNA. The focus was on bats (38%), rodents (38%), and primates (23%) that posed an elevated risk for contact with people, and we found coronavirus RNA in 121 animals, of which all but two were bats. Depending on the taxonomic family, bats were significantly more likely to be coronavirus RNA-positive when sampled either in the wet (Pteropodidae and Rhinolophidae) or dry season (Hipposideridae, Miniopteridae, Molossidae, and Vespertilionidae). The detected RNA sequences correspond to 15 alpha- and 6 betacoronaviruses, with some of them being very similar (>95% nucleotide identities) to known coronaviruses and others being more unique and potentially representing novel viruses. In seven of the bats, we detected RNA most closely related to sequences of the human common cold coronaviruses 229E or NL63 (>80% nucleotide identities). The findings highlight the potential for coronavirus spillover, especially in regions with a high diversity of bats and close human contact, and reinforces the need for ongoing surveillance.

From 2006 to 2007, an active surveillance program for human monkeypox (MPX) in the Democratic Republic of the Congo identified 151 cases of coinfection with monkeypox virus and varicella zoster virus from 1158 suspected cases of human MPX (13%). Using clinical and socio-demographic data collected with standardized instruments by trained, local nurse supervisors, we examined a variety of hypotheses to explain the unexpectedly high proportion of coinfections among the sample, including the hypothesis that the two viruses occur independently. The probabilities of disease incidence and selection necessary to yield the observed sample proportion of coinfections under an assumption of independence are plausible given what is known and assumed about human MPX incidence. Cases of human MPX are expected to be underreported, and more coinfections are expected with improved surveillance.

Due to the high level of biological diversity in the Congo Basin and human population dependence on bushmeat, the DRC represents an ideal location for expanding knowledge on wild animal exposures and thus the potential for transmission of zoonotic pathogens. However, limited information exists on patterns and extent of contact with wildlife in such communities. Using a cross-sectional study, 14 villages in the Sankuru Province of the DRC were surveyed between August and September 2007. Villagers ≥ 1 year of age and at home of the time of the survey were eligible and enrolled to describe and assess factors associated with animal exposures (both activity and type of animal). Among respondents, 91% reported exposure to rodents, 89% to duikers, 78% to non-human primates (NHPs), and 32% reported contact with bats in the month prior to the survey. The most frequently reported activities included eating (95%), cooking (70%), and butchering or skinning of animals (55%). The activities and animals to which subjects had contact varied by sex and age. Moreover, we observed a high correlation of the same activities across animal types. In this and other populations that rely on bushmeat, there is a high frequency of exposure to multiple animal species through various modalities. In the event of future zoonotic disease outbreaks, effective public health interventions and campaigns that mitigate the risk of animal contact during outbreaks need to be broad to include various modes of contact and should be directed to both men and women across all age groups. As available information is limited, further studies are necessary to better understand the complex relationships and exposures individuals have with animals.

Mumps is an acute viral infection and while the infection is usually mild, complications can lead to permanent sequelae including brain damage and deafness. The burden of mumps is currently unknown the Democratic Republic of Congo (DRC), we therefore assessed susceptibility to mumps infection among children 6-59 months of age.In collaboration with the 2013-2014 DRC Demographic and Health Survey, we conducted a serosurvey to assess population immunity to vaccine preventable diseases. Dried blood spot samples were collected from children 6 to 59 months of age and processed at the UCLA-DRC laboratory in Kinshasa, DRC using the Dynex Technologies Multiplier FLEX chemiluminescent immunoassay platform (Dynex multiplex assay, Chantilly, VA). Logistic multivariate analyses were used to determine risk factors for mumps seropositivity.Serologic and survey data were matched for 7195, 6-59 month-old children, among whom 22% were positive and 3% indeterminate for mumps antibodies in weighted analyses. In multivariate analyses, the odds of seropositivity increased with increasing age, female gender, number of children in household, increasing socioeconomic status and province (Kinshasa with the highest odds of positive test result compared with all other provinces).These data suggest that mumps virus is circulating in DRC and risk of exposure increases with age. At present, the introduction of a combined measles-mumps-rubella vaccine remains unlikely, as the capacity to maintain adequate vaccine coverage levels for routine immunization must be improved before additional antigens can be considered for the routine immunization schedule.

Objective Evaluating the effectiveness of a surveillance system, and how it improves over time has significant implications for disease control and prevention. In the Democratic Republic of Congo (DRC), the Integrated Disease Surveillance and Response (IDSR) was implemented to estimate the burden of disease, monitor changes in disease occurrence, and inform resource allocation. For this effort we utilized national passive surveillance data from DRC's IDSR to explore reporting trends of human monkeypox (MPX) from 2001 to 2013. Methods We obtained surveillance data on MPX cases occurring between January 2001 and December 2013 from the DRC Ministry of Health (MoH). Phases of the surveillance system, yearly trends in reporting and estimated incidence for MPX were analyzed using SAS v9.2 and Health Mapper. Results Between 2001 and 2013, three discrete surveillance phases were identified that described the evolution of the surveillance system. Overall, an increase in suspected MPX cases was reported, beyond what would be expected from simply an improved reporting system. When restricting the analysis to the "stable phase," national estimated incidence increased from 2.13 per 100,000 in 2008 to 2.84 per 100,000 in 2013. Conclusions The reported increase in MPX, based on an evolving surveillance system, is likely to be a true increase in disease occurrence rather than simply improvements to the surveillance system. Further analyses should provide critical information for improved prevention and control strategies and highlight areas of improvement for future data collection efforts.

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. Keywords: Ebola virus disease, Hawkes point process, Mathematical modeling, Democratic Republic of Congo, Compartmental models

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., Author summary 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 been reported, and in which EVD cases and their contacts have been difficult for health workers to reach. We used an ensemble of models to estimate EVD transmission rates and to forecast the short- and long-term course of the outbreak. Our models project that a final size of roughly up to 300 additional cases is most likely, and estimate that transmission rates are higher than would be seen under optimal levels of contact tracing and vaccination. While a catastrophic outbreak is not projected, is it not ruled out, and prevention and vigilance are warranted.

The critical evaluation of immunization programs is key to identifying areas of suboptimal vaccination coverage, monitoring activities, and aiding development of public health policy. For evaluation of vaccine effectiveness, direct antibody binding assay methods, including enzyme immunoassay, enzyme-linked fluorescence assays, and indirect immunofluorescence assay, are most commonly used for detection of IgG antibodies. However, despite their well-demonstrated, reliable performance, they can be labor-intensive and time-consuming and require separate assays for each individual marker. This necessitates increased sample volumes, processing time, and personnel, which may limit assessment to a few key targets in resource-limited settings, that is, low- and middle-income locations where funding for public health or general infrastructure that directly impacts public health is restricted, limiting access to equipment, infrastructure, and trained personnel. One solution is a multiplexed immunoassay, which allows for the detection of multiple analytes in a single reaction for increased efficiency and rapid surveillance of infectious diseases in limited-resource settings. Thus, the scope of the project precluded a full validation, and here we present abbreviated validation studies demonstrating adequate sensitivity, specificity, and reproducibility., Here we describe baseline validation studies and field performance of a research-use-only chemiluminescent multiplex serology panel for measles, mumps, rubella, and varicella-zoster virus used with dried blood spots in support of the 2013–2014 Democratic Republic of the Congo Demographic and Health Survey. Characterization of the panel using U.S. FDA-cleared commercial kits shows good concordance for measles, mumps, rubella, and varicella-zoster with average sensitivity across assays of 94.9% and an average specificity of 91.4%. As expected, performance versus available standards validated for plaque-reduction assays does not provide a 1:1 correspondence with international units and yet demonstrates excellent linearity (average Hill’s slope = 1.02) and ∼4 logs of dynamic range. In addition, for the four assays, the multiplexed format allowed for inclusion of three positive and two negative controls for each sample. A prototype Dynex Multiplier chemiluminescent automated immunoassay instrument with a charge-coupled device camera provided a rugged and robust processing and data acquisition platform. Performance of a multiplex instrument for serological testing in a substantially resource-limited environment shows excellent reproducibility, minimal cross-reactivity, and a clear discrimination between specific assays and should be considered a viable option for future serosurveys. IMPORTANCE The critical evaluation of immunization programs is key to identifying areas of suboptimal vaccination coverage, monitoring activities, and aiding development of public health policy. For evaluation of vaccine effectiveness, direct antibody binding assay methods, including enzyme immunoassay, enzyme-linked fluorescence assays, and indirect immunofluorescence assay, are most commonly used for detection of IgG antibodies. However, despite their well-demonstrated, reliable performance, they can be labor-intensive and time-consuming and require separate assays for each individual marker. This necessitates increased sample volumes, processing time, and personnel, which may limit assessment to a few key targets in resource-limited settings, that is, low- and middle-income locations where funding for public health or general infrastructure that directly impacts public health is restricted, limiting access to equipment, infrastructure, and trained personnel. One solution is a multiplexed immunoassay, which allows for the detection of multiple analytes in a single reaction for increased efficiency and rapid surveillance of infectious diseases in limited-resource settings. Thus, the scope of the project precluded a full validation, and here we present abbreviated validation studies demonstrating adequate sensitivity, specificity, and reproducibility.

Understanding and quantifying the transmission of zoonotic pathogens is essential for directing public health responses, especially for pathogens capable of transmission between humans. However, determining a pathogen’s transmission dynamics is complicated by challenges often encountered in zoonotic disease surveillance, including unobserved sources of transmission (both human and zoonotic), limited spatial information, and unknown scope of surveillance. In this work, we present a model-based inference method that addresses these challenges for subcritical zoonotic pathogens using a spatial model with two levels of mixing. After demonstrating the robustness of the method using simulation studies, we apply the new method to a dataset of human monkeypox cases detected during an active surveillance program from 1982-1986 in the Democratic Republic of the Congo (DRC). Our results provide estimates of the reproductive number and spillover rate of monkeypox during this surveillance period and suggest that most human-to-human transmission events occur over distances of 30km or less. Taking advantage of contact-tracing data available for a subset of monkeypox cases, we find that around 80% of contact-traced links could be correctly recovered from transmission trees inferred using only date and location. Our results highlight the importance of identifying the appropriate spatial scale of transmission, and show how even imperfect spatiotemporal data can be incorporated into models to obtain reliable estimates of human-to-human transmission patterns.Author SummarySurveillance datasets are often the only sources of information about the ecology and epidemiology of zoonotic infectious diseases. Methods that can extract as much information as possible from these datasets therefore provide a key advantage for informing our understanding of the disease dynamics and improving our ability to choose the optimal intervention strategy. We developed and tested a likelihood-based inference method based on a mechanistic model of the spillover and human-to-human transmission processes. We first used simulated datasets to explore which information about the disease dynamics of a subcritical zoonotic pathogen could be successfully extracted from a line-list surveillance dataset with non-localized spatial information and unknown geographic coverage. We then applied the method to a dataset of human monkeypox cases detected during an active surveillance program in the Democratic Republic of the Congo between 1982 and 1986 to obtain estimates of the reproductive number, spillover rate, and spatial dispersal of monkeypox in humans.

Background: Violence can impact Ebola virus disease (EVD) transmission in the current outbreak in the eastern Democratic Republic of the Congo (DRC). We hypothesized that violent events targeted against the Ebola response will be associated with more EVD transmission than untargeted events. Methods: We used a dynamic cohort of individuals who lived in Ebola-affected and unaffected health zones in DRC from April 2018 to August 2019. The time-varying exposure was the number of violent events, as defined by the Armed Conflict Location & Event Data Project (ACLED) database, that occurred in each health zone over a series of days. These violent events were categorized into "Ebola-targeted" vs. "Ebola-untargeted", and further sub-categorized by "civilians," "military or politics," or "protests." The outcome variable was the estimated daily reproduction number (Rest) by health zone, which was estimated from daily EVD case counts (from DRC Ministry of Health) by the Wallinga-Teunis method. We fit a linear time-series regression to model the relationship of violent events and R comparing EVD-affected and unaffected health zones. Findings: The average Rest was 1.06 (95% confidence interval [CI]: 1.02-1.11). We found an overall change in R of 0.035 (95% CI: 0.020-0.050) among Ebola-affected compared to unaffected health zones. Violent events targeting the Ebola response were associated with an increase in Rest of 0.098 (95% CI: 0.064-0.132) while untargeted, violent events had a smaller effect (0.022, 95% CI: 0.005-0.038). Additional analyses showed the increase in Rest was primarily driven by Ebola-targeted civilian events and, to a lesser extent, by Ebola-untargeted military, political, or protest events. Interpretation: These findings suggest that civilian acts of violence directly targeted against the Ebola response efforts had the largest impact on EVD transmission. To a lesser extent, untargeted military, political, or protest events also had an impact. Funding Statement: This work was supported by National Institute of Allergy and Infectious Disease (K08 grant number AI139361 to ETR; K23 grant number AI135037 to JDK) and National Institute of General Medical Sciences (R01 grant number GM130900 to TCP, JDK, and ETR). Declaration of Interests: The authors stated: "None to declare." Ethics Approval Statement: The authors stated: "Data were publicly available and de-identified, so ethics committee approval was not needed."

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.

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 (n2007 = 3032 and n2013-14 = 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.

Different species of adenoviruses (AdVs) infect humans and animals and are known for their role as pathogens, especially in humans, with animals, primarily rodents, often serving as model systems. However, although we know over 100 types of human AdVs, we know comparatively little about the diversity of animal AdVs. Due to the fact that rodents are the most diverse family of mammals and a standard model system for human disease, we set out to sample African rodents native to the Democratic Republic of the Congo and test them for AdV DNA using a semi-nested consensus PCR. A total of 775 animals were tested, and viral DNA was detected in four of them. The AdV DNA found belongs to three different AdVs, all being closely related to murine adenovirus 2 (MAdV-2). Considering the genetic differences of the amplicon were 9%, 11% and 19% from MAdV-2 and at least 10% from each other, they seem to belong to up to three different novel types within the Murine mastadenovirus B species. This evidence of genetic diversity highlights the opportunities to isolate and study additional AdVs that infect rodents as models for AdV biology and pathology. Keywords: Adenovirus, Africa, Murine adenovirus 2, PCR, Rodent

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, 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.Author summaryAs 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 been reported, and in which EVD cases and their contacts have been difficult for health workers to reach. We used an ensemble of models to estimate EVD transmission rates and to forecast the short- and long-term course of the outbreak. Our models project that a final size of roughly up to 300 additional cases is most likely, and estimate that transmission rates are higher than would be seen under optimal levels of contact tracing and vaccination. While a catastrophic outbreak is not projected, is it not ruled out, and prevention and vigilance are warranted.

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.

Background Transient immunosuppression and increased susceptibility to other infections after measles infection is well known, but recent studies have suggested the occurrence of an “immune amnesia” that could have long-term immunosuppressive effects. Methods We examined the association between past measles infection and acute episodes of fever, cough, and diarrhea among 2350 children aged 9 to 59 months whose mothers were selected for interview in the 2013–2014 Democratic Republic of the Congo (DRC) Demographic and Health Survey (DHS). Classification of children who had had measles was completed using maternal recall and measles immunoglobulin G serostatus obtained via dried-blood-spot analysis with a multiplex immunoassay. The association with time since measles infection and fever, cough, and diarrhea outcomes was also examined. Results The odds of fever in the previous 2 weeks were 1.80 (95% confidence interval [CI], 1.25–2.60) among children for whom measles was reported compared to children with no history of measles. Measles vaccination demonstrated a protective association against selected clinical markers of acute infectious diseases. Conclusion Our results suggest that measles might have a long-term effect on selected clinical markers of acute infectious diseases among children aged 9 to 59 months in the DRC. These findings support the immune-amnesia hypothesis suggested by others and underscore the need for continued evaluation and improvement of the DRC’s measles vaccination program., In this study, we assessed the association of previous measles infection with episodes of fever, cough, or diarrhea in a low-vaccine-coverage resource-poor area, and our results suggest that measles infection might have a long-term effect on markers of acute infectious disease in our study population.

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.

Background Previous studies suggest that cases of Ebola virus disease (EVD) may go unreported because they are asymptomatic or unrecognized, but evidence is limited by study designs and sample size. Methods A large population-based survey was conducted (n = 3415) to assess animal exposures and behaviors associated with Ebolavirus antibody prevalence in rural Kasai Oriental province of the Democratic Republic of Congo (DRC). Fourteen villages were randomly selected and all healthy individuals ≥1 year of age were eligible. Results Overall, 11% of subjects tested positive for Zaire Ebolavirus (EBOV) immunoglobulin G antibodies. Odds of seropositivity were higher for study participants older than 15 years of age and for males. Those residing in Kole (closer to the outbreak site) tested positive at a rate 1.6× higher than Lomela, with seropositivity peaking at a site located between Kole and Lomela. Multivariate analyses of behaviors and animal exposures showed that visits to the forest or hunting and exposure to rodents or duikers predicted a higher likelihood of EBOV seropositivity. Conclusions These results provide serologic evidence of Ebolavirus exposure in a population residing in non-EBOV outbreak locations in the DRC and define statistically significant activities and animal exposures that associate with EBOV seropositivity.

Background Measles continues to be a leading cause of vaccine-preventable disease mortality among children under five despite a safe and efficacious vaccine being readily available. While global vaccination coverage has improved tremendously, measles outbreaks persist throughout sub-Saharan Africa. Since 2010, the Democratic Republic of Congo (DRC) has seen a resurgence of measles outbreaks affecting all 11 provinces. These outbreaks are mainly attributed to gaps in routine immunization (RI) coverage compounded with missed supplementary immunization activities (SIAs). We utilized national passive surveillance data from DRC's Integrated Disease Surveillance and Response (IDSR) system to estimate the effect of immunization on measles incidence in DRC. Methods We investigated the decline in measles incidence post-immunization with one dose of measles containing vaccine (MCV1) with and without the addition of supplementary immunization activities (SIAs) and outbreak response immunization (ORI) campaigns. Measles case counts by health zone were obtained from the IDSR system between January 1, 2010 and December 31, 2013. The impact of measles immunization was modeled using a random effects multi-level model for count data with RI coverage levels and mass campaign activities from one year prior. Results The presence of an SIA (aIRR [95% CI] 0.86 [0.60–1.25]) and ORI (0.28 [0.20–0.39]) in the year prior were both associated with a decrease in measles incidence. When interaction terms were included, our results suggested that the high levels of MCV1 reported in the year prior and the presence of either mass campaign was associated with a decrease in measles incidence. Conclusions Our results highlight the importance of a two-dose measles vaccine schedule and the need for a strong routine immunization program coupled with frequent SIAs. Repeated occurrences of large-scale outbreaks in DRC suggest that vaccination coverage rates are grossly overestimated and signify the importance of the evaluation and modification of measles prevention and control strategies.

Background Large-scale measles outbreaks in areas with high administrative vaccine coverage rates suggest the need to re-evaluate measles prevention and control in the Democratic Republic of Congo (DRC). Monitoring of measles Vaccine Effectiveness (VE) is a useful measure of quality control in immunization programs. We estimated measles VE among children aged 12–59 months in the Democratic Republic of Congo (DRC) using laboratory surveillance data from 2010–2012. Methods We used the case-based surveillance system with laboratory confirmation to conduct a case-control study using the test negative design. Cases and controls were selected based on presence ( n = 1044) or absence ( n = 1335) of measles specific antibody IgM or epidemiologic linkage. Risk factors for measles were assessed using unconditional logistic regression, stratified by age. Results Among children 12–59 months, measles vaccination was protective against measles [aOR (95% C)], 0.20 (0.15–0.26) and estimated VE was 80% (95% CI 74–85%). Year of diagnosis, 2011: 6.02 (4.16–8.72) and 2012; 8.31 (5.57–12.40) was a risk factor for measles when compared to 2010. Compared to Kinshasa, children in Bas-Congo, Kasai-Oriental, Maniema and South Kivu provinces all had higher odds of developing measles. Measles VE was similar for children 12–23 months and 24–59 months (80% and 81% respectively). Conclusions Repeated occurrences of measles outbreaks and lower than expected VE estimates suggest the need to further evaluate measles vaccine efficacy and improve vaccine delivery strategies in DRC.

Background Human monkeypox is a zoonotic disease endemic to parts of Africa. Similar to other orthopoxviruses, virus and host have considerable interactions through immunomodulation. These interactions likely drive the establishment of a productive infection and disease progression, resulting in the range of disease presentations and case fatality rates observed for members of the Orthopoxvirus genus. Objectives Much of our understanding about the immune response to orthopoxvirus infection comes from either in vitro or in vivo studies performed in small animals or non-human primates. Here, we conducted a detailed assessment of cytokine responses to monkeypox virus using serum from acutely ill humans collected during monkeypox active disease surveillance (2005–2007) in the Democratic Republic of the Congo. Study design Nineteen serum samples that were from patients with confirmed monkeypox virus infections were selected for cytokine profiling. Cytokine profiling was performed on the Bio-Rad Bioplex 100 system using a 30-plex human cytokine panel. Results Cytokine profiling revealed elevated cytokine concentrations in all samples. Overproduction of certain cytokines (interleukin [IL]-2R, IL-10, and granulocyte macrophage-colony stimulating factor were observed in patients with serious disease (defined as >250 lesions based on the World Health Organization scoring system). Conclusions The data suggest that cytokine modulation affects monkeypox disease severity in humans.