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Background: Tetanus is a potentially fatal disease that is preventable through vaccination. While the Democratic Republic of the Congo (DRC) has continued to improve implementing routine vaccination activities throughout the country, they have struggled to maintain high childhood vaccine coverage. This study aims to examine the seroprevalence of tetanus in children 6 to 59 months to identify areas for intervention and improvement of vaccination coverage., Methods: In collaboration with the 2013-2014 Demographic and Health Survey, we assessed the seroprevalence of tetanus antibodies among children in the DRC. Dried blood spot samples collected from children 6-59 months of age were processed using a prototype DYNEX Multiplier® chemiluminescent automated immunoassay instrument with a multiplex measles, mumps, rubella, varicella and tetanus assay. Multivariable logistic regression was used to determine factors associated with tetanus vaccination and seroprotection., Results: Overall, 36.1% of children 6-59 months of age reported receiving at least 1 dose of tetanus vaccine while 28.7% reported receiving 3 doses; tetanus seroprotection was 40%. Increasing age in children was associated with decreased tetanus seroprotection, but increased number tetanus vaccinations received. Factors related to increased tetanus seroprotection included number of children in the household, wealth index of the family, urban residence compared to rural, level of maternal education, and province and geography., Conclusions: Our findings in this nationally representative sample indicate that serology biomarkers may help identify children who are not fully immunized to tetanus more accurately than reported vaccination. While children may be captured for routine immunization activities, as children age, decreasing seroprevalence may indicate additional need to bolster routine vaccination activities and documentation of vaccination in school aged children. Additionally, the study highlights gaps in rural residential areas and vaccination coverage based on maternal education, indicating that policies targeting maternal education and awareness could improve the coverage and seroprevalence of tetanus antibodies in the DRC., Competing Interests: The Bill and Melinda Gates Foundation employs SG. The authors have declared that no competing interests exist. These affiliations do not alter our adherence to PLOSONE policies on sharing data and materials.

Although multiple antigenically distinct ebolavirus species can cause human disease, previous serosurveys focused on only Zaire ebolavirus (EBOV). Thus, the extent of reactivity or exposure to other ebolaviruses, and which sociodemographic factors are linked to this seroreactivity, are unclear. We conducted a serosurvey of 539 healthcare workers (HCW) in Mbandaka, Democratic Republic of the Congo, using ELISA-based analysis of serum IgG against EBOV, Sudan ebolavirus (SUDV) and Bundibugyo ebolavirus (BDBV) glycoproteins (GP). We compared seroreactivity to risk factors for viral exposure using univariate and multivariable logistic regression. Seroreactivity against different GPs ranged from 2.2-4.6%. Samples from six individuals reacted to all three species of ebolavirus and 27 samples showed a species-specific IgG response. We find that community health volunteers are more likely to be seroreactive against each antigen than nurses, and in general, that HCWs with indirect patient contact have higher anti-EBOV GP IgG levels than those with direct contact. Seroreactivity against ebolavirus GP may be associated with positions that offer less occupational training and access to PPE. Those individuals with broadly reactive responses may have had multiple ebolavirus exposures or developed cross-reactive antibodies. In contrast, those individuals with species-specific BDBV or SUDV GP seroreactivity may have been exposed to an ebolavirus not previously known to circulate in the region., Competing Interests: The authors have declared that no competing interests exist. Author Nathalie Kavira was unable to confirm her authorship contributions. On her behalf, the corresponding author has reported her contributions to the best of their knowledge.

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., Competing Interests: The authors have declared that no competing interests exist. Author Emile Okitolonda-Wemakoy was unable to confirm their authorship contributions. On their behalf, the corresponding author has reported their contributions to the best of their knowledge.

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., Competing Interests: Metabiota (https://metabiota.com), Labyrinth Global Health (https://www.labyrinthgh.com), and Mosaic (https://mosaic.cm) are private contractors who receive funds from international donor organizations to conduct technical assistance and research. Metabiota employs or employed CK, FM, PMK, INL, FNK, JAL, PM, MM, UT, AG, BSS, DJM, JAA, CM, DOJ, KS, NDW, EMR, and CEL, Mosaic employs ML, and Labyrinth Global Health employs KS, MM and CEL. There are no patents, products in development or marketed products associated with this research to declare. The association with commercial entities that some of the authors have does not alter our adherence to PLOS ONE policies on sharing data and materials.

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., Competing Interests: The authors have declared that no competing interests exist. Author Jean-Jacques Muyembe was unable to confirm their authorship contributions. On their behalf, the corresponding author has reported their contributions to the best of their knowledge.

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

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