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Abstract Background Measles continues to circulate in the Democratic Republic of Congo, and the country suffered from several important outbreaks over the last 5 years. Despite a large outbreak starting in the former province of Katanga in 2010 and the resulting immunization activities, another outbreak occurred in 2015 in this same region. We conducted measles seroprevalence surveys in four health zones (HZ) in the former Katanga Province in order to assess the immunity against measles in children 6 months to 14 years after the 2015 outbreak. Methods We conducted multi-stage cluster surveys stratified by age group in four HZs, Kayamba, Malemba-Nkulu, Fungurume, and Manono. The age groups were 6–11 months, 12–59 months, and 5–14 years in Kayamba and Malemba-Nkulu, 6–59 months and 5–14 years in Manono and Fungurume. The serological status was measured on dried capillary blood spots collected systematically along with vaccination status (including routine Extended Program of Immunization (EPI), and supplementary immunization activities (SIAs)) and previous self-reported history of suspected measles. Results Overall seroprevalence against measles was 82.7% in Kayamba, 97.6% in Malemba-Nkulu, 83.2% in Manono, and 74.4% in Fungurume, and it increased with age in all HZs. It was 70.7 and 93.8% in children 12–59 months in Kayamba and Malemba-Nkulu, and 49.8 and 64.7% in children 6–59 months in Fungurume and Manono. The EPI coverage was low but varied across HZ. The accumulation of any type of vaccination against measles resulted in an overall vaccine coverage (VC) of at least 85% in children 12–59 months in Kayamba and Malemba-Nkulu, 86.1 and 74.8% in children 6–59 months in Fungurume and Manono. Previous measles infection in 2015-early 2016 was more frequently reported in children aged 12–59 months or 6–59 months (depending on the HZ). Conclusion The measured seroprevalence was consistent with the events that occurred in these HZs over the past few years. Measles seroprevalence might prove a valuable source of information to help adjust the timing of future SIAs and prioritizing support to the EPI in this region as long as the VC does not reach a level high enough to efficiently prevent epidemic flare-ups.

ABSTRACT: Objectives: To describe the characteristics of nosocomial cases of Ebola virus disease (EVD) in the Democratic Republic of the Congo between July 2018 and May 2020 in order to inform future interventions. Methods: Nosocomial cases of EVD were identified during outbreak response surveillance, and a retrospective analysis of cases was conducted according to demographic characteristics and type of health facility (HF). Results: Of 3481 cases of EVD, 579 (16.6%) were nosocomial. Of these, 332 cases occurred in women (57.3%). Patients and visitors accounted for 419 cases (72.4%), of which 79 (18.9%) were aged 6–≤18 years and 108 (25.8%) were aged ≤5 years. Health workers (HWs) accounted for the remaining 160 (27.6%) nosocomial cases. The case fatality rate (CFR) for HWs (66/160, 41.3%) was significantly lower than the CFR for patients and visitors (292/419, 69.7%) (P39 beds) had the highest prevalence of nosocomial EVD (148/579, 25.6%). Among HFs with at least one case of nosocomial infection, 50.0% (98/196) were privately owned. Conclusions: Nurses and traditional healers should be targeted for infection prevention and control training, and supportive supervision should be provided to HFs to mitigate EVD transmission.

The emerging field of outbreak analytics calls attention to the need for data from multiple sources to inform evidence-based decision making in managing infectious diseases outbreaks. To date, these approaches have not systematically integrated evidence from social and behavioural sciences. During the 2018–2020 Ebola outbreak in Eastern Democratic Republic of the Congo, an innovative solution to systematic and timely generation of integrated and actionable social science evidence emerged in the form of the Cellulle d’Analyse en Sciences Sociales (Social Sciences Analytics Cell) (CASS), a social science analytical cell. CASS worked closely with data scientists and epidemiologists operating under the Epidemiological Cell to produce integrated outbreak analytics (IOA), where quantitative epidemiological analyses were complemented by behavioural field studies and social science analyses to help better explain and understand drivers and barriers to outbreak dynamics. The primary activity of the CASS was to conduct operational social science analyses that were useful to decision makers. This included ensuring that research questions were relevant, driven by epidemiological data from the field, that research could be conducted rapidly (ie, often within days), that findings were regularly and systematically presented to partners and that recommendations were co-developed with response actors. The implementation of the recommendations based on CASS analytics was also monitored over time, to measure their impact on response operations. This practice paper presents the CASS logic model, developed through a field-based externally led consultation, and documents key factors contributing to the usefulness and adaption of CASS and IOA to guide replication for future outbreaks.