Projections of epidemic transmission and estimation of vaccination impact during an ongoing Ebola virus disease outbreak in Northeastern Democratic Republic of Congo, as of Feb. 25, 2019.

BackgroundAs of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak.MethodsFor short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott's rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts.ResultsDuring validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872-1054) and 955 cases by March 4 (95% prediction interval: 874-1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876-933) and 898 (95% prediction interval: 877-983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013-2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone.ConclusionsOur projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges

Projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018.

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

Response to treatment in a prospective cohort of patients with large ulcerated lesions suspected to be Buruli Ulcer (Mycobacterium ulcerans disease)

BACKGROUND: The World Health Organization (WHO) advises treatment of Mycobacterium ulcerans disease, also called "Buruli ulcer" (BU), with a combination of the antibiotics rifampicin and streptomycin (R+S), whether followed by surgery or not. In endemic areas, a clinical case definition is recommended. We evaluated the effectiveness of this strategy in a series of patients with large ulcers of > or =10 cm in longest diameter in a rural health zone of the Democratic Republic of Congo (DRC). METHODS: A cohort of 92 patients with large ulcerated lesions suspected to be BU was enrolled between October 2006 and September 2007 and treated according to WHO recommendations. The following microbiologic data were obtained: Ziehl-Neelsen (ZN) stained smear, culture and PCR. Histopathology was performed on a sub-sample. Directly observed treatment with R+S was administered daily for 12 weeks and surgery was performed after 4 weeks. Patients were followed up for two years after treatment. FINDINGS: Out of 92 treated patients, 61 tested positive for M. ulcerans by PCR. PCR negative patients had better clinical improvement than PCR positive patients after 4 weeks of antibiotics (54.8% versus 14.8%). For PCR positive patients, the outcome after 4 weeks of antibiotic treatment was related to the ZN positivity at the start. Deterioration of the ulcers was observed in 87.8% (36/41) of the ZN positive and in 12.2% (5/41) of the ZN negative patients. Deterioration due to paradoxical reaction seemed unlikely. After surgery and an additional 8 weeks of antibiotics, 98.4% of PCR positive patients and 83.3% of PCR negative patients were considered cured. The overall recurrence rate was very low (1.1%). INTERPRETATION: Positive predictive value of the WHO clinical case definition was low. Low relapse rate confirms the efficacy of antibiotics. However, the need for and the best time for surgery for large Buruli ulcers requires clarification. We recommend confirmation by ZN stain at the rural health centers, since surgical intervention without delay may be necessary on the ZN positive cases to avoid progression of the disease. PCR negative patients were most likely not BU cases. Correct diagnosis and specific management of these non-BU ulcers cases are urgently needed.This study was supported by the Directorate-General for Development and Cooperation (DGDC), Brussels, Belgium, the European Commission (International Science and Technology Cooperation Development Program) (project no. INCO-CT-2005-051476-BURULICO), and by a grant from the Health Services of Fundacao Calouste Gulbenkian. K.K. was supported by a grant from DGDC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Nomenclature- and Database-Compatible Names for the Two Ebola Virus Variants that Emerged in Guinea and the Democratic Republic of the Congo in 2014

In 2014, Ebola virus (EBOV) was identified as the etiological agent of a large and still expanding outbreak of Ebola virus disease (EVD) in West Africa and a much more confined EVD outbreak in Middle Africa. Epidemiological and evolutionary analyses confirmed that all cases of both outbreaks are connected to a single introduction each of EBOV into human populations and that both outbreaks are not directly connected. Coding-complete genomic sequence analyses of isolates revealed that the two outbreaks were caused by two novel EBOV variants, and initial clinical observations suggest that neither of them should be considered strains. Here we present consensus decisions on naming for both variants (West Africa: “Makona”, Middle Africa: “Lomela”) and provide database-compatible full, shortened, and abbreviated names that are in line with recently established filovirus sub-species nomenclatures

The critical need for pooled data on coronavirus disease 2019 in African children : an AFREhealth call for action through multicountry research collaboration

Globally, there are prevailing knowledge gaps in the epidemiology, clinical manifestations, and outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among children and adolescents; and these gaps are especially wide in African countries. The availability of robust age-disaggregated data is a critical first step in improving knowledge on disease burden and manifestations of coronavirus disease 2019 (COVID-19) among children. Furthermore, it is essential to improve understanding of SARS-CoV-2 interactions with comorbidities and coinfections such as human immunodeficiency virus (HIV), tuberculosis, malaria, sickle cell disease, and malnutrition, which are highly prevalent among children in sub-Saharan Africa. The African Forum for Research and Education in Health (AFREhealth) COVID-19 Research Collaboration on Children and Adolescents is conducting studies across Western, Central, Eastern, and Southern Africa to address existing knowledge gaps. This consortium is expected to generate key evidence to inform clinical practice and public health policy-making for COVID-19 while concurrently addressing other major diseases affecting children in African countries.The US National Institutes of Health (NIH)/ Fogarty International Centre (FIC) to the African Forum for Research and Education in Health (AFREhealth).https://academic.oup.com/cidam2022Paediatrics and Child Healt

A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa.

The progression of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Africa has so far been heterogeneous, and the full impact is not yet well understood. In this study, we describe the genomic epidemiology using a dataset of 8746 genomes from 33 African countries and two overseas territories. We show that the epidemics in most countries were initiated by importations predominantly from Europe, which diminished after the early introduction of international travel restrictions. As the pandemic progressed, ongoing transmission in many countries and increasing mobility led to the emergence and spread within the continent of many variants of concern and interest, such as B.1.351, B.1.525, A.23.1, and C.1.1. Although distorted by low sampling numbers and blind spots, the findings highlight that Africa must not be left behind in the global pandemic response, otherwise it could become a source for new variants

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century