In March 2014, the World Health Organization was notified of an outbreak of a communicable disease characterized by fever, severe diarrhea, vomiting, and a high fatality rate in Guinea. Virologic investigation identified Zaire ebolavirus (EBOV) as the causative agent. Full-length genome sequencing and phylogenetic analysis showed that EBOV from Guinea forms a separate clade in relationship to the known EBOV strains from the Democratic Republic of Congo and Gabon. Epidemiologic investigation linked the laboratory-confirmed cases with the presumed first fatality of the outbreak in December 2013. This study demonstrates the emergence of a new EBOV strain in Guinea.

The spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has been unprecedented in its speed and effects. Interruption of its transmission to prevent widespread community transmission is critical because its effects go beyond the number of COVID-19 cases and deaths and affect the health system capacity to provide other essential services. Highlighting the implications of such a situation, the predictions presented here are derived using a Markov chain model, with the transition states and country specific probabilities derived based on currently available knowledge. A risk of exposure, and vulnerability index are used to make the probabilities country specific. The results predict a high risk of exposure in states of small size, together with Algeria, South Africa and Cameroon. Nigeria will have the largest number of infections, followed by Algeria and South Africa. Mauritania would have the fewest cases, followed by Seychelles and Eritrea. Per capita, Mauritius, Seychelles and Equatorial Guinea would have the highest proportion of their population affected, while Niger, Mauritania and Chad would have the lowest. Of the World Health Organization's 1 billion population in Africa, 22% (16%–26%) will be infected in the first year, with 37 (29 – 44) million symptomatic cases and 150 078 (82 735–189 579) deaths. There will be an estimated 4.6 (3.6–5.5) million COVID-19 hospitalisations, of which 139 521 (81 876–167 044) would be severe cases requiring oxygen, and 89 043 (52 253–106 599) critical cases requiring breathing support. The needed mitigation measures would significantly strain health system capacities, particularly for secondary and tertiary services, while many cases may pass undetected in primary care facilities due to weak diagnostic capacity and non-specific symptoms. The effect of avoiding widespread and sustained community transmission of SARS-CoV-2 is significant, and most likely outweighs any costs of preventing such a scenario. Effective containment measures should be promoted in all countries to best manage the COVID-19 pandemic.

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The end-of-outbreak declaration is an important step in controlling infectious disease outbreaks. An objective estimation of the confidence level that an outbreak is over is important to reduce the risk of post-declaration flare-ups. We developed a simulation-based model to quantify that confidence. We tested it on simulated Ebola Virus Disease data. We found these confidence estimates were most sensitive to the instantaneous reproduction number, the reporting rate, and the time between the symptom onset to death or recovery of the last detected case. For Ebola Virus Disease, our results suggest that the current World Health Organization criterion of 42 days since the recovery or death of the last detected case is too short and sensitive to underreporting. Therefore, we suggest a shift to a preliminary end-of-outbreak declaration after 63 days from the symptom onset day of the last detected case. This preliminary declaration should still be followed by 90 days of enhanced surveillance to capture potential flare-ups of cases, after which the official end-of-outbreak can be declared. This sequence corresponds to more than 95% confidence that an outbreak is over in most of the scenarios examined. Our framework is generic, and therefore could be adapted to estimate end-of-outbreak confidence for other infectious diseases.

Background: Emerging and re-emerging diseases with pandemic potential continue to challenge fragile health systems in Africa, creating enormous human and economic toll. To provide evidence for the investment case for public health emergency preparedness, we analysed the spatial and temporal distribution of epidemics, disasters and other potential public health emergencies in the WHO African region between 2016 and 2018. Methods: We abstracted data from several sources, including: the WHO African Region's weekly bulletins on epidemics and emergencies, the WHO-Disease Outbreak News (DON) and the Emergency Events Database (EM-DAT) of the Centre for Research on the Epidemiology of Disasters (CRED). Other sources were: the Program for Monitoring Emerging Diseases (ProMED) and the Global Infectious Disease and Epidemiology Network (GIDEON). We included information on the time and location of the event, the number of cases and deaths and counterchecked the different data sources. Data analysis: We used bubble plots for temporal analysis and generated graphs and maps showing the frequency and distribution of each event. Based on the frequency of events, we categorised countries into three: Tier 1, 10 or more events, Tier 2, 5-9 events, and Tier 3, less than 5 or no event. Finally, we compared the event frequencies to a summary International Health Regulations (IHR) index generated from the IHR technical area scores of the 2018 annual reports. Results: Over 260 events were identified between 2016 and 2018. Forty-one countries (87%) had at least one epidemic between 2016 and 2018, and 21 of them (45%) had at least one epidemic annually. Twenty-two countries (47%) had disasters/humanitarian crises. Seven countries (the epicentres) experienced over 10 events and all of them had limited or developing IHR capacities. The top five causes of epidemics were: Cholera, Measles, Viral Haemorrhagic Diseases, Malaria and Meningitis.

The WHO African region is characterised by the largest infectious disease burden in the world. We conducted a retrospective descriptive analysis using records of all infectious disease outbreaks formally reported to the WHO in 2018 by Member States of the African region. We analysed the spatio-temporal distribution, the notification delay as well as the morbidity and mortality associated with these outbreaks. In 2018, 96 new disease outbreaks were reported across 36 of the 47 Member States. The most commonly reported disease outbreak was cholera which accounted for 20.8% (n = 20) of all events, followed by measles (n = 11, 11.5%) and Yellow fever (n = 7, 7.3%). About a quarter of the outbreaks (n = 23) were reported following signals detected through media monitoring conducted at the WHO regional office for Africa. The median delay between the disease onset and WHO notification was 16 days (range: 0–184). A total of 107 167 people were directly affected including 1221 deaths (mean case fatality ratio (CFR): 1.14% (95% confidence interval (CI) 1.07%–1.20%)). The highest CFR was observed for diseases targeted for eradication or elimination: 3.45% (95% CI 0.89%–10.45%). The African region remains prone to outbreaks of infectious diseases. It is therefore critical that Member States improve their capacities to rapidly detect, report and respond to public health events.

Large-scale protracted outbreaks can be prevented through early detection, notification, and rapid control. We assessed trends in timeliness of detecting and responding to outbreaks in the African Region reported to the World Health Organization during 2017–2019. We computed the median time to each outbreak milestone and assessed the rates of change over time using univariable and multivariable Cox proportional hazard regression analyses. We selected 296 outbreaks from 348 public reported health events and evaluated 184 for time to detection, 232 for time to notification, and 201 for time to end. Time to detection and end decreased over time, whereas time to notification increased. Multiple factors can account for these findings, including scaling up support to member states after the World Health Organization established its Health Emergencies Programme and support given to countries from donors and partners to strengthen their core capacities for meeting International Health Regulations.

IntroductionSince sex-based biological and gender factors influence COVID-19 mortality, we wanted to investigate the difference in mortality rates between women and men in sub-Saharan Africa (SSA).MethodWe included 69 580 cases of COVID-19, stratified by sex (men: n=43 071; women: n=26 509) and age (0–39 years: n=41 682; 40–59 years: n=20 757; 60+ years: n=7141), from 20 member nations of the WHO African region until 1 September 2020. We computed the SSA-specific and country-specific case fatality rates (CFRs) and sex-specific CFR differences across various age groups, using a Bayesian approach.ResultsA total of 1656 deaths (2.4% of total cases reported) were reported, with men accounting for 70.5% of total deaths. In SSA, women had a lower CFR than men (mean CFRdiff = −0.9%; 95% credible intervals (CIs) −1.1% to −0.6%). The mean CFR estimates increased with age, with the sex-specific CFR differences being significant among those aged 40 years or more (40–59 age group: mean CFRdiff = −0.7%; 95% CI −1.1% to −0.2%; 60+ years age group: mean CFRdiff = −3.9%; 95% CI −5.3% to −2.4%). At the country level, 7 of the 20 SSA countries reported significantly lower CFRs among women than men overall. Moreover, corresponding to the age-specific datasets, significantly lower CFRs in women than men were observed in the 60+ years age group in seven countries and 40–59 years age group in one country.ConclusionsSex and age are important predictors of COVID-19 mortality globally. Countries should prioritise the collection and use of sex-disaggregated data so as to design public health interventions and ensure that policies promote a gender-sensitive public health response.