Search

Monkeypox, a tropical zoonosis with an estimated death rate of 15% in children, is a resurgent presence in several countries in West and Central Africa (1,2). Before 2000, only 21 [...]

Ebola virus disease (EVD) is a severe, frequently fatal illness. In March 2014, the largest EVD outbreak in history began spreading through parts of West Africa. As of June 21, [...]

Background The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved. Methods and Findings Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ('cases') were asked if they had exposure to other potential Ebola cases ('potential source contacts') in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = -0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within [less than or equal to]4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible misclassifications). Conclusions Achieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population., Author(s): International Ebola Response Team, Junerlyn Agua-Agum 1, Archchun Ariyarajah 1, Bruce Aylward 1, Luke Bawo 2, Pepe Bilivogui 3, Isobel M. Blake 4, Richard J. Brennan 1, Amy Cawthorne [...]

Abstract Background Drug resistance displays a problem for the therapy of Mycobacterium tuberculosis infections. For molecular resistance testing, it is essential to have precise knowledge on genomic variations involved in resistance development. However, data from high-incidence settings are only sparely available. Therefore we performed a systematic approach and analyzed a total of 97 M. tuberculosis strains from previously treated patients in Sierra Leone for mutations in katG, rpoB, rrs, rpsL, gidB, embB, pncA and where applicable in inhA and ahpC. Of the strains investigated 50 were either mono- or poly-resistant to isoniazid, rifampin, streptomycin, ethambutol and pyrazinamide or MDR and 47 fully susceptible strains served as controls. Results The majority of isoniazid and rifampin resistant strains had mutations in katG315 (71.9%) and rpoB531 (50%). However, rpoB mutations in codons 511, 516 and 533 were also detected in five rifampin susceptible strains. MIC determinations revealed low-level rifampin resistance for those strains. Thus, the sensitivity and specificity of sequencing of katG for detection of drug resistance were 86.7% and 100% and for sequencing of rpoB 100% and 93.8%, respectively. Strikingly, none of the streptomycin resistant strains had mutations in rrs, but 47.5% harboured mutations in rpsL. Further changes were detected in gidB. Among ethambutol resistant strains 46.7% had mutations at embB306. Pyrazinamide resistant strains displayed a variety of mutations throughout pncA. The specificities of sequencing of rpsL, embB and pncA for resistance detection were high (96-100%), whereas sensitivities were lower (48.8%, 73.3%, 70%). Conclusions Our study reveals a good correlation between data from molecular and phenotypic resistance testing in this high-incidence setting. However, the fact that particular mutations in rpoB are not linked to high-level resistance is challenging and demonstrates that careful interpretation of molecular resistance assays is mandatory. In addition, certain variations, especially in gidB, appear to be phylogenetically informative polymorphisms rather than markers for drug resistance.

Abstract Background Among tuberculosis (TB) high incidence regions, Sub-Saharan Africa is particularly affected with approx. 1.6 million new cases every year. Besides this dramatic situation, data on the diversity of Mycobacterium tuberculosis complex (MTBC) strains causing this epidemic in this area are only sparsely available. Here we analyzed the population structure of strains from Sierra Leone with a special focus on the prevalence of M. africanum. Results A total of 97 strains isolated from smear positive cases registered for re-treatment in the Western Area and Kenema districts in years 2003/2004 were investigated by susceptibility testing (first line drugs) and molecular typing (IS6110 fingerprinting, spoligotyping, and MIRU-VNTR typing). Among the strains analyzed, 32 were resistant to isoniazid, and 11 were multidrug resistant (at least resistant to isoniazid and rifampin). The population diversity was high with two previously described M. africanum lineages (West African-1, n = 6; West African-2, n = 17) and seven M. tuberculosis lineages (Haarlem, n = 14; LAM, n = 15; EAI, n = 4; Beijing, n = 4; S-type, n = 4, X-type, n = 1; Cameroon, n = 4). Furthermore, two new M. tuberculosis genotypes Sierra Leone-1 (n = 7) and -2 (n = 10) were found. Strain classification according to a 7 bp deletion in pks1/15 revealed that the majority of M. tuberculosis strains belonged to the Euro American lineage (66 out of 74). Conclusion Resistance rates in Sierra Leone have reached an alarming level. The population structure of MTBC strains shows an intriguing diversity raising the question of possible consequences for TB epidemic and for the introduction of new diagnostic tests or treatment strategies in West Africa.

Background: The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.Methods and Findings: Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ("cases") were asked if they had exposure to other potential Ebola cases ("potential source contacts") in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = -0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible misclassifications).Conclusions: Achieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population. [ABSTRACT FROM AUTHOR]

Background. During 2014-2015, an outbreak of Ebola virus disease (EVD) swept across parts of West Africa. No approved antiviral drugs are available for Ebola treatment currently. Methods. A retrospective clinical case series was performed for EVD patients in Sierra Leone-China Friendship Hospital. Patients with confirmed EVD were sequentially enrolled and treated with either World Health Organization (WHO)-recommended supportive therapy (control group) from 10 to 30 October, or treated with WHO-recommended therapy plus favipiravir (T-705) from 1 to 10 November 2014. Survival and virological characteristics were observed for 85 patients in the control group and 39 in the T-705 treatment group. Results. The overall survival rate in the T-705 treatment group was higher than that of the control group (56.4% [22/39] vs 35.3% [30/85]; P = .027). Among the 35 patients who finished all designed endpoint observations, the survival rate in the T-705 treatment group (64.8% [11/17]) was higher than that of the control group (27.8% [5/18]). Furthermore, the average survival time of the treatment group (46.9 ± 5.6 days) was longer than that of the control group (28.9 ± 4.7 days). Most symptoms of patients in the treatment group improved significantly. Additionally, 52.9% of patients who received T-705 had a >100-fold viral load reduction, compared with only 16.7% of patients in the control group. Conclusions. Treatment of EVD with T-705 was associated with prolonged survival and markedly reduced viral load, which makes a compelling case for further randomized controlled trials of T-705 for treating EVD. [ABSTRACT FROM AUTHOR]

Sierra Leone is the most severely affected country by an unprecedented outbreak of Ebola virus disease (EVD) in West Africa. Although successfully contained, the transmission dynamics of EVD and the impact of interventions in the country remain unclear. We established a database of confirmed and suspected EVD cases from May 2014 to September 2015 in Sierra Leone and mapped the spatiotemporal distribution of cases at the chiefdom level. A Poisson transmission model revealed that the transmissibility at the chiefdom level, estimated as the average number of secondary infections caused by a patient per week, was reduced by 43% [95% confidence interval (CI): 30%, 52%] after October 2014, when the strategic plan of the United Nations Mission for Emergency Ebola Response was initiated, and by 65%(95% CI: 57%, 71%) after the end of December 2014, when 100% case isolation and safe burials were essentially achieved, both compared with before October 2014. Population density, proximity to Ebola treatment centers, cropland coverage, and atmospheric temperature were associated with EVD transmission. The household secondary attack rate (SAR) was estimated to be 0.059 (95% CI: 0.050, 0.070) for the overall outbreak. The household SAR was reduced by 82%, from 0.093 to 0.017, after the nationwide campaign to achieve 100% case isolation and safe burials had been conducted. This study provides a complete overview of the transmission dynamics of the 2014-2015 EVD outbreak in Sierra Leone at both chiefdom and household levels. The interventions implemented in Sierra Leone seem effective in containing the epidemic, particularly in interrupting household transmission. [ABSTRACT FROM AUTHOR]

A correction to the article "Genetic diversity and evolutionary dynamics of Ebola virus in Sierra Leone" which was published in issue #524 is presented.

The border region of Forécariah (Guinea) and Kambia (Sierra Leone) was of immense interest to the West Africa Ebola response. Cross-sectional household surveys with multi-stage cluster sampling procedure were used to collect random samples from Kambia ( n = 635) in July 2015 and Forécariah ( n = 502) in August 2015 to assess public knowledge, attitudes and practices related to Ebola. Knowledge of the disease was high in both places, and handwashing with soap and water was the most widespread prevention practice. Acceptance of safe alternatives to traditional burials was significantly lower in Forécariah compared with Kambia. In both locations, there was a minority who held discriminatory attitudes towards survivors. Radio was the predominant source of information in both locations, but those from Kambia were more likely to have received Ebola information from community sources (mosques/churches, community meetings or health workers) compared with those in Forécariah. These findings contextualize the utility of Ebola health messaging during the epidemic and suggest the importance of continued partnership with community leaders, including religious leaders, as a prominent part of future public health protection.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'., (© 2017 The Author(s).)