Suchergebnisse

Background: During the five decades since their discovery, filoviruses of four species have caused human hemorrhagic fever outbreaks: Marburg (MARV) marburgvirus, and Zaire (EBOV), Sudan (SUDV) and Bundybugyo (BDBV) ebolaviruses. The largest, devastating EBOV epidemic in West Africa in 2014-16, has been followed by outbreaks of MARV in Uganda, 2017, and EBOV in Democratic Republic of Congo, 2018, emphasizing the need to develop preparedness to diagnose all filoviruses. Objectives: The aim of this study was to optimize a new filovirus RT-qPCR to detect all filoviruses, define its limits of detection (LOD) and perform a field evaluation with outbreak samples. Study design: A pan-filovirus RT-qPCR targeting the L gene was developed and evaluated within the EbolaMoDRAD (Ebola virus: modern approaches for developing bedside rapid diagnostics) project. Specificity and sensitivity were determined and the effect of inactivation and PCR reagents (liquid and lyophilized format) were tested. Results: The LODs for the lyophilized pan-filovirus L-RT-qPCR assay were 9.4 copies per PCR reaction for EBOV, 9.9 for MARV, 1151 for SUDV, 65 for BDBV and 289 for Tai Forest virus. The test was set at the Pasteur Institute, Dakar, Senegal, and 83 Ebola patient samples, with viral load ranging from 5 to 5 million copies of EBOV per reaction, were screened. The results for the patient samples were in 100% concordance with the reference EBOVspecific assay. Discussion: Overall, the assay showed good sensitivity and specificity, covered all filoviruses known to be human pathogens, performed well both in lyophilized and liquid-phase formats and with EBOV outbreak clinical samples. ; Peer reviewed

Background During the five decades since their discovery, filoviruses of four species have caused human hemorrhagic fever outbreaks: Marburg (MARV) marburgvirus, and Zaire (EBOV), Sudan (SUDV) and Bundybugyo (BDBV) ebolaviruses. The largest, devastating EBOV epidemic in West Africa in 2014-16, has been followed by outbreaks of MARV in Uganda, 2017, and EBOV in Democratic Republic of Congo, 2018, emphasizing the need to develop preparedness to diagnose all filoviruses. Objectives The aim of this study was to optimize a new filovirus RT-qPCR to detect all filoviruses, define its limits of detection (LOD) and perform a field evaluation with outbreak samples. Study design A pan-filovirus RT-qPCR targeting the L gene was developed and evaluated within the EbolaMoDRAD (Ebola virus: modern approaches for developing bedside rapid diagnostics) project. Specificity and sensitivity were determined and the effect of inactivation and PCR reagents (liquid and lyophilized format) were tested. Results The LODs for the lyophilized pan-filovirus L-RT-qPCR assay were 9.4 copies per PCR reaction for EBOV, 9.9 for MARV, 1151 for SUDV, 65 for BDBV and 289 for Taï Forest virus. The test was set at the Pasteur Institute, Dakar, Senegal, and 83 Ebola patient samples, with viral load ranging from 5 to 5 million copies of EBOV per reaction, were screened. The results for the patient samples were in 100% concordance with the reference EBOV-specific assay. Discussion Overall, the assay showed good sensitivity and specificity, covered all filoviruses known to be human pathogens, performed well both in lyophilized and liquid-phase formats and with EBOV outbreak clinical samples.

Chikungunya virus (CHIKV), a mosquito-borne alphavirus, has traditionally circulated in Africa and Asia, causing human febrile illness accompanied by severe, chronic joint pain. In Africa, epidemic emergence of CHIKV involves the transition from an enzootic, sylvatic cycle involving arboreal mosquito vectors and nonhuman primates, into an urban cycle where peridomestic mosquitoes transmit among humans. In Asia, however, CHIKV appears to circulate only in the endemic, urban cycle. Recently, CHIKV emerged into the Indian Ocean and the Indian subcontinent to cause major epidemics. To examine patterns of CHIKV evolution and the origins of these outbreaks, as well as to examine whether evolutionary rates that vary between enzootic and epidemic transmission, we sequenced the genomes of 40 CHIKV strains and performed a phylogenetic analysis representing the most comprehensive study of its kind to date. We inferred that extant CHIKV strains evolved from an ancestor that existed within the last 500 years and that some geographic overlap exists between two main enzootic lineages previously thought to be geographically separated within Africa. We estimated that CHIKV was introduced from Africa into Asia 70 to 90 years ago. The recent Indian Ocean and Indian subcontinent epidemics appear to have emerged independently from the mainland of East Africa. This finding underscores the importance of surveillance to rapidly detect and control African outbreaks before exportation can occur. Significantly higher rates of nucleotide substitution appear to occur during urban than during enzootic transmission. These results suggest fundamental differences in transmission modes and/or dynamics in these two transmission cycles.

International audience ; Background: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock.Methods: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region.Findings: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected.Interpretation: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such ...

International audience ; Background: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock.Methods: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region.Findings: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected.Interpretation: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy

International audience ; Background: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock.Methods: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region.Findings: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected.Interpretation: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy

BACKGROUND: In the wake of the recent outbreak of Ebola virus disease (EVD) in several African countries, the World Health Organization prioritized the evaluation of treatment with convalescent plasma derived from patients who have recovered from the disease. We evaluated the safety and efficacy of convalescent plasma for the treatment of EVD in Guinea. METHODS: In this nonrandomized, comparative study, 99 patients of various ages (including pregnant women) with confirmed EVD received two consecutive transfusions of 200 to 250 ml of ABO-compatible convalescent plasma, with each unit of plasma obtained from a separate convalescent donor. The transfusions were initiated on the day of diagnosis or up to 2 days later. The level of neutralizing antibodies against Ebola virus in the plasma was unknown at the time of administration. The control group was 418 patients who had been treated at the same center during the previous 5 months. The primary outcome was the risk of death during the period from 3 to 16 days after diagnosis with adjustments for age and the baseline cycle-threshold value on polymerase-chain-reaction assay; patients who had died before day 3 were excluded. The clinically important difference was defined as an absolute reduction in mortality of 20 percentage points in the convalescent-plasma group as compared with the control group. RESULTS: A total of 84 patients who were treated with plasma were included in the primary analysis. At baseline, the convalescent-plasma group had slightly higher cycle-threshold values and a shorter duration of symptoms than did the control group, along with a higher frequency of eye redness and difficulty in swallowing. From day 3 to day 16 after diagnosis, the risk of death was 31% in the convalescent-plasma group and 38% in the control group (risk difference, -7 percentage points; 95% confidence interval [CI], -18 to 4). The difference was reduced after adjustment for age and cycle-threshold value (adjusted risk difference, -3 percentage points; 95% CI, -13 to 8). No serious adverse reactions associated with the use of convalescent plasma were observed. CONCLUSIONS: The transfusion of up to 500 ml of convalescent plasma with unknown levels of neutralizing antibodies in 84 patients with confirmed EVD was not associated with a significant improvement in survival. (Funded by the European Union's Horizon 2020 Research and Innovation Program and others; ClinicalTrials.gov number, NCT02342171.).

WOS: 000388677600001 ; PubMed ID: 27667586 ; In countries from which Crimean-Congo haemorrhagic fever (CCHF) is absent, the causative virus, CCHF virus (CCHFV), is classified as a hazard group 4 agent and handled in containment level (CL)-4. In contrast, most endemic countries out of necessity have had to perform diagnostic tests under biosafety level (BSL)-2 or -3 conditions. In particular, Turkey and several of the Balkan countries have safely processed more than 100 000 samples over many years in BSL-2 laboratories. It is therefore advocated that biosafety requirements for CCHF diagnostic procedures should be revised, to allow the tests required to be performed under enhanced BSL-2 conditions with appropriate biosafety laboratory equipment and personal protective equipment used according to standardized protocols in the countries affected. Downgrading of CCHFV research work from CL-4, BSL-4 to CL-3, BSL-3 should also be considered. ; European Commission under Health Cooperation Work Program of 7th Framework Program [260427] ; Funding was received through CCH Fever Network (Collaborative Project) supported by the European Commission under the Health Cooperation Work Program of the 7th Framework Program (grant agreement no. 260427) (http://www.cch-fever.eu/). The views expressed by state-employed American co-authors are their personal views, and do not necessarily represent the views of the US government agencies they work for. The views expressed by the ECDC coauthor are his personal views, and do not necessarily represent the views of the European agency he is working for.

On August 1, 2018, the Democratic Republic of the Congo declared its tenth Ebola virus disease outbreak. To aid the epidemiologic response, the Institut National de Recherche Biomédicale implemented an end-to-end genomic surveillance system, including sequencing, bioinformatic analysis, and dissemination of genomic epidemiologic results to frontline public health workers. We report 744 new genomes sampled between July 27, 2018 and April 27, 2020 generated by this surveillance effort. Together with previously available sequence data (n = 48 genomes), these data represent almost 24% of all laboratory-confirmed Ebola virus infections in DRC in the analyzed period. We inferred spatiotemporal transmission dynamics from the genomic data as new sequences were generated and disseminated the results to support epidemiologic response efforts. Here, we provide an overview of how this genomic surveillance system functioned, present a full phylodynamic analysis of 792 Ebola genomes from the Nord Kivu outbreak, and discuss how the genomic surveillance data informed response efforts and public health decision-making.

This is an Open Access article under the CC BY license. ; BACKGROUND: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock. METHODS: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region. FINDINGS: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. INTERPRETATION: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy. ; info:eu-repo/semantics/publishedVersion

BACKGROUND: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock. METHODS: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region. FINDINGS: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. INTERPRETATION: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy. FUNDING: Wellcome Trust.