Quantifying the value of viral genomics when inferring who infected whom in the 2014–16 Ebola virus outbreak in Guinea

Transmission trees can be established through detailed contact histories, statistical or phylogenetic inference, or a combination of methods. Each approach has its limitations, and the extent to which they succeed in revealing a 'true' transmission history remains unclear. In this study, we compared the transmission trees obtained through contact tracing investigations and various inference methods to identify the contribution and value of each approach. We studied eighty-six sequenced cases reported in Guinea between March and November 2015. Contact tracing investigations classified these cases into eight independent transmission chains. We inferred the transmission history from the genetic sequences of the cases (phylogenetic approach), their onset date (epidemiological approach), and a combination of both (combined approach). The inferred transmission trees were then compared to those from the contact tracing investigations. Inference methods using individual data sources (i.e. the phylogenetic analysis and the epidemiological approach) were insufficiently informative to accurately reconstruct the transmission trees and the direction of transmission. The combined approach was able to identify a reduced pool of infectors for each case and highlight likely connections among chains classified as independent by the contact tracing investigations. Overall, the transmissions identified by the contact tracing investigations agreed with the evolutionary history of the viral genomes, even though some cases appeared to be misclassified. Therefore, collecting genetic sequences during outbreak is key to supplement the information contained in contact tracing investigations. Although none of the methods we used could identify one unique infector per case, the combined approach highlighted the added value of mixing epidemiological and genetic information to reconstruct who infected whom

Emergence of Zaire Ebola Virus Disease in Guinea - Preliminary Report

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

Persistence and clearance of Ebola virus RNA from seminal fluid of Ebola virus disease survivors: a longitudinal analysis and modelling study

Background By January, 2016, all known transmission chains of the Ebola virus disease (EVD) outbreak in west Africa had been stopped. However, there is concern about persistence of Ebola virus in the reproductive tract of men who have survived EVD. We aimed to use biostatistical modelling to describe the dynamics of Ebola virus RNA load in seminal fl uid, including clearance parameters. Methods In this longitudinal study, we recruited men who had been discharged from three Ebola treatment units in Guinea between January and July, 2015. Participants provided samples of seminal fl uid at follow-up every 3–6 weeks, which we tested for Ebola virus RNA using quantitative real-time RT-PCR. Representative specimens from eight participants were then inoculated into immunodefi cient mice to test for infectivity. We used a linear mixed-eff ect model to analyse the dynamics of virus persistence in seminal fl uid over time. Findings We enrolled 26 participants and tested 130 seminal fl uid specimens; median follow up was 197 days (IQR 187–209 days) after enrolment, which corresponded to 255 days (228–287) after disease onset. Ebola virus RNA was detected in 86 semen specimens from 19 (73%) participants. Median duration of Ebola virus RNA detection was 158 days after onset (73–181; maximum 407 days at end of follow-up). Mathematical modelling of the quantitative time-series data showed a mean clearance rate of Ebola virus RNA from seminal fl uid of –0·58 log units per month, although the clearance kinetic varied greatly between participants. Using our biostatistical model, we predict that 50% and 90% of male survivors clear Ebola virus RNA from seminal fl uid at 115 days (90% prediction interval 72–160) and 294 days (212–399) after disease onset, respectively. We also predicted that the number of men positive for Ebola virus RNA in aff ected countries would decrease from about 50 in January 2016, to fewer than 1 person by July, 2016. Infectious virus was detected in 15 of 26 (58%) specimens tested in mice. Interpretation Time to clearance of Ebola virus RNA from seminal fl uid varies greatly between individuals and could be more than 13 months. Our predictions will assist in decision-making about surveillance and preventive measures in EVD outbreaks

Determinants of Transmission Risk During the Late Stage of the West African Ebola Epidemic.

Understanding risk factors for Ebola transmission is key for effective prediction and design of interventions. We used data on 860 cases in 129 chains of transmission from the latter half of the 2013-2016 Ebola epidemic in Guinea. Using negative binomial regression, we determined characteristics associated with the number of secondary cases resulting from each infected individual. We found that attending an Ebola treatment unit was associated with a 38% decrease in secondary cases (incidence rate ratio (IRR) = 0.62, 95% confidence interval (CI): 0.38, 0.99) among individuals that did not survive. Unsafe burial was associated with a higher number of secondary cases (IRR = 1.82, 95% CI: 1.10, 3.02). The average number of secondary cases was higher for the first generation of a transmission chain (mean = 1.77) compared with subsequent generations (mean = 0.70). Children were least likely to transmit (IRR = 0.35, 95% CI: 0.21, 0.57) compared with adults, whereas older adults were associated with higher numbers of secondary cases. Men were less likely to transmit than women (IRR = 0.71, 95% CI: 0.55, 0.93). This detailed surveillance data set provided an invaluable insight into transmission routes and risks. Our analysis highlights the key role that age, receiving treatment, and safe burial played in the spread of EVD

Persistence and clearance of Ebola virus RNA from seminal fluid of Ebola virus disease survivors: a longitudinal analysis and modelling study.

BACKGROUND: By January, 2016, all known transmission chains of the Ebola virus disease (EVD) outbreak in west Africa had been stopped. However, there is concern about persistence of Ebola virus in the reproductive tract of men who have survived EVD. We aimed to use biostatistical modelling to describe the dynamics of Ebola virus RNA load in seminal fluid, including clearance parameters. METHODS: In this longitudinal study, we recruited men who had been discharged from three Ebola treatment units in Guinea between January and July, 2015. Participants provided samples of seminal fluid at follow-up every 3-6 weeks, which we tested for Ebola virus RNA using quantitative real-time RT-PCR. Representative specimens from eight participants were then inoculated into immunodeficient mice to test for infectivity. We used a linear mixed-effect model to analyse the dynamics of virus persistence in seminal fluid over time. FINDINGS: We enrolled 26 participants and tested 130 seminal fluid specimens; median follow up was 197 days (IQR 187-209 days) after enrolment, which corresponded to 255 days (228-287) after disease onset. Ebola virus RNA was detected in 86 semen specimens from 19 (73%) participants. Median duration of Ebola virus RNA detection was 158 days after onset (73-181; maximum 407 days at end of follow-up). Mathematical modelling of the quantitative time-series data showed a mean clearance rate of Ebola virus RNA from seminal fluid of -0·58 log units per month, although the clearance kinetic varied greatly between participants. Using our biostatistical model, we predict that 50% and 90% of male survivors clear Ebola virus RNA from seminal fluid at 115 days (90% prediction interval 72-160) and 294 days (212-399) after disease onset, respectively. We also predicted that the number of men positive for Ebola virus RNA in affected countries would decrease from about 50 in January 2016, to fewer than 1 person by July, 2016. Infectious virus was detected in 15 of 26 (58%) specimens tested in mice. INTERPRETATION: Time to clearance of Ebola virus RNA from seminal fluid varies greatly between individuals and could be more than 13 months. Our predictions will assist in decision-making about surveillance and preventive measures in EVD outbreaks. FUNDING: This study was funded by European Union's Horizon 2020 research and innovation programme, Directorate-General for International Cooperation and Development of the European Commission, Institut national de la santé et de la recherche médicale (INSERM), German Research Foundation (DFG), and Innovative Medicines Initiative 2 Joint Undertaking

Resurgence of Ebola Virus Disease in Guinea Linked to a Survivor With Virus Persistence in Seminal Fluid for More Than 500 Days.

We report on an Ebola virus disease (EVD) survivor who showed Ebola virus in seminal fluid 531 days after onset of disease. The persisting virus was sexually transmitted in February 2016, about 470 days after onset of symptoms, and caused a new cluster of EVD in Guinea and Liberia

Virus genomes reveal factors that spread and sustained the Ebola epidemic.

The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics