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

Performance of a safe and dignified burial intervention during an Ebola epidemic in the eastern Democratic Republic of the Congo, 2018-2019

BACKGROUND

A protracted Ebola Virus Disease (EVD) epidemic in the eastern Ituri, North and South Kivu provinces of the Democratic Republic of Congo (DRC) caused 3470 confirmed and probable cases between July 2018 and April 2020. During the epidemic, the International Federation of Red Cross and Red Crescent Societies (IFRC) supported the DRC Red Cross and other local actors to offer safe and dignified burials (SDB) for suspected and confirmed EVD cases, so as to reduce transmission associated with infectious dead bodies. We conducted a retrospective cohort study of the SDB service's performance in order to inform future applications of this intervention.

METHODS

We analysed data on individual SDB responses to quantify performance based on key indicators and against pre-specified service standards. Specifically, we defined SDB timeliness as response within 24 h and success as all components of the service being implemented. Combining the database with other information sources, we also fit generalised linear mixed binomial models to explore factors associated with unsuccessful SDB.

RESULTS

Out of 14,624 requests for SDB, 99% were responded to, 89% within 24 h. Overall, 61% of SDBs were successful, somewhat below target (80%), with failures clustered during a high-insecurity period. Factors associated with increased odds of unsuccessful SDB included reported community and/or family nonacceptance, insecurity and suspensions of the EVD response, low health facility coverage and high coverage of radio and telephony. Burials supported by mobile Civil Protection (local authorities) and/or static, community-based 'harm reduction' teams were associated with lower odds of failure.

CONCLUSIONS

A large-scale, timely and moderately performant SDB service proved feasible during the challenging eastern DRC EVD response. Burial teams that are managed by community actors and operate locally, and supported rather than owned by the Red Cross or other humanitarian organisations, are a promising modality of delivering this pillar of EVD control.

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.

Social Network Analysis of Ebola Virus Disease During the 2014 Outbreak in Sukudu, Sierra Leone

Background

Transmission by unreported cases has been proposed as a reason for the 2013-2016 Ebola virus (EBOV) epidemic decline in West Africa, but studies that test this hypothesis are lacking. We examined a transmission chain within social networks in Sukudu village to assess spread and transmission burnout.

Methods

Network data were collected in 2 phases: (1) serological and contact information from Ebola cases (n = 48, including unreported); and (2) interviews (n = 148), including Ebola survivors (n = 13), to identify key social interactions. Social links to the transmission chain were used to calculate cumulative incidence proportion as the number of EBOV-infected people in the network divided by total network size.

Results

The sample included 148 participants and 1522 contacts, comprising 10 social networks: 3 had strong links (>50% of cases) to the transmission chain: household sharing (largely kinship), leisure time, and talking about important things (both largely non-kin). Overall cumulative incidence for these networks was 37 of 311 (12%). Unreported cases did not have higher network centrality than reported cases.

Conclusions

Although this study did not find evidence that explained epidemic decline in Sukudu, it excluded potential reasons (eg, unreported cases, herd immunity) and identified 3 social interactions in EBOV transmission.

Using phylogenetics to infer HIV-1 transmission direction between known transmission pairs

Identifying the transmission direction between individuals provides unparalleled power to understand infectious disease epidemiology. With epidemiological and clinical information typically unavailable to infer transmission direction, phylogenetic analysis of pathogen sequence data offers an alternative approach. While the success of this phylogenetic analysis varies, the reasons remain unknown. We analyze sequence data from over 100 transmission pairs for which both the transmission direction of HIV is known and detailed additional information is available. We find that easily quantifiable phylogenetic and sampling characteristics discriminate whether a phylogenetically inferred transmission direction is correct. Our analysis highlights that while phylogenetic approaches to infer transmission direction are unsuitable for individual-level analysis, such as forensic investigations, confidence in source attribution can be incorporated in population-level analyses.

Inferring the transmission direction between linked individuals living with HIV provides unparalleled power to understand the epidemiology that determines transmission. Phylogenetic ancestral-state reconstruction approaches infer the transmission direction by identifying the individual in whom the most recent common ancestor of the virus populations originated. While these methods vary in accuracy, it is unclear why. To evaluate the performance of phylogenetic ancestral-state reconstruction to determine the transmission direction of HIV-1 infection, we inferred the transmission direction for 112 transmission pairs where transmission direction and detailed additional information were available. We then fit a statistical model to evaluate the extent to which epidemiological, sampling, genetic, and phylogenetic factors influenced the outcome of the inference. Finally, we repeated the analysis under real-life conditions with only routinely available data. We found that whether ancestral-state reconstruction correctly infers the transmission direction depends principally on the phylogeny's topology. For example, under real-life conditions, the probability of identifying the correct transmission direction increases from 32%—when a monophyletic–monophyletic or paraphyletic–polyphyletic tree topology is observed and when the tip closest to the root does not agree with the state at the root—to 93% when a paraphyletic–monophyletic topology is observed and when the tip closest to the root agrees with the root state. Our results suggest that documenting larger differences in relative intrahost diversity increases our confidence in the transmission direction inference of linked pairs for population-level studies of HIV. These findings provide a practical starting point to determine our confidence in transmission direction inference from ancestral-state reconstruction.

Risk factors for Ebola virus disease among household care providers, Sierra Leone, 2015

BACKGROUND

Household contacts who provide care to an Ebola virus disease (EVD) case have a 3-fold higher risk of EVD compared with contacts who do not provide care.

METHODS

We enrolled persons with confirmed EVD from December 2014 to April 2015 in Freetown, Sierra Leone, and their household contacts. Index cases and contacts were interviewed, and contacts were followed for 21 days to identify secondary cases. Epidemiological data were analysed to describe household care and to identify risk factors for developing EVD.

RESULTS

Of 838 contacts in 147 households, 156 (17%) self-reported providing care to the index case; 56 households had no care provider, 52 a single care provider and 39 multiple care providers. The median care provider age was 29 years, 68% were female and 32% were the index case's spouse. Care providers were more likely to report physical contact, contact with body fluids or sharing clothing, bed linens or utensils with an index case, compared with non-care providers (P <0.01). EVD risk among non-care providers was greater when the number of care providers in the household increased (odds ratio: 1.61; 95% confidence interval: 1.1, 2.4). In multivariable analysis, factors associated with care provider EVD risk included no piped water access and absence of index case fever, and protective factors included age <20 years and avoiding the index case.

CONCLUSIONS

Limiting the number of care providers in a household could reduce the risk of EVD transmission to both care providers and non-care providers. Strategies to protect care providers from EVD exposure are needed.

Ethical and sociocultural challenges in managing dead bodies during epidemics and natural disasters

Background

Catastrophic natural disasters and epidemics claim thousands of lives and have severe and lasting consequences, accompanied by human suffering. The Ebola epidemic of 2014–2016 and the current COVID-19 pandemic have revealed some of the practical and ethical complexities relating to the management of dead bodies. While frontline staff are tasked with saving lives, managing the bodies of those who die remains an under-resourced and overlooked issue, with numerous ethical and practical problems globally.

Methods

This scoping review of literature examines the management of dead bodies during epidemics and natural disasters. 82 articles were reviewed, of which only a small number were empirical studies focusing on ethical or sociocultural issues that emerge in the management of dead bodies.

Results

We have identified a wide range of ethical and sociocultural challenges, such as ensuring dignity for the deceased while protecting the living, honouring the cultural and religious rituals surrounding death, alleviating the suffering that accompanies grieving for the survivors and mitigating inequalities of resource allocation. It was revealed that several ethical and sociocultural issues arise at all stages of body management: notification, retrieving, identification, storage and burial of dead bodies.

Conclusion

While practical issues with managing dead bodies have been discussed in the global health literature and the ethical and sociocultural facets of handling the dead have been recognised, they are nonetheless not given adequate attention. Further research is needed to ensure care for the dead in epidemics and that natural disasters are informed by ethical best practice.

o2geosocial: Reconstructing who-infected-whom from routinely collected surveillance data

Reconstructing the history of individual transmission events between cases is key to understanding what factors facilitate the spread of an infectious disease. Since conducting extended contact-tracing investigations can be logistically challenging and costly, statistical inference methods have been developed to reconstruct transmission trees from onset dates and genetic sequences. However, these methods are not as effective if the mutation rate of the virus is very slow, or if sequencing data is sparse. We developed the package o2geosocial to combine variables from routinely collected surveillance data with a simple transmission process model. The model reconstructs transmission trees when full genetic sequences are unavailable, or uninformative. Our model incorporates the reported age-group, onset date, location and genotype of infected cases to infer probabilistic transmission trees. The package also includes functions to summarise and visualise the inferred cluster size distribution. The results generated by o2geosocial can highlight regions where importations repeatedly caused large outbreaks, which may indicate a higher regional susceptibility to infections. It can also be used to generate the individual number of secondary transmissions, and show the features associated with individuals involved in high transmission events. The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub.