Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study

Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control

During the coronavirus disease 2019 pandemic, mathematical modeling has been widely used to understand epidemiological burden, trends, and transmission dynamics, to facilitate policy decisions, and, to a lesser extent, to evaluate infection prevention and control (IPC) measures. This review highlights the added value of using conventional epidemiology and modeling approaches to address the complexity of healthcare-associated infections (HAI) and antimicrobial resistance. It demonstrates how epidemiological surveillance data and modeling can be used to infer transmission dynamics in healthcare settings and to forecast healthcare impact, how modeling can be used to improve the validity of interpretation of epidemiological surveillance data, how modeling can be used to estimate the impact of IPC interventions, and how modeling can be used to guide IPC and antimicrobial treatment and stewardship decision-making. There are several priority areas for expanding the use of modeling in healthcare epidemiology and IPC. Importantly, modeling should be viewed as complementary to conventional healthcare epidemiological approaches, and this requires collaboration and active coordination between IPC, healthcare epidemiology, and mathematical modeling groups.

The potential risk components and prevention measures of the Ebola virus disease outbreak in Liberia: An in-depth interview with the health workers and stakeholders

Background

The Ebola virus, a highly infectious and deadly pathogen, has posed a significant public health threat in West Africa for several decades. Liberia is one of the most severely affected countries. Healthcare personnel, including nurses, are on the front lines of patient care, and their perspectives are invaluable in understanding the challenges that arise during outbreaks, especially in implementing prevention measures.

Objective

This study aimed to explore the potential risk components and prevention measures of the Ebola virus disease (EVD).

Methods

This study used an exploratory descriptive qualitative design. Five stakeholders, ten doctors and five nurses who had suffered from EVD during the outbreak in Liberia participated in semi-structured interviews to provide their experience and comprehensive perspectives on EVD. Data were collected from February 2022-August 2023. NVivo 12 plus was used for inductive thematic analysis.

Results

Six themes and several subthemes emerged: 1) transmission modes (body contact, body fluid, sexual intercourse, traditional burial), 2) funeral attendance (traditional practices and crowded gatherings), 3) community-led prevention (promoting good hygiene practices, increasing awareness, contact tracing, and surveillance), 4) Ebola virus vaccine (false sense of security, potential side effects, and limited data), 5) challenges in implementing prevention measures (inadequate health infrastructures, difficulty of tracing infected people, lack of resources, and cultural-social barriers), 6) Liberia's health systems (a weak, underfunded, fragile health infrastructure, lack of health facilities and shortage of health workers).

Conclusion

Several potential risk components contributing to the EVD outbreak should be a public concern. Strengthening the current healthcare system supported by local community and international aid providers (multidisciplinary teams) is needed to anticipate behavioral problems and to improve the efficacy of the prevention measures appropriate to the conditions in Liberia. Accordingly, the nurses' compliance with the recommended prevention practices is necessary.

Modelling the impact of stigmatisation of Ebola survivors on the disease transmission dynamics

Ebola virus disease (EVD) is one of the most highly stigmatised diseases in any affected country because of the disease's high infectivity and case fatality rate. Infected individuals and most especially survivors are often stigmatised by their communities for fear of contagion. We propose and analyse a mathematical model to examine the impact of stigmatisation of Ebola survivors on the disease dynamics. The model captures both the internal stigmatisation experienced by infected individuals after witnessing survivors being stigmatised and the external stigmatisation imposed on survivors by their communities. The results obtained from our analysis and simulations show that both internal and external stigma may lead to an increase in the burden of Ebola virus disease by sustaining the number of infected individuals who hide their infection and the number of unsafe burials of deceased Ebola victims. Strategies that seek to put an end to both forms of stigmatisation and promote safe burials will therefore go a long way in averting the EVD burden.

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.

Measuring the unknown: An estimator and simulation study for assessing case reporting during epidemics

The fraction of cases reported, known as 'reporting', is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018-2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5-10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities.

Spatial model of Ebola outbreaks contained by behavior change

The West African Ebola (2014-2016) epidemic caused an estimated 11.310 deaths and massive social and economic disruption. The epidemic was comprised of many local outbreaks of varying sizes. However, often local outbreaks recede before the arrival of international aid or susceptible depletion. We modeled Ebola virus transmission under the effect of behavior changes acting as a local inhibitor. A spatial model is used to simulate Ebola epidemics. Our findings suggest that behavior changes can explain why local Ebola outbreaks recede before substantial international aid was mobilized during the 2014-2016 epidemic.

The Hybrid Incidence Susceptible-Transmissible-Removed Model for Pandemics : Scaling Time to Predict an Epidemic's Population Density Dependent Temporal Propagation

The susceptible-transmissible-removed (STR) model is a deterministic compartment model, based on the susceptible-infected-removed (SIR) prototype. The STR replaces 2 SIR assumptions. SIR assumes that the emigration rate (due to death or recovery) is directly proportional to the infected compartment’s size. The STR replaces this assumption with the biologically appropriate assumption that the emigration rate is the same as the immigration rate one infected period ago. This results in a unique delay differential equation epidemic model with the delay equal to the infected period. Hamer’s mass action law for epidemiology is modified to resemble its chemistry precursor—the law of mass action. Constructing the model for an isolated population that exists on a surface bounded by the extent of the population’s movements permits compartment density to replace compartment size. The STR reduces to a SIR model in a timescale that negates the delay—the transmissible timescale. This establishes that the SIR model applies to an isolated population in the disease’s transmissible timescale. Cyclical social interactions will define a rhythmic timescale. It is demonstrated that the geometric mean maps transmissible timescale properties to their rhythmic timescale equivalents. This mapping defines the hybrid incidence (HI). The model validation demonstrates that the HI-STR can be constructed directly from the disease’s transmission dynamics. The basic reproduction number (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_0$$\end{document}R0) is an epidemic impact property. The HI-STR model predicts that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_0 \propto \root \mathfrak{B} \of {\rho_n}$$\end{document}R0∝ρnB where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho_n$$\end{document}ρn is the population density, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathfrak{B}}$$\end{document}B is the ratio of time increments in the transmissible- and rhythmic timescales. The model is validated by experimentally verifying the relationship. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{R}}_0$$\end{document}R0’s dependence on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho_n$$\end{document}ρn is demonstrated for droplet-spread SARS in Asian cities, aerosol-spread measles in Europe and non-airborne Ebola in Africa.

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.

Electronic data collection, management and analysis tools used for outbreak response in low- and middle-income countries: a systematic review and stakeholder survey

BACKGROUND

Use of electronic data collection, management and analysis tools to support outbreak response is limited, especially in low income countries. This can hamper timely decision-making during outbreak response. Identifying available tools and assessing their functions in the context of outbreak response would support appropriate selection and use, and likely more timely data-driven decision-making during outbreaks.

METHODS

We conducted a systematic review and a stakeholder survey of the Global Outbreak Alert and Response Network and other partners to identify and describe the use of, and technical characteristics of, electronic data tools used for outbreak response in low- and middle-income countries. Databases included were MEDLINE, EMBASE, Global Health, Web of Science and CINAHL with publications related to tools for outbreak response included from January 2010-May 2020. Software tool websites of identified tools were also reviewed. Inclusion and exclusion criteria were applied and counts, and proportions of data obtained from the review or stakeholder survey were calculated.

RESULTS

We identified 75 electronic tools including for data collection (33/75), management (13/75) and analysis (49/75) based on data from the review and survey. Twenty-eight tools integrated all three functionalities upon collection of additional information from the tool developer websites. The majority were open source, capable of offline data collection and data visualisation. EpiInfo, KoBoCollect and Open Data Kit had the broadest use, including for health promotion, infection prevention and control, and surveillance data capture. Survey participants highlighted harmonisation of data tools as a key challenge in outbreaks and the need for preparedness through training front-line responders on data tools. In partnership with the Global Health Network, we created an online interactive decision-making tool using data derived from the survey and review.

CONCLUSIONS

Many electronic tools are available for data -collection, -management and -analysis in outbreak response, but appropriate tool selection depends on knowledge of tools' functionalities and capabilities. The online decision-making tool created to assist selection of the most appropriate tool(s) for outbreak response helps by matching requirements with functionality. Applying the tool together with harmonisation of data formats, and training of front-line responders outside of epidemic periods can support more timely data-driven decision making in outbreaks.

Grappling with COVID-19 by imposing and lifting non-pharmaceutical interventions in Sri Lanka: A modeling perspective

The imposition and lifting of non-pharmaceutical interventions (NPIs) to avert the COVID-19 pandemic have gained popularity worldwide and will continue to be enforced until herd immunity is achieved. We developed a linear regression model to ascertain the nexus between the time-varying reproduction number averaged over a time window of six days (R_ts) and seven NPIs: contact tracing, quarantine efforts, social distancing and health checks, hand hygiene, wearing of facemasks, lockdown and isolation, and health-related supports. Our analysis suggests that the second wave that emerged in Sri Lanka in early October 2020 continued despite numerous NPIs.

The model indicates that the most effective single NPI was lockdown and isolation. Conversely, the least effective individual NPIs were hand hygiene and wearing of facemasks. The model also demonstrates that to mitigate the second wave to a satisfactory level (R_ts<1), the best single NPI was the contact tracing with stringent imposition (% of improvement of R_ts was 69.43 against the base case). By contrast, the best combination of two NPIs was the lockdown & isolation with health-related supports (% of improvement was 31.92 against the base case). As such, many health authorities worldwide can use this model to successfully strategize the imposition and lifting of NPIs for averting the COVID-19 pandemic.

Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread

Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1-4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.

Genetic evidence for the association between COVID-19 epidemic severity and timing of non-pharmaceutical interventions

Unprecedented public health interventions including travel restrictions and national lockdowns have been implemented to stem the COVID-19 epidemic, but the effectiveness of non-pharmaceutical interventions is still debated. We carried out a phylogenetic analysis of more than 29,000 publicly available whole genome SARS-CoV-2 sequences from 57 locations to estimate the time that the epidemic originated in different places. These estimates were examined in relation to the dates of the most stringent interventions in each location as well as to the number of cumulative COVID-19 deaths and phylodynamic estimates of epidemic size. Here we report that the time elapsed between epidemic origin and maximum intervention is associated with different measures of epidemic severity and explains 11% of the variance in reported deaths one month after the most stringent intervention. Locations where strong non-pharmaceutical interventions were implemented earlier experienced much less severe COVID-19 morbidity and mortality during the period of study.

A Quantitative Framework for Defining the End of an Infectious Disease Outbreak: Application to Ebola Virus Disease

The end-of-outbreak declaration is an important step in controlling infectious disease outbreaks. Objective estimation of the confidence level that an outbreak is over is important to reduce the risk of postdeclaration flare-ups. We developed a simulation-based model with which to quantify that confidence and tested it on simulated Ebola virus disease data. We found that these confidence estimates were most sensitive to the instantaneous reproduction number, the reporting rate, and the time between the symptom onset and death or recovery of the last detected case. For Ebola virus disease, our results suggested that the current World Health Organization criterion of 42 days since the recovery or death of the last detected case is too short and too 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 the 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.

How Does Railway Respond to the Spread of COVID-19? Countermeasure Analysis and Evaluation Around the World

The global COVID-19 pandemic is having a significant impact on the development of many aspects all over the world. As an important part of public services, rail transit requires effective response countermeasures to control the spread of COVID-19. Considering the current development of the epidemic situation, this article discusses the characteristics of COVID-19 transmission and identifies vulnerable areas to target in order to prevent and control the spread of the epidemic in the rail transit system. Countermeasures adopted to prevent the spread of COVID-19 are analyzed in terms of external and internal categories, which were classified into six groups: passenger service, case care, information, staff, equipment and operation management. An evaluation architecture was also constructed, which was established from the perspective of effectiveness, economic efficiency, acceptability, privacy and so on. The effect of implementing the measures was evaluated by a social survey, and their advantages and shortcomings were analyzed, which can be used to guide future epidemic prevention and control for rail transit systems around the world. It is important to formulate a reasonable work schedule according to local conditions, providing a reference for rapid response to future public health emergencies of international concern.

Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2

A long-standing question in infectious disease dynamics concerns the role of transmission heterogeneities, which are driven by demography, behavior, and interventions. On the basis of detailed patient and contact-tracing data in Hunan, China, we find that 80% of secondary infections traced back to 15% of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primary infections, which indicates substantial transmission heterogeneities. Transmission risk scales positively with the duration of exposure and the closeness of social interactions and is modulated by demographic and clinical factors. The lockdown period increases transmission risk in the family and households, whereas isolation and quarantine reduce risks across all types of contacts. The reconstructed infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom presentation. Modeling indicates that SARS-CoV-2 control requires the synergistic efforts of case isolation, contact quarantine, and population-level interventions because of the specific transmission kinetics of this virus.

Re-analysing Ebola spread in Sierra Leone: The importance of local social dynamics

BACKGROUND

The 2013-15 Ebola epidemic in West Africa was the largest so far recorded, and mainly affected three adjacent countries, Guinea, Liberia and Sierra Leone. The worst affected country (in terms of confirmed cases) was Sierra Leone. The present paper looks at the epidemic in Sierra Leone. The epidemic in this country was a concatenation of local outbreaks. These local outbreaks are not well characterized through analysis using standard numerical techniques. In part, this reflects difficulties in record collection at the height of the epidemic. This paper offers a different approach, based on application of field-based techniques of social investigation that provide a richer understanding of the epidemic.

METHODS

In a post-epidemic study (2016-18) of two districts (Bo and Moyamba) we use ethnographic data to reconstruct local infection pathways from evidence provided by affected communities, cross-referenced to records of the epidemic retained by the National Ebola Response Commission, now lodged in the Ebola Museum and Archive at Njala University. Our study documents and discusses local social and contextual factors largely missing from previously published studies.

RESULTS

Our major finding is that the epidemic in Sierra Leone was a series of local outbreaks, some of which were better contained than others. In those that were not well contained, a number of contingent factors helps explain loss of control. Several numerical studies have drawn attention to the importance of local heterogeneities in the Sierra Leone Ebola epidemic. Our qualitative study throws specific light on a number of elements that explain these heterogeneities: the role of externalities, health system deficiencies, cultural considerations and local coping capacities.

CONCLUSIONS

Social issues and local contingencies explain the spread of Ebola in Sierra Leone and are key to understanding heterogeneities in epidemiological data. Integrating ethnographic research into epidemic-response is critical to properly understand the patterns of spread and the opportunities to intervene. This conclusion has significant implications for future interdisciplinary research and interpretation of standard numerical data, and consequently for control of epidemic outbreaks.

The cost of insecurity: from flare-up to control of a major Ebola virus disease hotspot during the outbreak in the Democratic Republic of the Congo, 2019

The ongoing Ebola outbreak in the eastern Democratic Republic of the Congo is facing unprecedented levels of insecurity and violence. We evaluate the likely impact in terms of added transmissibility and cases of major security incidents in the Butembo coordination hub. We also show that despite this additional burden, an adapted response strategy involving enlarged ring vaccination around clusters of cases and enhanced community engagement managed to bring this main hotspot under control.

Coronavirus disease 2019 (COVID-19): research progress and clinical practice

Coronavirus disease 2019 (COVID-19) is a respiratory tract infection caused by SARS-CoV-2. As of March 30, 2020, there have been 693,224 reported patients with COVID-19 worldwide, with 1,446 in Japan. Currently, although aspects of the route of transmission are unclear, infection by contact and by inhaling droplets is considered to be the dominant transmission route. Inflammatory symptoms in the upper respiratory tract persist for several days to 1 week after onset, and in some patients symptoms of pneumonia worsen and become severe. The presence of underlying diseases and advanced age are risk factors for increased severity. Diagnosis is based on detection of SARS-CoV-2 by polymerase chain reaction (PCR) testing of nasopharyngeal swabs or sputum. Symptomatic management is the main treatment for this disease. Although the efficacy of several agents is currently being tested, at present there is no effective therapeutic agent. To prevent infection, in addition to standard preventive measures, measures that counteract infection by contact and droplet inhalation are important. In addition, if procedures that cause aerosolization of virus are used, then measures that prevent airborne infection should be implemented.

How decision makers can use quantitative approaches to guide outbreak responses

Decision makers are responsible for directing staffing, logistics, selecting public health interventions, communicating to professionals and the public, planning future response needs, and establishing strategic and tactical priorities along with their funding requirements. Decision makers need to rapidly synthesize data from different experts across multiple disciplines, bridge data gaps and translate epidemiological analysis into an operational set of decisions for disease control. Analytic approaches can be defined for specific response phases: investigation, scale-up and control. These approaches include: improved applications of quantitative methods to generate insightful epidemiological descriptions of outbreaks; robust investigations of causal agents and risk factors; tools to assess response needs; identifying and monitoring optimal interventions or combinations of interventions; and forecasting for response planning. Data science and quantitative approaches can improve decision-making in outbreak response. To realize these benefits, we need to develop a structured approach that will improve the quality and timeliness of data collected during outbreaks, establish analytic teams within the response structure and define a research agenda for data analytics in outbreak response.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Systematic Review of Important Viral Diseases in Africa in Light of the 'One Health' Concept

Emerging and re-emerging viral diseases are of great public health concern. The recent emergence of Severe Acute Respiratory Syndrome (SARS) related coronavirus (SARS-CoV-2) in December 2019 in China, which causes COVID-19 disease in humans, and its current spread to several countries, leading to the first pandemic in history to be caused by a coronavirus, highlights the significance of zoonotic viral diseases. Rift Valley fever, rabies, West Nile, chikungunya, dengue, yellow fever, Crimean-Congo hemorrhagic fever, Ebola, and influenza viruses among many other viruses have been reported from different African countries. The paucity of information, lack of knowledge, limited resources, and climate change, coupled with cultural traditions make the African continent a hotspot for vector-borne and zoonotic viral diseases, which may spread globally. Currently, there is no information available on the status of virus diseases in Africa. This systematic review highlights the available information about viral diseases, including zoonotic and vector-borne diseases, reported in Africa. The findings will help us understand the trend of emerging and re-emerging virus diseases within the African continent. The findings recommend active surveillance of viral diseases and strict implementation of One Health measures in Africa to improve human public health and reduce the possibility of potential pandemics due to zoonotic viruses.

Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study

Background

As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks.

Methods

In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time.

Findings

We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35-60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020.

Interpretation

News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.

Funding

Fogarty International Center, US National Institutes of Health.

Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study

BACKGROUND

As the outbreak of coronavirus disease 2019 (COVID-19) progresses, epidemiological data are needed to guide situational awareness and intervention strategies. Here we describe efforts to compile and disseminate epidemiological information on COVID-19 from news media and social networks.

METHODS

In this population-level observational study, we searched DXY.cn, a health-care-oriented social network that is currently streaming news reports on COVID-19 from local and national Chinese health agencies. We compiled a list of individual patients with COVID-19 and daily province-level case counts between Jan 13 and Jan 31, 2020, in China. We also compiled a list of internationally exported cases of COVID-19 from global news media sources (Kyodo News, The Straits Times, and CNN), national governments, and health authorities. We assessed trends in the epidemiology of COVID-19 and studied the outbreak progression across China, assessing delays between symptom onset, seeking care at a hospital or clinic, and reporting, before and after Jan 18, 2020, as awareness of the outbreak increased. All data were made publicly available in real time.

FINDINGS

We collected data for 507 patients with COVID-19 reported between Jan 13 and Jan 31, 2020, including 364 from mainland China and 143 from outside of China. 281 (55%) patients were male and the median age was 46 years (IQR 35-60). Few patients (13 [3%]) were younger than 15 years and the age profile of Chinese patients adjusted for baseline demographics confirmed a deficit of infections among children. Across the analysed period, delays between symptom onset and seeking care at a hospital or clinic were longer in Hubei province than in other provinces in mainland China and internationally. In mainland China, these delays decreased from 5 days before Jan 18, 2020, to 2 days thereafter until Jan 31, 2020 (p=0·0009). Although our sample captures only 507 (5·2%) of 9826 patients with COVID-19 reported by official sources during the analysed period, our data align with an official report published by Chinese authorities on Jan 28, 2020.

INTERPRETATION

News reports and social media can help reconstruct the progression of an outbreak and provide detailed patient-level data in the context of a health emergency. The availability of a central physician-oriented social network facilitated the compilation of publicly available COVID-19 data in China. As the outbreak progresses, social media and news reports will probably capture a diminishing fraction of COVID-19 cases globally due to reporting fatigue and overwhelmed health-care systems. In the early stages of an outbreak, availability of public datasets is important to encourage analytical efforts by independent teams and provide robust evidence to guide interventions.

FUNDING

Fogarty International Center, US National Institutes of Health.

The socio-economic distribution of exposure to Ebola: Survey evidence from Liberia and Sierra Leone

Socio-economic factors are widely believed to have been an important driver of the transmission of Ebola Virus Disease (EVD) during the West African outbreak of 2014-16, however, studies that have investigated the relationship between socio-economic status (SES) and EVD have found inconsistent results. Using nationally representative household survey data on whether respondents knew a close friend or family member with Ebola, we explore the SES determinants of EVD exposure along individual, household, and community lines in Liberia and Sierra Leone. While we find no overall association between household wealth and EVD exposure, we find that pooled data mask important differences observed within countries with higher wealth households more likely to have been exposed to EVD in Sierra Leone and the opposite relationship in Liberia. Finally, we also generally find a positive association between education and EVD exposure both at the individual and the community levels in the full sample. There is an urgent need to better understand these relationships to examine both why the outbreak spread and to help prepare for future outbreaks.

Identification of the relative timing of infectiousness and symptom onset for outbreak control

In an outbreak of an emerging disease the epidemiological characteristics of the pathogen may be largely unknown. A key determinant of ability to control the outbreak is the relative timing of infectiousness and symptom onset. We provide a method for identifying this relationship with high accuracy based on data from simulated household-stratified symptom-onset data. Further, this can be achieved with observations taken on only a few specific days, chosen optimally, within each household. The information provided by this method may inform decision making processes for outbreak response. An accurate and computationally-efficient heuristic for determining the optimal surveillance scheme is introduced. This heuristic provides a novel approach to optimal design for Bayesian model discrimination.

An assessment of Ebola-related stigma and its association with informal healthcare utilisation among Ebola survivors in Sierra Leone: a cross-sectional study

BACKGROUND

We examined the magnitude and correlates of Ebola virus disease (EVD)-related stigma among EVD survivors in Sierra Leone since their return to their communities. In addition, we determined whether EVD-related stigma is a predictor of informal health care use among EVD survivors.

METHODS

We conducted a cross-sectional study among 358 EVD survivors in five districts across all four geographic regions (Western Area, Northern Province, Eastern Province and Southern Province) of Sierra Leone. Ebola-related stigma was measured by adapting the validated HIV related stigma for people living with HIV/AIDS instrument. We also measured traditional and complementary medicine (T&CM) use (as a measure of informal healthcare use). Data were analysed using descriptive statistics and regression analysis.

RESULTS

EVD survivors report higher levels of internalised stigma (0.92 ± 0.77) compared to total enacted stigma (0.71 ± 0.61). Social isolation (0.96 ± 0.88) was the highest reported enacted stigma subscale. Ebola survivors who identified as Christians [AOR = 2.51, 95%CI: 1.15-5.49, p = 0.021], who perceived their health to be fair/poor [AOR = 2.58, 95%CI: 1.39-4.77. p = 0.003] and who reside in the northern region of Sierra Leone [AOR = 2.80, 95%CI: 1.29-6.07, p = 0.009] were more likely to experience internalised stigma. Verbal abuse [AOR = 1.95, 95%CI: 1.09-3.49, p = 0.025] and healthcare neglect [AOR = 2.35, 95%CI: 1.37-4.02, p = 0.002] were independent predictors of T&CM use among EVD survivors.

CONCLUSION

Our findings suggest EVD-related stigma (internalised and enacted) is prevalent among EVD survivors since their return to their communities. Religiosity, perceived health status and region were identified as independent predictors of internalised stigma. Verbal abuse and healthcare neglect predict informal healthcare use. EVD survivor-centred and community-driven anti-stigma programs are needed to promote EVD survivors' recovery and community re-integration.

The Emerging Threat of Ebola

Ebola is one of the deadliest infectious disease of the modern era. Over 50% of those infected die. Prior to 1976, the disease was unknown. No one knows exactly where it came from, but it is postulated that a mutation in an animal virus allowed it to jump species and infect humans. In 1976 simultaneous outbreaks of Ebola occurred in what is now South Sudan and the Democratic Republic of the Congo (DRC). For 20 years, only sporadic cases were seen, but in 1995 a new outbreak occurred killing hundreds in the DRC. Since that time the frequency of these outbreaks has been increasing. It is uncertain why this is occurring, but many associate it with increasing human encroachment into forested areas bringing people and animals into more intimate contact and increased mobility of previously remote population. This chapter will navigate Ebola in the context of global health and security.

There are multiple objectives of this chapter. First is to provide a basic understanding of Ebola disease processes and outbreak patterns. Second, is to explore the interplay between social determinants of health and Ebola. The role of technology in spreading Ebola outbreaks will be explained as will Ebola’s potential as a bioweapon. Readers will gain understanding of the link between environmental degradation and Ebola outbreaks.

This chapter will be divided into five main sections. These are (1) a case study; (2) Ebola Disease process; (3) Social determinants of health and Ebola; (4) Ebola in the modern era, and (5) the link between Ebola and environmental degradation.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Evidence-Based Clinical Management of Ebola Virus Disease and Epidemic Viral Hemorrhagic Fevers

The 2014 to 2016 Ebola virus disease outbreak underscored the threat posed by hemorrhagic fevers. Filoviral outbreaks have been identified since 1967, but data collection has remained sparse, limiting current knowledge of these illnesses. Documentation of objective physical signs and laboratory parameters and appropriate clinical management are connected and interdependent. Implementing both is necessary to improve outcomes. Clinical features include severe volume depletion due to diarrhea and vomiting, shock, rhabdomyolysis, and metabolic disturbances. Overt hemorrhage is uncommon. Point-of-care devices and inexpensive electronic equipment enable better monitoring and record keeping in resource-limited settings.

Infection Rates and Risk Factors for Infection Among Health Workers During Ebola and Marburg Virus Outbreaks: A Systematic Review

Background

Infection in health workers (HWs) has characterized outbreaks of Ebola virus disease (EVD) and Marburg virus disease (MVD). We conducted a systematic review to investigate infection and mortality rates and common exposure risks in HWs in EVD and MVD outbreaks.

Methods

We searched the EMBASE and PubMed databases to identify articles posted before 27 December 2017, with no language restrictions. Data on the number, frequency, and mortality of HW infection and exposure risks were extracted.

Results

Ninety-four articles related to 22 outbreaks were included. HW infections composed 2%-100% of cases in EVD and 5%-50% of cases in MVD outbreaks. Among exposed HWs, 0.6%-92% developed EVD, and 1%-10% developed MVD. HW infection rates were consistent through outbreaks. The most common exposure risk situations were inadequate personal protective equipment and exposure to patients with unrecognized EVD/MVD. Similar risks were reported in past EVD/MVD outbreaks and in the recent outbreak in West Africa.

Conclusions

Many outbreaks reported high proportions of infected HWs. Similar HW infection rates and exposure risk factors in both past and recent EVD and MVD outbreaks emphasize the need to improve the implementation of appropriate infection control measures consistently across all healthcare settings.

Household Transmission of Ebola Virus: Risks and Preventive Factors, Freetown, Sierra Leone, 2015

Background

Knowing risk factors for household transmission of Ebola virus is important to guide preventive measures during Ebola outbreaks.

Methods

We enrolled all confirmed persons with Ebola who were the first case in a household, December 2014-April 2015, in Freetown, Sierra Leone, and their household contacts. Cases and contacts were interviewed, contacts followed prospectively through the 21-day incubation period, and secondary cases confirmed by laboratory testing.

Results

We enrolled 150 index Ebola cases and 838 contacts; 83 (9.9%) contacts developed Ebola during 21-day follow-up. In multivariable analysis, risk factors for transmission included index case death in the household, Ebola symptoms but no reported fever, age <20 years, more days with wet symptoms; and providing care to the index case (P < .01 for each). Protective factors included avoiding the index case after illness onset and a piped household drinking water source (P < .01 for each).

Conclusions

To reduce Ebola transmission, communities should rapidly identify and follow-up all household contacts; isolate those with Ebola symptoms, including those without reported fever; and consider closer monitoring of contacts who provided care to cases. Households could consider efforts to minimize risk by designating one care provider for ill persons with all others avoiding the suspected case.

Outbreak analytics: a developing data science for informing the response to emerging pathogens

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

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.

Estimating undetected Ebola spillovers

The preparedness of health systems to detect, treat, and prevent onward transmission of Ebola virus disease (EVD) is central to mitigating future outbreaks. Early detection of outbreaks is critical to timely response, but estimating detection rates is difficult because unreported spillover events and outbreaks do not generate data. Using three independent datasets available on the distributions of secondary infections during EVD outbreaks across West Africa, in a single district (Western Area) of Sierra Leone, and in the city of Conakry, Guinea, we simulated realistic outbreak size distributions and compared them to reported outbreak sizes. These three empirical distributions lead to estimates for the proportion of detected spillover events and small outbreaks of 26% (range 8-40%, based on the full outbreak data), 48% (range 39-62%, based on the Sierra Leone data), and 17% (range 11-24%, based on the Guinea data). We conclude that at least half of all spillover events have failed to be reported since EVD was first recognized. We also estimate the probability of detecting outbreaks of different sizes, which is likely less than 10% for single-case spillover events. Comparing models of the observation process also suggests the probability of detecting an outbreak is not simply the cumulative probability of independently detecting any one individual. Rather, we find that any individual's probability of detection is highly dependent upon the size of the cluster of cases. These findings highlight the importance of primary health care and local case management to detect and contain undetected early stage outbreaks at source.

Sub- or supercritical transmissibilities in a finite disease outbreak: Symmetry in outbreak properties of a disease conditioned on extinction

This work is concerned with the transmissibility of a disease, on observation of an outbreak of limited size. When such an outbreak occurs, an accurate estimate of the transmissibility of the responsible pathogen is essential for an appropriate response to future outbreaks. Transmissibility is usually characterised in terms of the reproduction number, R, which is the mean number of new cases of infection produced by a single infectious individual. A subcritical reproduction number (R < 1) guarantees that an outbreak will eventually die out of its own accord. By contrast, a supercritical reproduction number (R > 1) does not guarantee spread of the disease, since even with appreciable transmissibility, an outbreak may become extinct due to stochastic effects associated with a small number of infected individuals. Once the number of infectious individuals is conditioned on extinction, we show that an exact symmetry of the underlying theory ensures two distinct values of R, one larger than unity, the other smaller than unity, for which all outbreak properties are identical, with no signature of difference. Therefore a disease with a subcritical R, or its supercritical counterpart, when conditioned on extinction, have, for a given outbreak, identical individual likelihoods. In the full likelihood, this symmetry is lost, since the individual likelihood for a subcritical R is weighted by an extinction probability of unity, but the individual likelihood of a supercritical R is weighted by a sub-unity extinction probability. However, the inference can still benefit from the underlying symmetry, since it yields a mapping of all supercritical reproduction numbers onto the subcritical domain (R < 1), thereby speeding up evaluation of the likelihood profile. The symmetry holds in the standard situation, where the distribution of secondary cases is Poisson, as well as where this distribution has a negative binomial form and super-spreading can occur.

Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data

There exists significant interest in developing statistical and computational tools for inferring 'who infected whom' in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close contact, including Ebola virus (EBOV), severe acute respiratory syndrome coronavirus (SARS-CoV) and Mycobacterium tuberculosis (TB), and routinely used to reconstruct transmission chains. As an improvement over previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework. By analyzing simulated outbreaks under various contact tracing scenarios, we demonstrate that contact data significantly improves our ability to reconstruct transmission trees, even under realistic limitations on the coverage of the contact tracing effort and the amount of non-infectious mixing between cases. Indeed, contact data is equally or more informative than fully sampled whole genome sequence data in certain scenarios. We then use our method to analyze the early stages of the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with contact data and genetic sequence in a probabilistic manner for the first time. This simple yet flexible model can easily be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains.

Fogarty International Center collaborative networks in infectious disease modeling: Lessons learnt in research and capacity building

Due to a combination of ecological, political, and demographic factors, the emergence of novel pathogens has been increasingly observed in animals and humans in recent decades. Enhancing global capacity to study and interpret infectious disease surveillance data, and to develop data-driven computational models to guide policy, represents one of the most cost-effective, and yet overlooked, ways to prepare for the next pandemic. Epidemiological and behavioral data from recent pandemics and historic scourges have provided rich opportunities for validation of computational models, while new sequencing technologies and the 'big data' revolution present new tools for studying the epidemiology of outbreaks in real time. For the past two decades, the Division of International Epidemiology and Population Studies (DIEPS) of the NIH Fogarty International Center has spearheaded two synergistic programs to better understand and devise control strategies for global infectious disease threats. The Multinational Influenza Seasonal Mortality Study (MISMS) has strengthened global capacity to study the epidemiology and evolutionary dynamics of influenza viruses in 80 countries by organizing international research activities and training workshops. The Research and Policy in Infectious Disease Dynamics (RAPIDD) program and its precursor activities has established a network of global experts in infectious disease modeling operating at the research-policy interface, with collaborators in 78 countries. These activities have provided evidence-based recommendations for disease control, including during large-scale outbreaks of pandemic influenza, Ebola and Zika virus. Together, these programs have coordinated international collaborative networks to advance the study of emerging disease threats and the field of computational epidemic modeling. A global community of researchers and policy-makers have used the tools and trainings developed by these programs to interpret infectious disease patterns in their countries, understand modeling concepts, and inform control policies. Here we reflect on the scientific achievements and lessons learnt from these programs (h-index = 106 for RAPIDD and 79 for MISMS), including the identification of outstanding researchers and fellows; funding flexibility for timely research workshops and working groups (particularly relative to more traditional investigator-based grant programs); emphasis on group activities such as large-scale modeling reviews, model comparisons, forecasting challenges and special journal issues; strong quality control with a light touch on outputs; and prominence of training, data-sharing, and joint publications.

Prevalence of infection among asymptomatic and paucisymptomatic contact persons exposed to Ebola virus in Guinea: a retrospective, cross-sectional observational study

BACKGROUND

The prevalence of Ebola virus infection among people who have been in contact with patients with Ebola virus disease remains unclear, but is essential to understand the dynamics of transmission. This study aimed to identify risk factors for seropositivity and to estimate the prevalence of Ebola virus infection in unvaccinated contact persons.

METHODS

In this retrospective, cross-sectional observational study, we recruited individuals between May 12, 2016, and Sept 8, 2017, who had been in physical contact with a patient with Ebola virus disease, from four medical centres in Guinea (Conakry, Macenta, N'zérékoré, and Forécariah). Contact persons had to be 7 years or older and not diagnosed with Ebola virus disease. Participants were selected through the Postebogui survivors' cohort. We collected self-reported information on exposure and occurrence of symptoms after exposure using a questionnaire, and tested antibody response against glycoprotein, nucleoprotein, and 40-kDa viral protein of Zaire Ebola virus by taking a blood sample. The prevalence of Ebola virus infection was estimated with a latent class model.

FINDINGS

1721 contact persons were interviewed and given blood tests, 331 of whom reported a history of vaccination so were excluded, resulting in a study population of 1390. Symptoms were reported by 216 (16%) contact persons. The median age of participants was 26 years (range 7-88) and 682 (49%) were male. Seropositivity was identified in 18 (8·33%, 95% CI 5·01-12·80) of 216 paucisymptomatic contact persons and 39 (3·32%, 5·01-12·80) of 1174 (2-4) asymptomatic individuals (p=0·0021). Seropositivity increased with participation in burial rituals (adjusted odds ratio [aOR] 2·30, 95% CI 1·21-4·17; p=0·0079) and exposure to blood or vomit (aOR 2·15, 1·23-3·91; p=0·0090). Frequency of Ebola virus infection varied from 3·06% (95% CI 1·84-5·05) in asymptomatic contact persons who did not participate in burial rituals to 5·98% (2·81-8·18) in those who did, and from 7·17% (3·94-9·09) in paucisymptomatic contact persons who did not participate in burial rituals to 17·16% (12·42-22·31) among those who did.

INTERPRETATION

This study provides a new assessment of the prevalence of Ebola virus infection among contact persons according to exposure, provides evidence for the occurrence of paucisymptomatic cases, and reinforces the importance of closely monitoring at-risk contact persons.

FUNDING

Institut National de la Santé et de la Recherche Médicale, Reacting, the French Ebola Task Force, Institut de Recherche pour le Développement, and Montpellier University Of Excellence-University of Montpellier.

Exploratory investigation of region level risk factors of Ebola Virus Disease in West Africa

Background

Ebola Virus Disease (EVD) is a highly infectious disease that has produced over 25,000 cases in the past 50 years. While many past outbreaks resulted in relatively few cases, the 2014 outbreak in West Africa was the most deadly occurrence of EVD to date, producing over 15,000 confirmed cases.

Objective

In this study, we investigated population level predictors of EVD risk at the regional level in Sierra Leone, Liberia, and Guinea.

Methods

Spatial and descriptive analyses were conducted to assess distribution of EVD cases. Choropleth maps showing the spatial distribution of EVD risk across the study area were generated in ArcGIS. Poisson and negative binomial models were then used to investigate population and regional predictors of EVD risk.

Results

Results indicated that the risk of EVD was significantly lower in areas with higher proportions of: (a) the population living in urban areas, (b) households with a low quality or no toilets, and (c) married men working in blue collar jobs. However, risk of EVD was significantly higher in areas with high mean years of education.

Conclusions

The identified significant predictors of high risk were associated with areas with higher levels of urbanization. This may be due to higher population densities in the more urban centers and hence higher potential of infectious contact. However, there is need to better understand the role of urbanization and individual contact structure in an Ebola outbreak. We discuss shortcomings in available data and emphasize the need to consider spatial scale in future data collection and epidemiological studies.

Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April-May, 2018: an epidemiological study

BACKGROUND

On May 8, 2018, the Government of the Democratic Republic of the Congo reported an outbreak of Ebola virus disease in Équateur Province in the northwest of the country. The remoteness of most affected communities and the involvement of an urban centre connected to the capital city and neighbouring countries makes this outbreak the most complex and high risk ever experienced by the Democratic Republic of the Congo. We provide early epidemiological information arising from the ongoing investigation of this outbreak.

METHODS

We classified cases as suspected, probable, or confirmed using national case definitions of the Democratic Republic of the Congo Ministère de la Santé Publique. We investigated all cases to obtain demographic characteristics, determine possible exposures, describe signs and symptoms, and identify contacts to be followed up for 21 days. We also estimated the reproduction number and projected number of cases for the 4-week period from May 25, to June 21, 2018.

FINDINGS

As of May 30, 2018, 50 cases (37 confirmed, 13 probable) of Zaire ebolavirus were reported in the Democratic Republic of the Congo. 21 (42%) were reported in Bikoro, 25 (50%) in Iboko, and four (8%) in Wangata health zones. Wangata is part of Mbandaka, the urban capital of Équateur Province, which is connected to major national and international transport routes. By May 30, 2018, 25 deaths from Ebola virus disease had been reported, with a case fatality ratio of 56% (95% CI 39-72) after adjustment for censoring. This case fatality ratio is consistent with estimates for the 2014-16 west African Ebola virus disease epidemic (p=0·427). The median age of people with confirmed or probable infection was 40 years (range 8-80) and 30 (60%) were male. The most commonly reported signs and symptoms in people with confirmed or probable Ebola virus disease were fever (40 [95%] of 42 cases), intense general fatigue (37 [90%] of 41 cases), and loss of appetite (37 [90%] of 41 cases). Gastrointestinal symptoms were frequently reported, and 14 (33%) of 43 people reported haemorrhagic signs. Time from illness onset and hospitalisation to sample testing decreased over time. By May 30, 2018, 1458 contacts had been identified, of which 746 (51%) remained under active follow-up. The estimated reproduction number was 1·03 (95% credible interval 0·83-1·37) and the cumulative case incidence for the outbreak by June 21, 2018, is projected to be 78 confirmed cases (37-281), assuming heterogeneous transmissibility.

INTERPRETATION

The ongoing Ebola virus outbreak in the Democratic Republic of the Congo has similar epidemiological features to previous Ebola virus disease outbreaks. Early detection, rapid patient isolation, contact tracing, and the ongoing vaccination programme should sufficiently control the outbreak. The forecast of the number of cases does not exceed the current capacity to respond if the epidemiological situation does not change. The information presented, although preliminary, has been essential in guiding the ongoing investigation and response to this outbreak.

FUNDING

None.

epicontacts: Handling, visualisation and analysis of epidemiological contacts

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub.

epicontacts: Handling, visualisation and analysis of epidemiological contacts

Epidemiological outbreak data is often captured in line list and contact format to facilitate contact tracing for outbreak control. epicontacts is an R package that provides a unique data structure for combining these data into a single object in order to facilitate more efficient visualisation and analysis. The package incorporates interactive visualisation functionality as well as network analysis techniques. Originally developed as part of the Hackout3 event, it is now developed, maintained and featured as part of the R Epidemics Consortium (RECON). The package is available for download from the Comprehensive R Archive Network (CRAN) and GitHub.

Herd Immunity to Ebolaviruses Is Not a Realistic Target for Current Vaccination Strategies

The recent West African Ebola virus pandemic, which affected >28,000 individuals increased interest in anti-Ebolavirus vaccination programs. Here, we systematically analyzed the requirements for a prophylactic vaccination program based on the basic reproductive number (R₀, i.e., the number of secondary cases that result from an individual infection). Published R₀ values were determined by systematic literature research and ranged from 0.37 to 20. R₀s ≥ 4 realistically reflected the critical early outbreak phases and superspreading events. Based on the R₀, the herd immunity threshold (I_c) was calculated using the equation I_c = 1 - (1/R₀). The critical vaccination coverage (V_c) needed to provide herd immunity was determined by including the vaccine effectiveness (E) using the equation V_c = I_c/E. At an R₀ of 4, the I_c is 75% and at an E of 90%, more than 80% of a population need to be vaccinated to establish herd immunity. Such vaccination rates are currently unrealistic because of resistance against vaccinations, financial/logistical challenges, and a lack of vaccines that provide long-term protection against all human-pathogenic Ebolaviruses. Hence, outbreak management will for the foreseeable future depend on surveillance and case isolation. Clinical vaccine candidates are only available for Ebola viruses. Their use will need to be focused on health-care workers, potentially in combination with ring vaccination approaches.

Quantifying Transmission Heterogeneity Using Both Pathogen Phylogenies and Incidence Time Series

Heterogeneity in individual-level transmissibility can be quantified by the dispersion parameter k of the offspring distribution. Quantifying heterogeneity is important as it affects other parameter estimates, it modulates the degree of unpredictability of an epidemic, and it needs to be accounted for in models of infection control. Aggregated data such as incidence time series are often not sufficiently informative to estimate k. Incorporating phylogenetic analysis can help to estimate k concurrently with other epidemiological parameters. We have developed an inference framework that uses particle Markov Chain Monte Carlo to estimate k and other epidemiological parameters using both incidence time series and the pathogen phylogeny. Using the framework to fit a modified compartmental transmission model that includes the parameter k to simulated data, we found that more accurate and less biased estimates of the reproductive number were obtained by combining epidemiological and phylogenetic analyses. However, k was most accurately estimated using pathogen phylogeny alone. Accurately estimating k was necessary for unbiased estimates of the reproductive number, but it did not affect the accuracy of reporting probability and epidemic start date estimates. We further demonstrated that inference was possible in the presence of phylogenetic uncertainty by sampling from the posterior distribution of phylogenies. Finally, we used the inference framework to estimate transmission parameters from epidemiological and genetic data collected during a poliovirus outbreak. Despite the large degree of phylogenetic uncertainty, we demonstrated that incorporating phylogenetic data in parameter inference improved the accuracy and precision of estimates.

Unreported cases in the 2014-2016 Ebola epidemic: Spatiotemporal variation, and implications for estimating transmission

In the recent 2014–2016 Ebola epidemic in West Africa, non-hospitalized cases were an important component of the chain of transmission. However, non-hospitalized cases are at increased risk of going unreported because of barriers to access to healthcare. Furthermore, underreporting rates may fluctuate over space and time, biasing estimates of disease transmission rates, which are important for understanding spread and planning control measures. We performed a retrospective analysis on community deaths during the recent Ebola epidemic in Sierra Leone to estimate the number of unreported non-hospitalized cases, and to quantify how Ebola reporting rates varied across locations and over time. We then tested if variation in reporting rates affected the estimates of disease transmission rates that were used in surveillance and response. We found significant variation in reporting rates among districts, and district-specific rates of increase in reporting over time. Correcting time series of numbers of cases for variable reporting rates led, in some instances, to different estimates of the time-varying reproduction number of the epidemic, particularly outside the capital. Future analyses that compare Ebola transmission rates over time and across locations may be improved by considering the impacts of differential reporting rates.

Epidemics are defined by a surge of cases of a disease, yet often a significant number of cases in an epidemic are never reported, for example because not all infected individuals have access to medical care. This underreporting can introduce bias into analyses of disease spread, by distorting patterns in where and when the most cases are observed. Conversely, quantifying underreporting can improve epidemic forecasts and containment strategies. In this study, we analyze data from the recent Ebola epidemic in West Africa, including the time, location and Ebola status of 6491 individual community burials, conducted over 25 weeks in four districts in Sierra Leone. We quantify how reporting rates varied over space and time, and show that estimates of transmission rates that are corrected for dynamic underreporting diverge significantly from uncorrected estimates, particularly earlier in the epidemic and outside the capital.

Contact tracing performance during the Ebola virus disease outbreak in Kenema district, Sierra Leone

Contact tracing in an Ebola virus disease (EVD) outbreak is the process of identifying individuals who may have been exposed to infected persons with the virus, followed by monitoring for 21 days (the maximum incubation period) from the date of the most recent exposure. The goal is to achieve early detection and isolation of any new cases in order to prevent further transmission. We performed a retrospective data analysis of 261 probable and confirmed EVD cases in the national EVD database and 2525 contacts in the Contact Line Lists in Kenema district, Sierra Leone between 27 April and 4 September 2014 to assess the performance of contact tracing during the initial stage of the outbreak. The completion rate of the 21-day monitoring period was 89% among the 2525 contacts. However, only 44% of the EVD cases had contacts registered in the Contact Line List and 6% of probable or confirmed cases had previously been identified as contacts. Touching the body fluids of the case and having direct physical contact with the body of the case conferred a 9- and 20-fold increased risk of EVD status, respectively. Our findings indicate that incompleteness of contact tracing led to considerable unmonitored transmission in the early months of the epidemic. To improve the performance of early outbreak contact tracing in resource poor settings, our results suggest the need for prioritized contact tracing after careful risk assessment and better alignment of Contact Line Listing with case ascertainment and investigation.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'.

Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013-2016

The 2013–2016 Ebola outbreak in West Africa is the largest on record with 28 616 confirmed, probable and suspected cases and 11 310 deaths officially recorded by 10 June 2016, the true burden probably considerably higher. The case fatality ratio (CFR: proportion of cases that are fatal) is a key indicator of disease severity useful for gauging the appropriate public health response and for evaluating treatment benefits, if estimated accurately. We analysed individual-level clinical outcome data from Guinea, Liberia and Sierra Leone officially reported to the World Health Organization. The overall mean CFR was 62.9% (95% CI: 61.9% to 64.0%) among confirmed cases with recorded clinical outcomes. Age was the most important modifier of survival probabilities, but country, stage of the epidemic and whether patients were hospitalized also played roles. We developed a statistical analysis to detect outliers in CFR between districts of residence and treatment centres (TCs), adjusting for known factors influencing survival and identified eight districts and three TCs with a CFR significantly different from the average. From the current dataset, we cannot determine whether the observed variation in CFR seen by district or treatment centre reflects real differences in survival, related to the quality of care or other factors or was caused by differences in reporting practices or case ascertainment.

This article is part of the themed issue ‘The 2013–2016 West African Ebola epidemic: data, decision-making and disease control’.

Strategy and technology to prevent hospital-acquired infections: Lessons from SARS, Ebola, and MERS in Asia and West Africa

Hospital-acquired infections (HAIs) are serious problems for healthcare systems, especially in developing countries where public health infrastructure and technology for infection preventions remain undeveloped. Here, we characterized how strategy and technology could be mobilized to improve the effectiveness of infection prevention and control in hospitals during the outbreaks of Ebola, Middle East respiratory syndrome (MERS), and severe acute respiratory syndrome (SARS) in Asia and West Africa. Published literature on the hospital-borne outbreaks of SARS, Ebola, and MERS in Asia and West Africa was comprehensively reviewed. The results showed that healthcare systems and hospital management in affected healthcare facilities had poor strategies and inadequate technologies and human resources for the prevention and control of HAIs, which led to increased morbidity, mortality, and unnecessary costs. We recommend that governments worldwide enforce disaster risk management, even when no outbreaks are imminent. Quarantine and ventilation functions should be taken into consideration in architectural design of hospitals and healthcare facilities. We also recommend that health authorities invest in training healthcare workers for disease outbreak response, as their preparedness is essential to reducing disaster risk.