Use of Capture-Recapture to Estimate Underreporting of Ebola Virus Disease, Montserrado County, Liberia

Methodological issues of retrospective surveys for measuring mortality of highly clustered diseases: case study of the 2014-16 Ebola outbreak in Bo District, Sierra Leone

BACKGROUND

There is a lack of empirical data on design effects (DEFF) for mortality rate for highly clustered data such as with Ebola virus disease (EVD), along with a lack of documentation of methodological limitations and operational utility of mortality estimated from cluster-sampled studies when the DEFF is high.

OBJECTIVES

The objectives of this paper are to report EVD mortality rate and DEFF estimates, and discuss the methodological limitations of cluster surveys when data are highly clustered such as during an EVD outbreak.

METHODS

We analysed the outputs of two independent population-based surveys conducted at the end of the 2014-2016 EVD outbreak in Bo District, Sierra Leone, in urban and rural areas. In each area, 35 clusters of 14 households were selected with probability proportional to population size. We collected information on morbidity, mortality and changes in household composition during the recall period (May 2014 to April 2015). Rates were calculated for all-cause, all-age, under-5 and EVD-specific mortality, respectively, by areas and overall. Crude and adjusted mortality rates were estimated using Poisson regression, accounting for the surveys sample weights and the clustered design.

RESULTS

Overall 980 households and 6,522 individuals participated in both surveys. A total of 64 deaths were reported, of which 20 were attributed to EVD. The crude and EVD-specific mortality rates were 0.35/10,000 person-days (95%CI: 0.23-0.52) and 0.12/10,000 person-days (95%CI: 0.05-0.32), respectively. The DEFF for EVD mortality was 5.53, and for non-EVD mortality, it was 1.53. DEFF for EVD-specific mortality was 6.18 in the rural area and 0.58 in the urban area. DEFF for non-EVD-specific mortality was 1.87 in the rural area and 0.44 in the urban area.

CONCLUSION

Our findings demonstrate a high degree of clustering; this contributed to imprecise mortality estimates, which have limited utility when assessing the impact of disease. We provide DEFF estimates that can inform future cluster surveys and discuss design improvements to mitigate the limitations of surveys for highly clustered data.

Using next generation matrices to estimate the proportion of infections that are not detected in an outbreak

Contact tracing, where exposed individuals are followed up to break ongoing transmission chains, is a key pillar of outbreak response for infectious disease outbreaks. Unfortunately, these systems are not fully effective, and infections can still go undetected as people may not remember all their contacts or contacts may not be traced successfully. A large proportion of undetected infections suggests poor contact tracing and surveillance systems, which could be a potential area of improvement for a disease response. In this paper, we present a method for estimating the proportion of infections that are not detected during an outbreak. Our method uses next generation matrices that are parameterized by linked contact tracing data and case line-lists. We validate the method using simulated data from an individual-based model and then investigate two case studies: the proportion of undetected infections in the SARS-CoV-2 outbreak in New Zealand during 2020 and the Ebola epidemic in Guinea during 2014. We estimate that only 5.26% of SARS-CoV-2 infections were not detected in New Zealand during 2020 (95% credible interval: 0.243 - 16.0%) if 80% of contacts were under active surveillance but depending on assumptions about the ratio of contacts not under active surveillance versus contacts under active surveillance 39.0% or 37.7% of Ebola infections were not detected in Guinea (95% credible intervals: 1.69 - 87.0% or 1.70 - 80.9%).

Mathematical modeling in perspective of vector-borne viral infections: a review

Background

Viral diseases are highly widespread infections caused by viruses. These viruses are passing from one human to other humans through a certain medium. The medium might be mosquito, animal, reservoir and food, etc. Here, the population of both human and mosquito vectors are important.

Main body of the abstract

The main objectives are here to introduce the historical perspective of mathematical modeling, enable the mathematical modeler to understand the basic mathematical theory behind this and present a systematic review on mathematical modeling for four vector-borne viral diseases using the deterministic approach. Furthermore, we also introduced other mathematical techniques to deal with vector-borne diseases. Mathematical models could help forecast the infectious population of humans and vectors during the outbreak.

Short conclusion

This study will be helpful for mathematical modelers in vector-borne diseases and ready-made material in the review for future advancement in the subject. This study will not only benefit vector-borne conditions but will enable ideas for other illnesses.

Sensitivity of contact-tracing for COVID-19 in Thailand: a capture-recapture application

BACKGROUND

We investigate the completeness of contact tracing for COVID-19 during the first wave of the COVID-19 pandemic in Thailand, from early January 2020 to 30 June 2020.

METHODS

Uni-list capture-recapture models were applied to the frequency distributions of index cases to inform two questions: (1) the unobserved number of index cases with contacts, and (2) the unobserved number of index cases with secondary cases among their contacts.

RESULTS

Generalized linear models (using Poisson and logistic families) did not return any significant predictor (age, sex, nationality, number of contacts per case) on the risk of transmission and hence capture-recapture models did not adjust for observed heterogeneity. Best fitting models, a zero truncated negative binomial for question 1 and zero-truncated Poisson for question 2, returned sensitivity estimates for contact tracing performance of 77.6% (95% CI = 73.75-81.54%) and 67.6% (95% CI = 53.84-81.38%), respectively. A zero-inflated negative binomial model on the distribution of index cases with secondary cases allowed the estimation of the effective reproduction number at 0.14 (95% CI = 0.09-0.22), and the overdispersion parameter at 0.1.

CONCLUSION

Completeness of COVID-19 contact tracing in Thailand during the first wave appeared moderate, with around 67% of infectious transmission chains detected. Overdispersion was present suggesting that most of the index cases did not result in infectious transmission chains and the majority of transmission events stemmed from a small proportion of index cases.

Novel Use of Capture-Recapture Methods to Estimate Completeness of Contact Tracing during an Ebola Outbreak, Democratic Republic of the Congo, 2018-2020

Despite its critical role in containing outbreaks, the efficacy of contact tracing, measured as the sensitivity of case detection, remains an elusive metric. We estimated the sensitivity of contact tracing by applying unilist capture-recapture methods on data from the 2018–2020 outbreak of Ebola virus disease in the Democratic Republic of the Congo. To compute sensitivity, we applied different distributional assumptions to the zero-truncated count data to estimate the number of unobserved case-patients with any contacts and infected contacts. Geometric distributions were the best-fitting models. Our results indicate that contact tracing efforts identified almost all (n = 792, 99%) of case-patients with any contacts but only half (n = 207, 48%) of case-patients with infected contacts, suggesting that contact tracing efforts performed well at identifying contacts during the listing stage but performed poorly during the contact follow-up stage. We discuss extensions to our work and potential applications for the ongoing coronavirus pandemic.

Lessons learned from implementation of a national hotline for Ebola virus disease emergency preparedness in South Sudan

Background

The world’s second largest Ebola outbreak occurred in the Democratic Republic of Congo from 2018 to 2020. At the time, risk of cross-border spread into South Sudan was very high. Thus, the South Sudan Ministry of Health scaled up Ebola preparedness activities in August 2018, including implementation of a 24-h, toll-free Ebola virus disease (EVD) hotline. The primary purpose was the hotline was to receive EVD alerts and the secondary goal was to provide evidence-based EVD messages to the public.

Methods

To assess whether the hotline augmented Ebola preparedness activities in a protracted humanitarian emergency context, we reviewed 22 weeks of call logs from January to June 2019. Counts and percentages were calculated for all available data.

Results

The hotline received 2114 calls during the analysis period, and an additional 1835 missed calls were documented. Callers used the hotline throughout 24-h of the day and were most often men and individuals living in Jubek state, where the national capital is located. The leading reasons for calling were to learn more about EVD (68%) or to report clinical signs or symptoms (16%). Common EVD-related questions included EVD signs and symptoms, transmission, and prevention. Only one call was documented as an EVD alert, and there was no documentation of reported symptoms or whether the person met the EVD case definition.

Conclusions

Basic surveillance information was not collected from callers. To trigger effective outbreak investigation from hotline calls, the hotline should capture who is reporting and from where, symptoms and travel history, and whether this information should be further investigated. Electronic data capture will enhance data quality and availability of information for review. Additionally, the magnitude of missed calls presents a major challenge. When calls are answered, there is potential to provide health communication, so risk communication needs should be considered. However, prior to hotline implementation, governments should critically assess whether their hotline would yield actionable data and if other data sources for surveillance or community concerns are available.

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.

SARS-CoV-2 infection: a global outbreak and its implication on public health

BACKGROUND

A novel corona virus is formally named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which results in causing coronavirus disease 2019 (COVID-19). It is the latest prevalent pandemic worldwide when compared to other infectious diseases like Avian flu, Middle East respiratory syndrome and severe acute respiratory syndrome (SARS).

MAIN BODY

Coronavirus disease 2019 (COVID-19) is currently occurring pandemic over world. It was emerged in Wuhan, China, in the end of December 2019 and spreading across worldwide. As the coronavirus is spreading easily through direct contact with infected people droplets, inhalation, and also air droplets, it hit up a huge amount of population even reported with death. Still, with small amounts of asymptomatic transmission between people it spreads throughout the globe. People need special care to protect from the transmission of disease. However, there are no drugs so far that shows efficacy; there is an immediate need for the development of vaccines. In order to decrease the COVID-19 cases, organizations rapidly involve in the preparation of vaccine and many vaccines have been developed by various countries. The governments took safety measures to control the spread of virus and also to minimize morbidity and mortality rate to least possible.

CONCLUSION

The purpose of this review article is to increase our understanding of COVID-19 and facilitate the people to take a move in facing challenges of the world.

Systematic analysis of the scientific literature on population surveillance

Introduction

Population surveillance provides data on the health status of the population through continuous scrutiny of different indicators. Identifying risk factors is essential for the quickly detecting and controlling of epidemic outbreaks and reducing the incidence of cross-infections and non-communicable diseases. The objective of the present study is to analyze research on population surveillance, identifying the main topics of interest for investigators in the area.

Methodology

We included documents indexed in the Web of Science Core Collection in the period from 2000 to 2019 and assigned with the generic Medical Subject Heading (MeSH) “population surveillance” or its related terms (“public health surveillance,” “sentinel surveillance” or “biosurveillance”). A co-occurrence analysis was undertaken to identify the document clusters comprising the main research topics. Scientific production, collaboration, and citation patterns in each of the clusters were characterized bibliometrically. We also analyzed research on coronaviruses, relating the results obtained to the management of the COVID-19 pandemic.

Results

We included 39,184 documents, which reflected a steady growth in scientific output driven by papers on “Public, Environmental & Occupational Health” (21.62% of the documents) and “Infectious Diseases” (10.49%). Research activity was concentrated in North America (36.41%) and Europe (32.09%). The USA led research in the area (40.14% of documents). Ten topic clusters were identified, including “Disease Outbreaks,” which is closely related to two other clusters (“Genetics” and “Influenza”). Other clusters of note were “Cross Infections” as well as one that brought together general public health concepts and topics related to non-communicable diseases (cardiovascular and coronary diseases, mental diseases, diabetes, wound and injuries, stroke, and asthma). The rest of the clusters addressed “Neoplasms,” “HIV,” “Pregnancy,” “Substance Abuse/Obesity,” and “Tuberculosis.” Although research on coronavirus has focused on population surveillance only occasionally, some papers have analyzed and collated guidelines whose relevance to the dissemination and management of the COVID-19 pandemic has become obvious. Topics include tracing the spread of the virus, limiting mass gatherings that would facilitate its propagation, and the imposition of quarantines. There were important differences in the scientific production and citation of different clusters: the documents on mental illnesses, stroke, substance abuse/obesity, and cross-infections had much higher citations than the clusters on disease outbreaks, tuberculosis, and especially coronavirus, where these values are substantially lower.

Conclusions

The role of population surveillance should be strengthened, promoting research and the development of public health surveillance systems in countries whose contribution to the area is limited.

Rigorous surveillance is necessary for high confidence in end-of-outbreak declarations for Ebola and other infectious diseases

The World Health Organization considers an Ebola outbreak to have ended once 42 days have passed since the last possible exposure to a confirmed case. Benefits of a quick end-of-outbreak declaration, such as reductions in trade/travel restrictions, must be balanced against the chance of flare-ups from undetected residual cases. We show how epidemiological modelling can be used to estimate the surveillance level required for decision-makers to be confident that an outbreak is over. Results from a simple model characterizing an Ebola outbreak suggest that a surveillance sensitivity (i.e. case reporting percentage) of 79% is necessary for 95% confidence that an outbreak is over after 42 days without symptomatic cases. With weaker surveillance, unrecognized transmission may still occur: if the surveillance sensitivity is only 40%, then 62 days must be waited for 95% certainty. By quantifying the certainty in end-of-outbreak declarations, public health decision-makers can plan and communicate more effectively. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This issue is linked with the earlier theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Use of Capture-Recapture Analysis to Assess Reporting Completeness of Births to Hepatitis B-Positive Women in New York City, 2013-2014

OBJECTIVES

The New York City (NYC) Department of Health and Mental Hygiene (DOHMH) depends on reporting by health care facilities and laboratories for disease surveillance. Our objective was to evaluate the completeness of DOHMH surveillance to identify births to hepatitis B virus (HBV)-positive women to prevent perinatal transmission.

METHODS

We identified infants born to HBV-positive women by matching mothers of all infants born in NYC during May 1, 2013-May 1, 2014, identified from the Citywide Immunization Registry (CIR) to persons with HBV-positive laboratory reports in the Electronic Laboratory Reporting (ELR) system. We then matched infants born to mothers identified in the CIR/ELR match to infants born to HBV-positive women from the DOHMH perinatal HBV surveillance database. We performed capture-recapture analysis to evaluate completeness of DOHMH case identification. We compared the proportion of infants born to HBV-positive mothers reported to DOHMH with the proportion of infants identified only through the CIR/ELR match for receipt of postexposure prophylaxis (PEP) and completion of the HBV vaccination series and post-vaccination serology testing.

RESULTS

Of 1662 infants identified from the CIR/ELR match and 1554 infants in the DOHMH database, 1493 infants matched. Of 169 infants only in the CIR/ELR data set, 55 were born to HBV-positive women residing in NYC. Sixty-one infants were only in the DOHMH database. An estimated 2 infants were not identified by either method. The CIR/ELR match increased infant identification by 3.5%, from 1554 to 1609 infants. The proportion of infants who received PEP was significantly higher among infants whose mothers were reported to DOHMH (vs not reported to DOHMH).

PRACTICAL IMPLICATIONS

Use of the CIR/ELR match may further improve DOHMH identification of infants born to HBV-positive women and receipt of infant PEP.

Coccidioidomycosis: An underreported cause of death-Arizona, 2008-2013

In Arizona during 1997-2013, coccidioidomycosis increased from 21 to 90 cases/100,000 population, but coccidioidomycosis-associated deaths remained stable at 3-6 deaths/million population. We used the capture-recapture method by using death certificates and hospital discharge data to more fully estimate the total number of coccidioidomycosis-attributable deaths and compared this with published estimates. Death certificates were included if any cause of death included coccidioidomycosis; hospital discharge data deaths were included if any discharge diagnosis included coccidioidomycosis and laboratory confirmation. Among deaths during 2008-2013, we identified 529 coccidioidomycosis-attributable deaths from death certificates and 560 from hospital discharge data, with 251 deaths identified in both databases. Capture-recapture estimated 1,178 total coccidioidomycosis-attributable deaths, compared with 164 deaths (underlying cause of death) or 529 deaths (any cause of death) on death certificates. Coccidioidomycosis-attributable deaths are underreported from two- to sevenfold on Arizona death certificates, demonstrating an education need for death certifiers to document coccidioidomycosis mortality.

Cell Phones ≠ Self and Other Problems with Big Data Detection and Containment during Epidemics

Evidence from Sierra Leone reveals the significant limitations of big data in disease detection and containment efforts. Early in the 2014-2016 Ebola epidemic in West Africa, media heralded HealthMap's ability to detect the outbreak from newsfeeds. Later, big data-specifically, call detail record data collected from millions of cell phones-was hyped as useful for stopping the disease by tracking contagious people. It did not work. In this article, I trace the causes of big data's containment failures. During epidemics, big data experiments can have opportunity costs: namely, forestalling urgent response. Finally, what counts as data during epidemics must include that coming from anthropological technologies because they are so useful for detection and containment.

Risk factors for measles mortality and the importance of decentralized case management during an unusually large measles epidemic in eastern Democratic Republic of Congo in 2013

In 2013, a large measles epidemic occurred in the Aketi Health Zone of the Democratic Republic of Congo. We conducted a two-stage, retrospective cluster survey to estimate the attack rate, the case fatality rate, and the measles-specific mortality rate during the epidemic. 1424 households containing 7880 individuals were included. The estimated attack rate was 14.0%, (35.0% among children aged <5 years). The estimated case fatality rate was 4.2% (6.1% among children aged <5 years). Spatial analysis and linear regression showed that younger children, those who did not receive care, and those living farther away from Aketi Hospital early in the epidemic had a higher risk of measles related death. Vaccination coverage prior to the outbreak was low (76%), and a delayed reactive vaccination campaign contributed to the high attack rate. We provide evidences suggesting that a comprehensive case management approach reduced measles fatality during this epidemic in rural, inaccessible resource-poor setting.

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.

Characterizing the dynamics underlying global spread of epidemics

Over the past few decades, global metapopulation epidemic simulations built with worldwide air-transportation data have been the main tool for studying how epidemics spread from the origin to other parts of the world (e.g., for pandemic influenza, SARS, and Ebola). However, it remains unclear how disease epidemiology and the air-transportation network structure determine epidemic arrivals for different populations around the globe. Here, we fill this knowledge gap by developing and validating an analytical framework that requires only basic analytics from stochastic processes. We apply this framework retrospectively to the 2009 influenza pandemic and 2014 Ebola epidemic to show that key epidemic parameters could be robustly estimated in real-time from public data on local and global spread at very low computational cost. Our framework not only elucidates the dynamics underlying global spread of epidemics but also advances our capability in nowcasting and forecasting epidemics.

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’.

Potential for broad-scale transmission of Ebola virus disease during the West Africa crisis: lessons for the Global Health security agenda

Background

The 2014–2016 Ebola crisis in West Africa had approximately eight times as many reported deaths as the sum of all previous Ebola outbreaks. The outbreak magnitude and occurrence of multiple Ebola cases in at least seven countries beyond Liberia, Sierra Leone, and Guinea, hinted at the possibility of broad-scale transmission of Ebola.

Main text

Using a modeling tool developed by the US Centers for Disease Control and Prevention during the Ebola outbreak, we estimated the number of Ebola cases that might have occurred had the disease spread beyond the three countries in West Africa to cities in other countries at high risk for disease transmission (based on late 2014 air travel patterns). We estimated Ebola cases in three scenarios: a delayed response, a Liberia-like response, and a fast response scenario. Based on our estimates of the number of Ebola cases that could have occurred had Ebola spread to other countries beyond the West African foci, we emphasize the need for improved levels of preparedness and response to public health threats, which is the goal of the Global Health Security Agenda. Our estimates suggest that Ebola could have potentially spread widely beyond the West Africa foci, had local and international health workers and organizations not committed to a major response effort. Our results underscore the importance of rapid detection and initiation of an effective, organized response, and the challenges faced by countries with limited public health systems. Actionable lessons for strengthening local public health systems in countries at high risk of disease transmission include increasing health personnel, bolstering primary and critical healthcare facilities, developing public health infrastructure (e.g. laboratory capacity), and improving disease surveillance. With stronger local public health systems infectious disease outbreaks would still occur, but their rapid escalation would be considerably less likely, minimizing the impact of public health threats such as Ebola.

Conclusions

The Ebola outbreak could have potentially spread to other countries, where limited public health surveillance and response capabilities may have resulted in additional foci. Health security requires robust local health systems that can rapidly detect and effectively respond to an infectious disease outbreak.

Electronic supplementary material

The online version of this article (10.1186/s40249-017-0373-4) contains supplementary material, which is available to authorized users.

Key data for outbreak evaluation: building on the Ebola experience

Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'.

The serology of Ebolavirus - a wider geographical range, a wider genus of viruses or a wider range of virulence?

Viruses of the genus Ebolavirus are the causative agents of Ebola virus disease (EVD), of which there have been only 25 recorded outbreaks since the discovery of Zaire and Sudan ebolaviruses in the late 1970s. Until the west African outbreak commencing in late 2013, EVD was confined to an area of central Africa stretching from the coast of Gabon through the Congo river basin and eastward to the Great Lakes. Nevertheless, population serological studies since 1976, most of which were carried out in the first two decades after that date, have suggested a wider distribution and more frequent occurrence across tropical Africa. We review this body of work, discussing the various methods employed over the years and the degree to which they can currently be regarded as reliable. We conclude that there is adequate evidence for a wider geographical range of exposure to Ebolavirus or related filoviruses and discuss three possibilities that could account for this: (a) EVD outbreaks have been misidentified as other diseases in the past; (b) unidentified, and clinically milder, species of the genus Ebolavirus circulate over a wider range than the most pathogenic species; and (c) EVD may be subclinical with a frequency high enough that smaller outbreaks may be unidentified. We conclude that the second option is the most likely and therefore predict the future discovery of other, less virulent, members of the genus Ebolavirus.

Mortality, Morbidity and Health-Seeking Behaviour during the Ebola Epidemic 2014-2015 in Monrovia Results from a Mobile Phone Survey

Between March 2014 and July 2015 at least 10,500 Ebola cases including more than 4,800 deaths occurred in Liberia, the majority in Monrovia. However, official numbers may have underestimated the size of the outbreak. Closure of health facilities and mistrust in existing structures may have additionally impacted on all-cause morbidity and mortality. To quantify mortality and morbidity and describe health-seeking behaviour in Monrovia, Médecins sans Frontières (MSF) conducted a mobile phone survey from December 2014 to March 2015. We drew a random sample of households in Monrovia and conducted structured mobile phone interviews, covering morbidity, mortality and health-seeking behaviour from 14 May 2014 until the day of the survey. We defined an Ebola-related death as any death meeting the Liberian Ebola case definition. We calculated all-cause and Ebola-specific mortality rates. The sample consisted of 6,813 household members in 905 households. We estimated a crude mortality rate (CMR) of 0.33/10,000 persons/day (95%CI:0.25–0.43) and an Ebola-specific mortality rate of 0.06/10,000 persons/day (95%-CI:0.03–0.11). During the recall period, 17 Ebola cases were reported including those who died. In the 30 days prior to the survey 277 household members were reported sick; malaria accounted for 54% (150/277). Of the sick household members, 43% (122/276) did not visit any health care facility. The mobile phone-based survey was found to be a feasible and acceptable alternative method when data collection in the community is impossible. CMR was estimated well below the emergency threshold of 1/10,000 persons/day. Non-Ebola-related mortality in Monrovia was not higher than previous national estimates of mortality for Liberia. However, excess mortality directly resulting from Ebola did occur in the population. Importantly, the small proportion of sick household members presenting to official health facilities when sick might pose a challenge for future outbreak detection and mitigation. Substantial reported health-seeking behaviour outside of health facilities may also suggest the need for adapted health messaging and improved access to health care.

During the Ebola outbreak in 2014/2015 more than 4,800 people died of Ebola in Liberia. Health care providers in the field have assumed that closure of health facilities and mistrust in existing structures resulted not only substantial additional deaths from Ebola but also impacted on death rate of other diseases and on the way people tried to seek health care. We conducted a mobile phone survey in Monrovia to identify deaths and diseases a household had faced since the beginning of the Ebola outbreak and the kind of health care they sought. We estimated that the non-Ebola-related death rate in Monrovia was not higher than previous national estimates for Liberia. However, additional deaths occurred in the population of Monrovia directly resulting from Ebola. Of the household members that were sick of any disease during the survey period, 43% did not visit any health care facility. The high proportion among the sick household members that sought health care in pharmacies or drug stores or by health care workers among their peers but outside health facilities emphasizes the importance of ensuring access to non-Ebola-related health care as outbreak control is challenged by sick community members staying undiagnosed and untreated.

Exposure-Specific and Age-Specific Attack Rates for Ebola Virus Disease in Ebola-Affected Households, Sierra Leone

Using histories of household members of Ebola virus disease (EVD) survivors in Sierra Leone, we calculated risk of EVD by age and exposure level, adjusting for confounding and clustering, and estimated relative risks. Of 937 household members in 94 households, 448 (48%) had had EVD. Highly correlated with exposure, EVD risk ranged from 83% for touching a corpse to 8% for minimal contact and varied by age group: 43% for children <2 years of age; 30% for those 5-14 years of age; and >60% for adults >30 years of age. Compared with risk for persons 20-29 years of age, exposure-adjusted relative risks were lower for those 5-9 (0.70), 10-14 (0.64), and 15-19 (0.71) years of age but not for children <2 (0.92) or 2-4 (0.97) years of age. Lower risk for 5-19-year-olds, after adjustment for exposure, suggests decreased susceptibility in this group.

[Epidemiological aspects of Ebola virus disease in Guinea (december 2013-april 2016)]

Ebola Zaire species variant Makona between its emergence in December 2013 and April 2016, resulted in an epidemic of Guinea importance and unprecedented gravity with 3814 reported cases of which 3358 were confirmed (88.0%) and 2544 were died (66.7%). The epidemic has evolved in phases: a silent phase without identification of all fatal cases until February 2014; a first outbreak from March 2014, when the alarm is raised and the virus detected, which lasted until July 2014; a second increase, which was the most intense, from August 2014 to January 2015 focused primarily on the forest Guinea; and a final increase from February 2015 centered on lower Guinea and the capital Conakry. Adapting strategies in 2015 (initiative "Zero Ebola in 60 days" active case search and suspicious deaths and awareness of active prefectures, microbanding the last affected communities and raking around these localities) and ring vaccination of contacts around confirmed cases has allowed to gradually control the main outbreak in October 2015. But a survivor was originally resurgence in forest areas between March and April 2016 with 10 cases including 8 deaths. The epidemic has particularly affected the forest Guinea region (44% and 48% of Guinean cases and deaths), elderly women (≥ 50 years), and health professionals (211 cases including 115 deaths); however, almost one-third of the patients (32.6%) was not provided supportive care in the Ebola centers. The epidemic is currently marked by the resurgence of small foci, from excreting subjects cured of the virus who have been controlled so far successfully. The survivors are the subject of special attention. It is necessary to learn lessons from the response to better prepare for the future, to improve knowledge about the natural history of the Ebola virus disease, and to rethink communication in this regard with the public and its leaders.