The Epi Info Viral Hemorrhagic Fever (VHF) Application: A Resource for Outbreak Data Management and Contact Tracing in the 2014-2016 West Africa Ebola Epidemic

Data extraction and comparison for complex systematic reviews: a step-by-step guideline and an implementation example using open-source software

BACKGROUND

Data extraction (DE) is a challenging step in systematic reviews (SRs). Complex SRs can involve multiple interventions and/or outcomes and encompass multiple research questions. Attempts have been made to clarify DE aspects focusing on the subsequent meta-analysis; there are, however, no guidelines for DE in complex SRs. Comparing datasets extracted independently by pairs of reviewers to detect discrepancies is also cumbersome, especially when the number of extracted variables and/or studies is colossal. This work aims to provide a set of practical steps to help SR teams design and build DE tools and compare extracted data for complex SRs.

METHODS

We provided a 10-step guideline, from determining data items and structure to data comparison, to help identify discrepancies and solve data disagreements between reviewers. The steps were organised into three phases: planning and building the database and data manipulation. Each step was described and illustrated with examples, and relevant references were provided for further guidance. A demonstration example was presented to illustrate the application of Epi Info and R in the database building and data manipulation phases. The proposed guideline was also summarised and compared with previous DE guidelines.

RESULTS

The steps of this guideline are described generally without focusing on a particular software application or meta-analysis technique. We emphasised determining the organisational data structure and highlighted its role in the subsequent steps of database building. In addition to the minimal programming skills needed, creating relational databases and data validation features of Epi info can be utilised to build DE tools for complex SRs. However, two R libraries are needed to facilitate data comparison and solve discrepancies.

CONCLUSIONS

We hope adopting this guideline can help review teams construct DE tools that suit their complex review projects. Although Epi Info depends on proprietary software for data storage, it can still be a potential alternative to other commercial DE software for completing complex reviews.

Go.Data as a digital tool for case investigation and contact tracing in the context of COVID-19: a mixed-methods study

BACKGROUND

A manual approach to case investigation and contact tracing can introduce delays in response and challenges for field teams. Go.Data, an outbreak response tool developed by the World Health Organization (WHO) in collaboration with the Global Outbreak Alert and Response Network, streamlines data collection and analysis during outbreaks. This study aimed to characterize Go.Data use during COVID-19, elicit shared benefits and challenges, and highlight key opportunities for enhancement.

METHODS

This study utilized mixed methods through qualitative interviews and a quantitative survey with Go.Data implementors on their experiences during COVID-19. Survey data was analyzed for basic univariate statistics. Interview data were coded using deductive and inductive reasoning and thematic analysis of categories. Overarching themes were triangulated with survey data to clarify key findings.

RESULTS

From April to June 2022, the research team conducted 33 interviews and collected 41 survey responses. Participants were distributed across all six WHO regions and 28 countries. While most implementations represented government actors at national or subnational levels, additional inputs were collected from United Nations agencies and universities. Results highlighted WHO endorsement, accessibility, adaptability, and flexible support modalities as main enabling factors. Formalization and standardization of data systems and people processes to prepare for future outbreaks were a welcomed byproduct of implementation, as 76% used paper-based reporting prior and benefited from increased coordination around a shared platform. Several challenges surfaced, including shortage of the appropriate personnel and skill-mix within teams to ensure smooth implementation. Among opportunities for enhancements were improved product documentation and features to improve usability with large data volumes.

CONCLUSIONS

This study was the first to provide a comprehensive picture of Go.Data implementations during COVID-19 and what joint lessons could be learned. It ultimately demonstrated that Go.Data was a useful complement to responses across diverse contexts, and helped set a reproducible foundation for future outbreaks. Concerted preparedness efforts across the domains of workforce composition, data architecture and political sensitization should be prioritized as key ingredients for future Go.Data implementations. While major developments in Go.Data functionality have addressed some key gaps highlighted during the pandemic, continued dialogue between WHO and implementors, including cross-country experience sharing, is needed ensure the tool is reactive to evolving user needs.

Investigation of and Strategies to Control the Final Cluster of the 2018-2020 Ebola Virus Disease Outbreak in the Eastern Democratic Republic of Congo

Background

On April 10, 2020, while the independent committee of the International Health Regulation was meeting to decide whether the 10th Ebola outbreak in the Demogratic Republic of Congo still constituted a Public Health Emergency of International Concern, a new confirmed case was reported in the city of Beni, the last epicenter of the epidemic. This study aimed to understand the source of this cluster and learn from the implemented control strategies for improved response in the future.

Methods

We conducted a combined epidemiological and genomic investigation to understand the origins and dynamics of transmission within this cluster and describe the strategy that successfully controlled the outbreak.

Results

Eight cases were identified as belonging to this final cluster. A total of 1028 contacts were identified. Whole-genome sequencing revealed that all cases belonged to the same cluster, the closest sequence to which was identified as a case from the Beni area with symptom onset in July 2019 and a difference of just 31 nucleotides. Outbreak control measures included community confinement of high-risk contacts.

Conclusions

This study illustrates the high risk of additional flare-ups in the period leading to the end-of-outbreak declaration and the importance of maintaining enhanced surveillance and confinement activities to rapidly control Ebola outbreaks.

Implementation of DHIS2 for Disease Surveillance in Guinea: 2015–2020

A robust epidemic-prone disease surveillance system is a critical component of public health infrastructure and supports compliance with the International Health Regulations (IHR). One digital health platform that has been implemented in numerous low- and middle-income countries is the District Health Information System Version 2 (DHIS2). In 2015, in the wake of the Ebola epidemic, the Ministry of Health in Guinea established a strategic plan to strengthen its surveillance system, including adoption of DHIS2 as a health information system that could also capture surveillance data. In 2017, the DHIS2 platform for disease surveillance was piloted in two regions, with the aim of ensuring the timely availability of quality surveillance data for better prevention, detection, and response to epidemic-prone diseases. The success of the pilot prompted the national roll-out of DHIS2 for weekly aggregate disease surveillance starting in January 2018. In 2019, the country started to also use the DHIS2 Tracker to capture individual cases of epidemic-prone diseases. As of February 2020, for aggregate data, the national average timeliness of reporting was 72.2%, and average completeness 98.5%; however, the proportion of individual case reports filed was overall low and varied widely between diseases. While substantial progress has been made in implementation of DHIS2 in Guinea for use in surveillance of epidemic-prone diseases, much remains to be done to ensure long-term sustainability of the system. This paper describes the implementation and outcomes of DHIS2 as a digital health platform for disease surveillance in Guinea between 2015 and early 2020, highlighting lessons learned and recommendations related to the processes of planning and adoption, pilot testing in two regions, and scale up to national level.

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.

Building the Sierra Leone Ebola Database: organization and characteristics of data systematically collected during 2014-2015 Ebola epidemic

PURPOSE

During the 2014-2016 Ebola outbreak in West Africa, the Sierra Leone Ministry of Health and Sanitation (MoHS), the US Centers for Disease Control and Prevention, and responding partners under the coordination of the National Ebola Response Center (NERC) and the MoHS's Emergency Operation Center (EOC) systematically recorded information from the 117 Call Center system and district alert phone lines, case investigations, laboratory sample testing, clinical management, and safe and dignified burial records. Since 2017, CDC assisted MoHS in building and managing the Sierra Leone Ebola Database (SLED) to consolidate these major data sources. The primary objectives of the project were helping families to identify the location of graves of their loved ones who died at the time of the Ebola epidemic through the SLED Family Reunification Program and creating a data source for epidemiological research. The objective of this paper is to describe the process of consolidating epidemic records into a useful and accessible data collection and to summarize data characteristics, strength, and limitations of this unique information source for public health research.

METHODS

Because of the unprecedented conditions during the epidemic, most of the records collected from responding organizations required extensive processing before they could be used as a data source for research or the humanitarian purpose of locating burial sites. This process required understanding how the data were collected and used during the outbreak. To manage the complexity of processing the data obtained from various sources, the Sierra Leone Ebola Database (SLED) Team used an organizational strategy that allowed tracking of the data provenance and lifecycle.

RESULTS

The SLED project brought raw data into one consolidated data collection. It provides researchers with secure and ethical access to the SLED data and serves as a basis for the research capacity building in Sierra Leone. The SLED Family Reunification Program allowed Sierra Leonean families to identify location of the graves of loved ones who died during the Ebola epidemic.

CONCLUSIONS

The SLED project consolidated and utilized epidemic data recorded during the Sierra Leone Ebola Virus Disease outbreak that were collected and contributed to SLED by national and international organizations. This project has provided a foundation for developing a method of ethical and secure SLED data access while preserving the host nation's data ownership. SLED serves as a data source for the SLED Family Reunification Program and for epidemiological research. It presents an opportunity for building research capacity in Sierra Leone and provides a foundation for developing a relational database. Large outbreak data systems such as SLED provide a unique opportunity for researchers to improve responses to epidemics and indicate the need to include data management preparedness in the plans for emergency response.

A lateral flow biosensor based on gold nanoparticles detects four hemorrhagic fever viruses

The pathogen of viral hemorrhagic fever (VHF), which is harmful to human health, is a hemorrhagic fever virus. Clinicians have long needed convenient and sensitive point-of-care rapid diagnostic tests (RDTs) for hemorrhagic fever viruses. Commonly used methods for pathogen detection rely on conventional culture-based tests, antibody-based assays and polymerase chain reaction (PCR)-based techniques. However, these methods are costly, laborious and time-consuming. Herein, we present a simple and sensitive biosensor for the rapid detection of hemorrhagic fever viruses. For this assay, we develop lateral flow biosensors (LFBs) based on magnetic beads and nicking enzyme-assisted isothermal strand-displacement amplification (SDA) for the detection of hemorrhagic fever viruses. The detection limit of this assay is 10 fM.

GoCoronaGo: Privacy Respecting Contact Tracing for COVID-19 Management

The COVID-19 pandemic is imposing enormous global challenges in managing the spread of the virus. A key pillar to mitigation is contact tracing, which complements testing and isolation. Digital apps for contact tracing using Bluetooth technology available in smartphones have gained prevalence globally. In this article, we discuss various capabilities of such digital contact tracing, and its implication on community safety and individual privacy, among others. We further describe the GoCoronaGo institutional contact tracing app that we have developed, and the conscious and sometimes contrarian design choices we have made. We offer a detailed overview of the app, backend platform and analytics, and our early experiences with deploying the app to over 1000 users within the Indian Institute of Science campus in Bangalore. We also highlight research opportunities and open challenges for digital contact tracing and analytics over temporal networks constructed from them.

Automated and partly automated contact tracing: a systematic review to inform the control of COVID-19

Evidence for the use of automated or partly automated contact-tracing tools to contain severe acute respiratory syndrome coronavirus 2 is scarce. We did a systematic review of automated or partly automated contact tracing. We searched PubMed, EMBASE, OVID Global Health, EBSCO Medical COVID Information Portal, Cochrane Library, medRxiv, bioRxiv, arXiv, and Google Advanced for articles relevant to COVID-19, severe acute respiratory syndrome, Middle East respiratory syndrome, influenza, or Ebola virus, published from Jan 1, 2000, to April 14, 2020. We also included studies identified through professional networks up to April 30, 2020. We reviewed all full-text manuscripts. Primary outcomes were the number or proportion of contacts (or subsequent cases) identified. Secondary outcomes were indicators of outbreak control, uptake, resource use, cost-effectiveness, and lessons learnt. This study is registered with PROSPERO (CRD42020179822). Of the 4036 studies identified, 110 full-text studies were reviewed and 15 studies were included in the final analysis and quality assessment. No empirical evidence of the effectiveness of automated contact tracing (regarding contacts identified or transmission reduction) was identified. Four of seven included modelling studies that suggested that controlling COVID-19 requires a high population uptake of automated contact-tracing apps (estimates from 56% to 95%), typically alongside other control measures. Studies of partly automated contact tracing generally reported more complete contact identification and follow-up compared with manual systems. Automated contact tracing could potentially reduce transmission with sufficient population uptake. However, concerns regarding privacy and equity should be considered. Well designed prospective studies are needed given gaps in evidence of effectiveness, and to investigate the integration and relative effects of manual and automated systems. Large-scale manual contact tracing is therefore still key in most contexts.

Lessons Learned from Reinforcing Epidemiologic Surveillance During the 2017 Ebola Outbreak in the Likati District, Democratic Republic of the Congo

On May 12, 2017, the Democratic Republic of Congo (DRC) publicly declared an outbreak of Ebola virus disease (EVD) in the Likati District of the Bas-Uélé Province, 46 days after the index case became symptomatic. The delayed EVD case detection and reporting highlights the importance of establishing real-time surveillance, consistent with the Global Health Security Agenda. We describe lessons learned from implementing improved EVD case detection and reporting strategies at the outbreak epicenter and make recommendations for future response efforts. The strategies included daily coordination meetings to enhance effective and efficient outbreak response activities, assessment and adaptation of case definitions and reporting tools, establishment of a community alert system using context-appropriate technology, training facility and community health workers on adapted case definitions and reporting procedures, development of context-specific plans for outbreak data management, and strengthened operational support for communications and information-sharing networks. Post-outbreak, surveillance officials should preemptively plan for the next outbreak by developing emergency response plans, evaluating the case definitions and reporting tools used, retraining on revised case definitions, and developing responsive strategies for overcoming telecommunications and technology challenges. The ongoing EVD outbreak in the North Kivu and Ituri provinces of DRC, currently the second largest EVD outbreak in history, demonstrates that documentation of successful context-specific strategies and tools are needed to combat the next outbreak. The lessons learned from the rapid containment of the EVD outbreak in Likati can be applied to the DRC and other rural low-resource settings to ensure readiness for future zoonotic disease outbreaks.

Mobile-based and open-source case detection and infectious disease outbreak management systems: a review

In this paper we perform a rapid review of existing mobile-based, open-source systems for infectious disease outbreak data collection and management. Our inclusion criteria were designed to match the PANDORA-ID-NET consortium’s goals for capacity building in sub-Saharan Africa, and to reflect the lessons learned from the 2014–16 West African Ebola outbreak. We found eight candidate systems that satisfy some or most of these criteria, but only one (SORMAS) fulfils all of them. In addition, we outline a number of desirable features that are not currently present in most outbreak management systems.

Precision Global Health - The case of Ebola: a scoping review

BACKGROUND

The 2014-2016 Ebola outbreak across West Africa was devastating, acting not only as a wake-up call for the global health community, but also as a catalyst for innovative change and global action. Improved infectious disease monitoring is the stepping-stone toward better disease prevention and control efforts, and recent research has revealed the potential of digital technologies to transform the field of global health. This scoping review aimed to identify which digital technologies may improve disease prevention and control, with regard to the 2014-2016 Ebola outbreak in West Africa.

METHODS

A search was conducted on PubMed, EBSCOhost and Web of Science, with search dates ranging from 2013 (01/01/2013) - 2017 (13/06/2017). Data was extracted into a summative table and data synthesized through grouping digital technology domains, using narrative and graphical methods.

FINDINGS

The scoping review identified 82 full-text articles, and revealed big data (48%, n = 39) and modeling (26%, n = 21) technologies to be the most utilized within the Ebola outbreak. Digital technologies were mainly used for surveillance purposes (90%, n = 74), and key challenges were related to scalability and misinformation from social media platforms.

INTERPRETATION

Digital technologies demonstrated their potential during the Ebola outbreak through: more rapid diagnostics, more precise predictions and estimations, increased knowledge transfer, and raising situational awareness through mHealth and social media platforms such as Twitter and Weibo. However, better integration into both citizen and health professionals' communities is necessary to maximise the potential of digital technologies.

Improving health information systems during an emergency: lessons and recommendations from an Ebola treatment centre in Sierra Leone

Background

The 2014–2016 West Africa Ebola epidemic highlighted the difficulty of collecting patient information during emergencies, especially in highly infectious environments. Health information systems (HISs) appropriate for such settings were lacking prior to this outbreak. Here we describe our development and implementation of paper and electronic HISs at the Sierra Leone Kerry Town Ebola treatment centre (ETC) from 2014 to 2015. We share our approach, experiences, and recommendations for future health emergencies.

Methods

We developed eight fact-finding questions about data-related needs, priorities, and restrictions at the ETC (“inputs”) to inform eight structural decisions (“outputs”) across six core HIS components. Semi-structured interviews about the “inputs” were then conducted with HIS stakeholders, chosen based on their teams’ involvement in ETC HIS-related activities. Their responses were used to formulate the “output” results to guide the HIS design. We implemented the HIS using an Agile approach, monitored system usage, and developed a structured questionnaire on user experiences and opinions.

Results

Some key “input” responses were: 1) data needs for priorities (patient care, mandatory reporting); 2) challenges around infection control, limited equipment, and staff clinical/language proficiencies; 3) patient/clinical flows; and 4) weak points from staff turnover, infection control, and changing protocols. Key outputs included: 1) determining essential data, 2) data tool design decisions (e.g. large font sizes, checkboxes/buttons), 3) data communication methods (e.g. radio, “collective memory”), 4) error reduction methods (e.g. check digits, pre-written wristbands), and 5) data storage options (e.g. encrypted files, accessible folders). Implementation involved building data collection tools (e.g. 13 forms), preparing the systems (e.g. supplies), training staff, and maintenance (e.g. removing old forms). Most patients had basic (100%, n = 456/456), drug (96.9%, n = 442/456), and additional clinical/epidemiological (98.9%, n = 451/456) data stored. The questionnaire responses highlighted the importance of usability and simplicity in the HIS.

Conclusions

HISs during emergencies are often ad-hoc and disjointed, but systematic design and implementation can lead to high-quality systems focused on efficiency and ease of use. Many of the processes used and lessons learned from our work are generalizable to other health emergencies. Improvements should be started now to have rapidly adaptable and deployable HISs ready for the next health emergency.

Electronic supplementary material

The online version of this article (10.1186/s12911-019-0817-9) contains supplementary material, which is available to authorized users.

Marburg virus disease outbreak in Kween District Uganda, 2017: Epidemiological and laboratory findings

INTRODUCTION

In October 2017, a blood sample from a resident of Kween District, Eastern Uganda, tested positive for Marburg virus. Within 24 hour of confirmation, a rapid outbreak response was initiated. Here, we present results of epidemiological and laboratory investigations.

METHODS

A district task force was activated consisting of specialised teams to conduct case finding, case management and isolation, contact listing and follow up, sample collection and testing, and community engagement. An ecological investigation was also carried out to identify the potential source of infection. Virus isolation and Next Generation sequencing were performed to identify the strain of Marburg virus.

RESULTS

Seventy individuals (34 MVD suspected cases and 36 close contacts of confirmed cases) were epidemiologically investigated, with blood samples tested for MVD. Only four cases met the MVD case definition; one was categorized as a probable case while the other three were confirmed cases. A total of 299 contacts were identified; during follow- up, two were confirmed as MVD. Of the four confirmed and probable MVD cases, three died, yielding a case fatality rate of 75%. All four cases belonged to a single family and 50% (2/4) of the MVD cases were female. All confirmed cases had clinical symptoms of fever, vomiting, abdominal pain and bleeding from body orifices. Viral sequences indicated that the Marburg virus strain responsible for this outbreak was closely related to virus strains previously shown to be circulating in Uganda.

CONCLUSION

This outbreak of MVD occurred as a family cluster with no additional transmission outside of the four related cases. Rapid case detection, prompt laboratory testing at the Uganda National VHF Reference Laboratory and presence of pre-trained, well-prepared national and district rapid response teams facilitated the containment and control of this outbreak within one month, preventing nationwide and global transmission of the disease.

Sensitivity and Specificity of Suspected Case Definition Used during West Africa Ebola Epidemic

Rapid early detection and control of Ebola virus disease (EVD) is contingent on accurate case definitions. Using an epidemic surveillance dataset from Guinea, we analyzed an EVD case definition developed by the World Health Organization (WHO) and used in Guinea. We used the surveillance dataset (March-October 2014; n = 2,847 persons) to identify patients who satisfied or did not satisfy case definition criteria. Laboratory confirmation determined cases from noncases, and we calculated sensitivity, specificity and predictive values. The sensitivity of the defintion was 68.9%, and the specificity of the definition was 49.6%. The presence of epidemiologic risk factors (i.e., recent contact with a known or suspected EVD case-patient) had the highest sensitivity (74.7%), and unexplained deaths had the highest specificity (92.8%). Results for case definition analyses were statistically significant (p<0.05 by χ² test). Multiple components of the EVD case definition used in Guinea contributed to improved overall sensitivity and specificity.

Isolated Case of Marburg Virus Disease, Kampala, Uganda, 2014

In September 2014, a single fatal case of Marburg virus was identified in a healthcare worker in Kampala, Uganda. The source of infection was not identified, and no secondary cases were identified. We describe the rapid identification, laboratory diagnosis, and case investigation of the third Marburg virus outbreak in Uganda.

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'.

Novel surveillance methods for the control of Ebola virus disease

The unprecedented scale of the 2013-2016 West African Ebola virus disease (EVD) outbreak was in a large part due to failings in surveillance: contacts of confirmed cases were not systematically identified, monitored and diagnosed early, and new cases appearing in previously unaffected communities were similarly not rapidly identified, diagnosed and isolated. Over the course of this epidemic, traditional surveillance methods were strengthened and novel methods introduced. The wealth of experience gained, and the systems introduced in West Africa, should be used in future EVD outbreaks, as well as for other communicable diseases in the region and beyond.

Analysis of patient data from laboratories during the Ebola virus disease outbreak in Liberia, April 2014 to March 2015

An outbreak of Ebola virus disease (EVD) in Liberia began in March 2014 and ended in January 2016. Epidemiological information on the EVD cases was collected and managed nationally; however, collection and management of the data were challenging at the time because surveillance and reporting systems malfunctioned during the outbreak. EVD diagnostic laboratories, however, were able to register basic demographic and clinical information of patients more systematically. Here we present data on 16,370 laboratory samples that were tested between April 4, 2014 and March 29, 2015. A total of 10,536 traceable individuals were identified, of whom 3,897 were confirmed cases (positive for Ebola virus RNA). There were significant differences in sex, age, and place of residence between confirmed and suspected cases that tested negative for Ebola virus RNA. Age (young children and the elderly) and place of residence (rural areas) were the risk factors for death due to the disease. The case fatality rate of confirmed cases decreased from 80% to 63% during the study period. These findings may help support future investigations and lead to a fuller understanding of the outbreak in Liberia.

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'.