Using EpiCore to Enable Rapid Verification of Potential Health Threats: Illustrated Use Cases and Summary Statistics.

BACKGROUND: The proliferation of digital disease-detection systems has led to an increase in earlier warning signals, which subsequently have resulted in swifter responses to emerging threats. Such highly sensitive systems can also produce weak signals needing additional information for action. The delays in the response to a genuine health threat are often due to the time it takes to verify a health event. It was the delay in outbreak verification that was the main impetus for creating EpiCore. OBJECTIVE: This paper describes the potential of crowdsourcing information through EpiCore, a network of voluntary human, animal, and environmental health professionals supporting the verification of early warning signals of potential outbreaks and informing risk assessments by monitoring ongoing threats. METHODS: This paper uses summary statistics to assess whether EpiCore is meeting its goal to accelerate the time to verification of identified potential health events for epidemic and pandemic intelligence purposes from around the world. Data from the EpiCore platform from January 2018 to December 2022 were analyzed to capture request for information response rates and verification rates. Illustrated use cases are provided to describe how EpiCore members provide information to facilitate the verification of early warning signals of potential outbreaks and for the monitoring and risk assessment of ongoing threats through EpiCore and its utilities. RESULTS: Since its launch in 2016, EpiCore network membership grew to over 3300 individuals during the first 2 years, consisting of professionals in human, animal, and environmental health, spanning 161 countries. The overall EpiCore response rate to requests for information increased by year between 2018 and 2022 from 65.4% to 68.8% with an initial response typically received within 24 hours (in 2022, 94% of responded requests received a first contribution within 24 h). Five illustrated use cases highlight the various uses of EpiCore. CONCLUSIONS: As the global demand for data to facilitate disease prevention and control continues to grow, it will be crucial for traditional and nontraditional methods of disease surveillance to work together to ensure health threats are captured earlier. EpiCore is an innovative approach that can support health authorities in decision-making when used complementarily with official early detection and verification systems. EpiCore can shorten the time to verification by confirming early detection signals, informing risk-assessment activities, and monitoring ongoing events.

Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study.

BACKGROUND: Seasonal respiratory viruses had lower incidence during their 2019-2020 and 2020-2021 seasons, which overlapped with the COVID-19 pandemic. The widespread implementation of precautionary measures to prevent transmission of SARS-CoV-2 has been seen to also mitigate transmission of seasonal influenza. The COVID-19 pandemic also led to changes in care seeking and access. Participatory surveillance systems have historically captured mild illnesses that are often missed by surveillance systems that rely on encounters with a health care provider for detection. OBJECTIVE: This study aimed to assess if a crowdsourced syndromic surveillance system capable of detecting mild influenza-like illness (ILI) also captured the globally observed decrease in ILI in the 2019-2020 and 2020-2021 influenza seasons, concurrent with the COVID-19 pandemic. METHODS: Flu Near You (FNY) is a web-based participatory syndromic surveillance system that allows participants in the United States to report their health information using a brief weekly survey. Reminder emails are sent to registered FNY participants to report on their symptoms and the symptoms of household members. Guest participants may also report. ILI was defined as fever and sore throat or fever and cough. ILI rates were determined as the number of ILI reports over the total number of reports and assessed for the 2016-2017, 2017-2018, 2018-2019, 2019-2020, and 2020-2021 influenza seasons. Baseline season (2016-2017, 2017-2018, and 2018-2019) rates were compared to the 2019-2020 and 2020-2021 influenza seasons. Self-reported influenza diagnosis and vaccination status were captured and assessed as the total number of reported events over the total number of reports submitted. CIs for all proportions were calculated via a 1-sample test of proportions. RESULTS: ILI was detected in 3.8% (32,239/848,878) of participants in the baseline seasons (2016-2019), 2.58% (7418/287,909) in the 2019-2020 season, and 0.27% (546/201,079) in the 2020-2021 season. Both influenza seasons that overlapped with the COVID-19 pandemic had lower ILI rates than the baseline seasons. ILI decline was observed during the months with widespread implementation of COVID-19 precautions, starting in February 2020. Self-reported influenza diagnoses decreased from early 2020 through the influenza season. Self-reported influenza positivity among ILI cases varied over the observed time period. Self-reported influenza vaccination rates in FNY were high across all observed seasons. CONCLUSIONS: A decrease in ILI was detected in the crowdsourced FNY surveillance system during the 2019-2020 and 2020-2021 influenza seasons, mirroring trends observed in other influenza surveillance systems. Specifically, the months within seasons that overlapped with widespread pandemic precautions showed decreases in ILI and confirmed influenza. Concerns persist regarding respiratory pathogens re-emerging with changes to COVID-19 guidelines. Traditional surveillance is subject to changes in health care behaviors. Systems like FNY are uniquely situated to detect disease across disease severity and care seeking, providing key insights during public health emergencies.

Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View.

Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.

Looking Ahead in the Rearview Mirror: During Action Review and Tabletop (DART) to Strengthen Health Emergency Readiness and Resiliency.

During health emergencies, such as the COVID-19 pandemic, systematic evaluation of capabilities, and multisector coordination are challenging while operating in triage mode. During Action Review and Tabletop (DART) identifies recommendations for strengthening readiness and resiliency by creating a single methodology integrating retrospective analysis of the response to date with a prospective analysis of future scenarios. DART utilizes a role-based questionnaire and participant-led discussion for retrospective response review and identification of future scenarios of concern. Tabletop exercises exploring those future scenarios are conducted in a multi-role format to assess readiness and resiliency. Participants evaluate findings to determine recommended actions to improve response capabilities. 3 COVID-19 focused DARTs demonstrated the ability of this participant-led approach to systematically assess, not only readiness for today, but also resiliency to future complications. While demonstrating its usefulness during COVID-19, DART's flexible and modular design promises to be an effective for any ongoing health emergency.

Multicenter Surveillance of Antimicrobial Resistance among Gram-Negative Bacteria Isolated from Bloodstream Infections in Ghana.

BACKGROUND: Antimicrobial resistance (AMR) in Gram-negative bacteria-causing bloodstream infections (BSIs), such as Klebsiella pneumoniae and non-typhoidal Salmonella (NTS), is a major public health concern. Nonetheless, AMR surveillance remains scarce in sub-Saharan Africa, where BSI treatment is largely empirical. The aim of the study was to determine the distribution and AMR patterns of BSI-causing NTS, K. pneumoniae, and other Gram-negative bacteria in Ghana. METHODS: A cross-sectional study was conducted between April and December 2021 at eleven sentinel health facilities across Ghana as part of a pilot study on the feasibility and implementation of the human sector AMR surveillance harmonized protocol in sub-Saharan Africa. Gram-negative bacteria recovered from blood specimens of febrile patients were identified using MALDI-TOF and evaluated for antimicrobial resistance using the BD Phoenix M50 analyzer and Kirby-Bauer disc diffusion. The Department of Medical Microbiology at the University of Ghana served as the reference laboratory. RESULTS: Out of 334 Gram-negative blood isolates, there were 18 (5.4%) NTS, 85 (25.5%) K. pneumoniae, 88 (26.4%) Escherichia coli, 40 (12.0%) Acinetobacter baumannii, 25 (7.5%) Pseudomonas aeruginosa, and 77 (23.1%) other Gram-negative bacteria. As a composite, the isolates displayed high resistance to the antibiotics tested-amoxicillin (89.3%), tetracycline (76.1%), trimethoprim-sulfamethoxazole (71.5%), and chloramphenicol (59.7%). Resistance to third-generation cephalosporins [ceftriaxone (73.7%), cefotaxime (77.8%), and ceftazidime (56.3%)] and fluoroquinolones [ciprofloxacin (55.3%)] was also high; 88% of the isolates were multidrug resistant, and the rate of extended-spectrum beta-lactamase (ESBL) production was 44.6%. Antibiotic resistance in K. pneumoniae followed the pattern of all Gram-negative isolates. Antibiotic resistance was lower in NTS blood isolates, ranging between 16.7-38.9% resistance to the tested antibiotics. Resistance rates of 38.9%, 22.2%, and 27.8% were found for cefotaxime, ceftriaxone, and ceftazidime, respectively, and 27.8% and 23.8% for ciprofloxacin and azithromycin, respectively, which are used in the treatment of invasive NTS. The prevalence of multidrug resistance in NTS isolates was 38.9%. CONCLUSIONS: Multicenter AMR surveillance of Gram-negative blood isolates from febrile patients was well-received in Ghana, and the implementation of a harmonized protocol was feasible. High resistance and multidrug resistance to first- or second-choice antibiotics, including penicillins, third-generation cephalosporins, and fluoroquinolones, were found, implying that these antibiotics might have limited effectiveness in BSI treatment in the country. Continuation of AMR surveillance in Gram-negative blood isolates is essential for a better understanding of the extent of AMR in these pathogens and to guide clinical practice and policymaking.

The Landscape of Participatory Surveillance Systems Across the One Health Spectrum: Systematic Review.

BACKGROUND: Participatory surveillance systems augment traditional surveillance systems through bidirectional community engagement. The digital platform evolution has enabled the expansion of participatory surveillance systems, globally, for the detection of health events impacting people, animals, plants, and the environment, in other words, across the entire One Health spectrum. OBJECTIVE: The aim of this landscape was to identify and provide descriptive information regarding system focus, geography, users, technology, information shared, and perceived impact of ongoing participatory surveillance systems across the One Health spectrum. METHODS: This landscape began with a systematic literature review to identify potential ongoing participatory surveillance systems. A survey was sent to collect standardized data from the contacts of systems identified in the literature review and through direct outreach to stakeholders, experts, and professional organizations. Descriptive analyses of survey and literature review results were conducted across the programs. RESULTS: The landscape identified 60 ongoing single-sector and multisector participatory surveillance systems spanning five continents. Of these, 29 (48%) include data on human health, 26 (43%) include data on environmental health, and 24 (40%) include data on animal health. In total, 16 (27%) systems are multisectoral; of these, 9 (56%) collect animal and environmental health data; 3 (19%) collect human, animal, and environmental health data; 2 (13%) collect human and environmental health data; and 2 (13%) collect human and animal health data. Out of 60 systems, 31 (52%) are designed to cover a national scale, compared to those with a subnational (n=19, 32%) or multinational (n=10, 17%) focus. All systems use some form of digital technology. Email communication or websites (n=40, 67%) and smartphones (n=29, 48%) are the most common technologies used, with some using both. Systems have capabilities to download geolocation data (n=31, 52%), photographs (n=29, 48%), and videos (n=6, 10%), and can incorporate lab data or sample collection (n=15, 25%). In sharing information back with users, most use visualization, such as maps (n=43, 72%); training and educational materials (n=37, 62%); newsletters, blogs, and emails (n=34, 57%); and disease prevention information (n=32, 53%). Out of the 46 systems responding to the survey regarding perceived impacts of their systems, 36 (78%) noted "improved community knowledge and understanding" and 31 (67%) noted "earlier detection." CONCLUSIONS: The landscape demonstrated the breadth of applicability of participatory surveillance around the world to collect data from community members and trained volunteers in order to inform the detection of events, from invasive plant pests to weekly influenza symptoms. Acknowledging the importance of bidirectionality of information, these systems simultaneously share findings back with the users. Such directly engaged community detection systems capture events early and provide opportunities to stop outbreaks quickly.

EpiHacks, a Process for Technologists and Health Experts to Cocreate Optimal Solutions for Disease Prevention and Control: User-Centered Design Approach.

BACKGROUND: Technology-based innovations that are created collaboratively by local technology specialists and health experts can optimize the addressing of priority needs for disease prevention and control. An EpiHack is a distinct, collaborative approach to developing solutions that combines the science of epidemiology with the format of a hackathon. Since 2013, a total of 12 EpiHacks have collectively brought together over 500 technology and health professionals from 29 countries. OBJECTIVE: We aimed to define the EpiHack process and summarize the impacts of the technology-based innovations that have been created through this approach. METHODS: The key components and timeline of an EpiHack were described in detail. The focus areas, outputs, and impacts of the twelve EpiHacks that were conducted between 2013 and 2021 were summarized. RESULTS: EpiHack solutions have served to improve surveillance for influenza, dengue, and mass gatherings, as well as laboratory sample tracking and One Health surveillance, in rural and urban communities. Several EpiHack tools were scaled during the COVID-19 pandemic to support local governments in conducting active surveillance. All tools were designed to be open source to allow for easy replication and adaptation by other governments or parties. CONCLUSIONS: EpiHacks provide an efficient, flexible, and replicable new approach to generating relevant and timely innovations that are locally developed and owned, are scalable, and are sustainable.

Using Timeliness Metrics to Track Progress and Identify Gaps in Disease Surveillance.

Timely outbreak detection and response can translate into illnesses averted and lives saved. As such, timeliness is an important criterion for evaluating performance of infectious disease surveillance systems. Through the use of clearly defined outbreak milestones, timeliness metrics can capture the speed of outbreak detection, verification, response, and other key actions across the timeline of an outbreak and evaluate progress over time. In this article, we describe a series of country-level pilot studies designed to assess the feasibility and utility of tracking timeliness metrics and highlight key findings. We then discuss subsequent efforts to develop a timeliness metrics measurement framework through expert consultation and provide recommendations for implementation. National surveillance programs, international agencies, and donor organizations can use timeliness metrics to identify gaps in surveillance performance and track progress toward improved global health security.

Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study.

BACKGROUND: With the evolution of digital media, areas such as public health are adding new platforms to complement traditional systems of epidemiological surveillance. Participatory surveillance and digital epidemiology have become innovative tools for the construction of epidemiological landscapes with citizens' participation, improving traditional sources of information. Strategies such as these promote the timely detection of warning signs for outbreaks and epidemics in the region. OBJECTIVE: This study aims to describe the participatory surveillance platform Guardians of Health, which was used in a project conducted during the 2016 Olympic and Paralympic Games in Rio de Janeiro, Brazil, and officially used by the Brazilian Ministry of Health for the monitoring of outbreaks and epidemics. METHODS: This is a descriptive study carried out using secondary data from Guardians of Health available in a public digital repository. Based on syndromic signals, the information subsidy for decision making by policy makers and health managers becomes more dynamic and assertive. This type of information source can be used as an early route to understand the epidemiological scenario. RESULTS: The main result of this research was demonstrating the use of the participatory surveillance platform as an additional source of information for the epidemiological surveillance performed in Brazil during a mass gathering. The platform Guardians of Health had 7848 users who generated 12,746 reports about their health status. Among these reports, the following were identified: 161 users with diarrheal syndrome, 68 users with respiratory syndrome, and 145 users with rash syndrome. CONCLUSIONS: It is hoped that epidemiological surveillance professionals, researchers, managers, and workers become aware of, and allow themselves to use, new tools that improve information management for decision making and knowledge production. This way, we may follow the path for a more intelligent, efficient, and pragmatic disease control system.

Evaluation of the EpiCore outbreak verification system.

OBJECTIVE: To describe a crowdsourced disease surveillance project (EpiCore) and evaluate its usefulness in obtaining information regarding potential disease outbreaks. METHODS: Volunteer human, animal and environmental health professionals from around the world were recruited to EpiCore and trained to provide early verification of health threat alerts in their geographical region via a secure, easy-to-use, online platform. Experts in the area of emerging infectious diseases sent requests for information on unverified health threats to these volunteers, who used local knowledge and expertise to respond to requests. Experts reviewed and summarized the responses and rapidly disseminated important information to the global health community through the existing event-based disease surveillance network, ProMED. FINDINGS: From March 2016 to September 2017, 2068 EpiCore volunteers from 142 countries were trained in methods of informal disease surveillance and use of the EpiCore online platform. These volunteers provided 790 individual responses to 759 requests for information addressing unverified health threats in 112 countries; 361 (45%) responses were considered to be useful. Most responses were received within hours of the requests. The responses led to 194 ProMED posts, of which 99 (51%) supported verification of an outbreak, were published on ProMED and sent to over 87 000 subscribers. CONCLUSION: There is widespread willingness among health professionals around the world to voluntarily assist efforts to verify and provide supporting information on unconfirmed health threats in their region. By linking this member network of health experts through a secure online reporting platform, EpiCore enables faster global outbreak detection and reporting.

Data quality and timeliness of outbreak reporting system among countries in Greater Mekong subregion: Challenges for international data sharing.

Cross-border disease transmission is a key challenge for prevention and control of outbreaks. Variation in surveillance structure and national guidelines used in different countries can affect their data quality and the timeliness of outbreak reports. This study aimed to evaluate timeliness and data quality of national outbreak reporting for four countries in the Mekong Basin Disease Surveillance network (MBDS). Data on disease outbreaks occurring from 2010 to 2015 were obtained from the national disease surveillance reports of Cambodia, Lao PDR, Myanmar, and Vietnam. Data included total cases, geographical information, and dates at different timeline milestones in the outbreak detection process. Nine diseases or syndromes with public health importance were selected for the analysis including: dengue, food poisoning & diarrhea, severe diarrhea, diphtheria, measles, H5N1 influenza, H1N1 influenza, rabies, and pertussis. Overall, 2,087 outbreaks were reported from the four countries. The number of outbreaks and number of cases per outbreak varied across countries and diseases, depending in part on the outbreak definition used in each country. Dates on index onset, report, and response were >95% complete in all countries, while laboratory confirmation dates were 10%-100% incomplete in most countries. Inconsistent and out of range date data were observed in 1%-5% of records. The overall timeliness of outbreak report, response, and public communication was within 1-15 days, depending on countries and diseases. Diarrhea and severe diarrhea outbreaks showed the most rapid time to report and response, whereas diseases such as rabies, pertussis and diphtheria required a longer time to report and respond. The hierarchical structure of the reporting system, data collection method, and country's resources could affect the data quality and timeliness of the national outbreak reporting system. Differences in data quality and timeliness of outbreak reporting system among member countries should be considered when planning data sharing strategies within a regional network.

Participatory Disease Surveillance: Engaging Communities Directly in Reporting, Monitoring, and Responding to Health Threats.

BACKGROUND: Since 2012, the International Workshop on Participatory Surveillance (IWOPS) has served as an informal network to share best practices, consult on analytic methods, and catalyze innovation to advance the burgeoning method of direct engagement of populations in voluntary monitoring of disease. OBJECTIVE: This landscape provides an overview of participatory disease surveillance systems in the IWOPS network and orients readers to this growing field of practice. METHODS: Authors reviewed participatory approaches that include human and animal health surveillance, both syndromic (self- reported symptoms) and event-based, and how these tools have been leveraged for disease modeling and forecasting. The authors also discuss benefits, challenges, and future directions for participatory disease surveillance. RESULTS: There are at least 23 distinct participatory surveillance tools or programs represented in the IWOPS network across 18 countries. Organizations supporting these tools are diverse in nature. CONCLUSIONS: Participatory disease surveillance is a promising method to complement both traditional, facility-based surveillance and newer digital epidemiology systems.

Saúde na Copa: The World's First Application of Participatory Surveillance for a Mass Gathering at FIFA World Cup 2014, Brazil.

BACKGROUND: The 2005 International Health Regulations (IHRs) established parameters for event assessments and notifications that may constitute public health emergencies of international concern. These requirements and parameters opened up space for the use of nonofficial mechanisms (such as websites, blogs, and social networks) and technological improvements of communication that can streamline the detection, monitoring, and response to health problems, and thus reduce damage caused by these problems. Specifically, the revised IHR created space for participatory surveillance to function, in addition to the traditional surveillance mechanisms of detection, monitoring, and response. Participatory surveillance is based on crowdsourcing methods that collect information from society and then return the collective knowledge gained from that information back to society. The spread of digital social networks and wiki-style knowledge platforms has created a very favorable environment for this model of production and social control of information. OBJECTIVE: The aim of this study was to describe the use of a participatory surveillance app, Healthy Cup, for the early detection of acute disease outbreaks during the Fédération Internationale de Football Association (FIFA) World Cup 2014. Our focus was on three specific syndromes (respiratory, diarrheal, and rash) related to six diseases that were considered important in a mass gathering context (influenza, measles, rubella, cholera, acute diarrhea, and dengue fever). METHODS: From May 12 to July 13, 2014, users from anywhere in the world were able to download the Healthy Cup app and record their health condition, reporting whether they were good, very good, ill, or very ill. For users that reported being ill or very ill, a screen with a list of 10 symptoms was displayed. Participatory surveillance allows for the real-time identification of aggregates of symptoms that indicate possible cases of infectious diseases. RESULTS: From May 12 through July 13, 2014, there were 9434 downloads of the Healthy Cup app and 7155 (75.84%) registered users. Among the registered users, 4706 (4706/7155, 65.77%) were active users who posted a total of 47,879 times during the study period. The maximum number of users that signed up in one day occurred on May 30, 2014, the day that the app was officially launched by the Minister of Health during a press conference. During this event, the Minister of Health announced the special government program Health in the World Cup on national television media. On that date, 3633 logins were recorded, which accounted for more than half of all sign-ups across the entire duration of the study (50.78%, 3633/7155). CONCLUSIONS: Participatory surveillance through community engagement is an innovative way to conduct epidemiological surveillance. Compared to traditional epidemiological surveillance, advantages include lower costs of data acquisition, timeliness of information collected and shared, platform scalability, and capacity for integration between the population being served and public health services.

Finding Outbreaks Faster.

Rapid detection, reporting, and response to an infectious disease outbreak are critical to prevent localized health events from emerging as pandemic threats. Metrics to evaluate the timeliness of these critical activities, however, are lacking. Easily understood and comparable measures for tracking progress and encouraging investment in rapid detection, reporting, and response are sorely needed. We propose that the timeliness of outbreak detection, reporting, laboratory confirmation, response, and public communication should be considered as measures for improving global health security at the national level, allowing countries to track progress over time and inform investments in disease surveillance.

Determinants of Participants' Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System.

BACKGROUND: Flu Near You (FNY) is an Internet-based participatory surveillance system in the United States and Canada that allows volunteers to report influenza-like symptoms using a brief weekly symptom report. OBJECTIVE: Our objective was to evaluate the representativeness of the FNY population compared with the general population of the United States, explore the demographic and behavioral characteristics associated with FNY's high-participation users, and summarize results from a user survey of a cohort of FNY participants. METHODS: We compared (1) the representativeness of sex and age groups of FNY participants during the 2014-2015 flu season versus the general US population and (2) the distribution of Human Development Index (HDI) scores of FNY participants versus that of the general US population. We analyzed associations between demographic and behavioral factors and the level of participant follow-up (ie, high vs low). Finally, descriptive statistics of responses from FNY's 2015 and 2016 end-of-season user surveys were calculated. RESULTS: During the 2014-2015 influenza season, 47,234 unique participants had at least one FNY symptom report that was either self-reported (users) or submitted on their behalf (household members). The proportion of female FNY participants was significantly higher than that of the general US population (n=28,906, 61.2% vs 51.1%, P<.001). Although each age group was represented in the FNY population, the age distribution was significantly different from that of the US population (P<.001). Compared with the US population, FNY had a greater proportion of individuals with HDI >5.0, signaling that the FNY user distribution was more affluent and educated than the US population baseline. We found that high-participation use (ie, higher participation in follow-up symptom reports) was associated with sex (females were 25% less likely than men to be high-participation users), higher HDI, not reporting an influenza-like illness at the first symptom report, older age, and reporting for household members (all differences between high- and low-participation users P<.001). Approximately 10% of FNY users completed an additional survey at the end of the flu season that assessed detailed user characteristics (3217/33,324 in 2015; 4850/44,313 in 2016). Of these users, most identified as being either retired or employed in the health, education, and social services sectors and indicated that they achieved a bachelor's degree or higher. CONCLUSIONS: The representativeness of the FNY population and characteristics of its high-participation users are consistent with what has been observed in other Internet-based influenza surveillance systems. With targeted recruitment of underrepresented populations, FNY may improve as a complementary system to timely tracking of flu activity, especially in populations that do not seek medical attention and in areas with poor official surveillance data.

Global Capacity for Emerging Infectious Disease Detection, 1996-2014.

The speed with which disease outbreaks are recognized is critical for establishing effective control efforts. We evaluate global improvements in the timeliness of outbreak discovery and communication during 2010-2014 as a follow-up to a 2010 report. For all outbreaks reported by the World Health Organization's Disease Outbreak News, we estimate the number of days from first symptoms until outbreak discovery and until first public communication. We report median discovery and communication delays overall, by region, and by Human Development Index (HDI) quartile. We use Cox proportional hazards regression to assess changes in these 2 outcomes over time, along with Loess curves for visualization. Improvement since 1996 was greatest in the Eastern Mediterranean and Western Pacific regions and in countries in the middle HDI quartiles. However, little progress has occurred since 2010. Further improvements in surveillance will likely require additional international collaboration with a focus on regions of low or unstable HDI.

Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons.

OBJECTIVES: We summarized Flu Near You (FNY) data from the 2012-2013 and 2013-2014 influenza seasons in the United States. METHODS: FNY collects limited demographic characteristic information upon registration, and prompts users each Monday to report symptoms of influenza-like illness (ILI) experienced during the previous week. We calculated the descriptive statistics and rates of ILI for the 2012-2013 and 2013-2014 seasons. We compared raw and noise-filtered ILI rates with ILI rates from the Centers for Disease Control and Prevention ILINet surveillance system. RESULTS: More than 61 000 participants submitted at least 1 report during the 2012-2013 season, totaling 327 773 reports. Nearly 40 000 participants submitted at least 1 report during the 2013-2014 season, totaling 336 933 reports. Rates of ILI as reported by FNY tracked closely with ILINet in both timing and magnitude. CONCLUSIONS: With increased participation, FNY has the potential to serve as a viable complement to existing outpatient, hospital-based, and laboratory surveillance systems. Although many established systems have the benefits of specificity and credibility, participatory systems offer advantages in the areas of speed, sensitivity, and scalability.

Why we need crowdsourced data in infectious disease surveillance.

In infectious disease surveillance, public health data such as environmental, hospital, or census data have been extensively explored to create robust models of disease dynamics. However, this information is also subject to its own biases, including latency, high cost, contributor biases, and imprecise resolution. Simultaneously, new technologies including Internet and mobile phone based tools, now enable information to be garnered directly from individuals at the point of care. Here, we consider how these crowdsourced data offer the opportunity to fill gaps in and augment current epidemiological models. Challenges and methods for overcoming limitations of the data are also reviewed. As more new information sources become mature, incorporating these novel data into epidemiological frameworks will enable us to learn more about infectious disease dynamics.