FluTracking during the COVID-19 pandemic: lessons learnt for future participatory surveillance

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.

Symptom-only case definitions are insufficient to discriminate COVID-like illness from acute respiratory infection (ARI) or influenza-like illness (ILI), due to the overlap in case definitions. Our objective was to develop a statistical method that does not rely on case definitions to determine the contribution of influenza virus and SARS-CoV-2 to the ARI burden during periods when both viruses are circulating. Data sources used for testing the approach were weekly ARI syndrome reports from the Infectieradar participatory syndromic surveillance system during the analysis period (the first 25 weeks of 2022, in which SARS-CoV-2 and influenza virus co-circulated in the Netherlands) and data from virologically tested ARI (including ILI) patients who consulted a general practitioner in the same period. Estimation of the proportions of ARI attributable to influenza virus, SARS-CoV-2, or another cause was framed as an inference problem, through which all data sources are combined within a Bayesian framework to infer the weekly numbers of ARI reports attributable to each cause. Posterior distributions for the attribution proportions were obtained using Markov Chain Monte-Carlo methods. Application of the approach to the example data sources indicated that, of the total ARI reports (total of 11,312; weekly mean of 452) during the analysis period, the model attributed 35.4% (95% CrI: 29.2-40.0%) and 27.0% (95% CrI: 19.3-35.2%) to influenza virus and SARS-CoV-2, respectively. The proposed statistical model allows the attribution of respiratory syndrome reports from participatory surveillance to either influenza virus or SARS-CoV-2 infection in periods when both viruses are circulating, but comparability of the participatory surveillance and virologically tested populations is important. Portability for use by other countries with established participatory respiratory surveillance systems is an asset.

BackgroundSince 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.ObjectiveThis landscape provides an overview of participatory disease surveillance systems in the IWOPS network and orients readers to this growing field of practice.MethodsAuthors 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.ResultsThere 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.ConclusionsParticipatory disease surveillance is a promising method to complement both traditional, facility-based surveillance and newer digital epidemiology systems.

Traditional surveillance of seasonal influenza is generally affected by reporting lags of at least one week and by continuous revisions of the numbers initially released. As a consequence, influenza forecasts are often limited by the time required to collect new and accurate data. On the other hand, the availability of novel data streams for disease detection can help in overcoming these issues by capturing an additional surveillance signal that can be used to complement data collected by public health agencies. In this study, we investigate how combining both traditional and participatory Web-based surveillance data can provide accurate predictions for seasonal influenza in real-time fashion. To this aim, we use two data sources available in Italy from two different monitoring systems: traditional surveillance data based on sentinel doctors reports and digital surveillance data deriving from a participatory system that monitors the influenza activity through Internet-based surveys. We integrate such digital component in a linear autoregressive exogenous (ARX) model based on traditional surveillance data and evaluate its predictive ability over the course of four influenza seasons in Italy, from 2012-2013 to 2015-2016, for each of the four weekly time horizons. Our results show that by using data extracted from a Web-based participatory surveillance system, which are usually available one week in advance with respect to traditional surveillance, it is possible to obtain accurate weekly predictions of influenza activity at national level up to four weeks in advance. Compared to a model that is only based on data from sentinel doctors, our approach significantly improves real-time forecasts of influenza activity, by increasing the Pearson's correlation up to 30% and by reducing the Mean Absolute Error up to 43% for the four weekly time horizons.

ObjectiveTo estimate and compare influenza attack rates (AR) in the United States (US) using different approaches to adjust for reporting biases in participatory syndromic surveillance data.IntroductionBecause the dynamics and severity of influenza in the US vary each season, yearly estimates of disease burden in the population are essential to evaluate interventions and allocate resources. The CDC uses data from a national health-care based surveillance system and mathematical models to estimate the overall burden of disease in the general population. Over the past decade, crowd-sourced syndromic surveillance systems have emerged as a digital data source that collects health-related information in near real-time. These systems complement traditional surveillance systems by capturing individuals who do not seek medical care and allowing for a longitudinal view of illness burden. However, because not all participants report every week and participants are more likely to report when ill, the number of weekly reports is temporally and spatially inconsistent and the estimates of disease burden and incidence may be biased. In this study, we use data from Flu Near You (FNY), a participatory surveillance system based in the US and Canada1, to estimate and compare Influenza-like Illness (ILI) ARs using different approaches to adjust for reporting biases in participatory surveillance data.MethodsThis analysis uses FNY data from the 2015-16 influenza season. Four different approaches of bias adjustment were assessed. The first approach includes all FNY participants, defined as users and household members, who submitted at least one symptom report, whereas the second approach only includes FNY participants who submitted at least 10 symptom reports. The third approach includes all FNY participants who submitted at least one symptom report, but drops the first symptom report for all participants. For the first three approaches, all missing reports were assumed to be non-ILI when estimating attack rates. Finally, the fourth approach includes FNY participants who submitted at least 10 symptom reports and uses multiple imputation to account for missing reports. Age-stratified and overall estimates of ILI ARs were calculated for each of the four approaches to bias adjustment by dividing the sum of the weekly incident cases of ILI, defined as the first report of fever with cough and/or sore throat, by the population at risk at the beginning of the period.ResultsDuring the 2016-2017 influenza season, FNY received an average of 10,723 unique symptom reports per week from 46,390 registered users and their household members. For FNY, the youngest age group assessed, 5-17, had the largest ILI AR, and the ILI ARs decreased as the age group increased for all approaches. Overall, the approach that drops all first reports had the smallest ARs, whereas the approach that selects a cohort of users who submit at least 10 reports during the season and imputes the missing reports had the largest ARs. Although the influenza ARs estimated by the CDC were less than the ILI ARs estimated using FNY data for all age-groups, a similar pattern was observed across age groups, except for the 50-64 age group, which had the largest influenza AR.ConclusionsAs expected, the ARs estimated using FNY data were greater than the CDC’s influenza ARs because FNY estimates ARs of ILI and does not adjust for the probability of reporting ILI when experiencing non-flu illness. The approach of dropping the first report had the smallest ARs because during the 2015-16 influenza season the weekly percent of ILI cases that were first time reports ranged from 18-59%. This approach was developed to adjust for the potential correlation between symptom presence and willingness to join the platform. However, important information about the dynamics of disease may be lost when using this approach. The multiple imputation method was used only for individuals who submitted at least 10 reports to maintain a missing data rate below 30%. The imputation model also assumed that data were missing at random, which may not be appropriate in this case, because approximately 30% of FNY users have reported that they are more likely to report when ill. As shown in Table 1, the AR estimate depends on the bias adjustment approach. Simulation-based studies should be performed to further evaluate these methods.

Global flu view: a platform to connect crowdsourced disease surveillance around the world

BackgroundWhile laboratory testing for infectious diseases such as COVID-19 is the surveillance gold standard, it is not always feasible, particularly in settings where resources are scarce. In the small country of Lesotho, located in sub-Saharan Africa, COVID-19 testing has been limited, thus surveillance data available to local authorities are limited. The goal of this study was to compare a participatory influenza-like illness (ILI) surveillance system in Lesotho with COVID-19 case count data, and ultimately to determine whether the participatory surveillance system adequately estimates the case count data.MethodsA nationally-representative sample was called on their mobile phones weekly to create an estimate of incidence of ILI between July 2020 and July 2021. Case counts from the website Our World in Data (OWID) were used as the gold standard to which our participatory surveillance data were compared. We calculated Spearman’s and Pearson’s correlation coefficients to compare the weekly incidence of ILI reports to COVID-19 case count data.ResultsOver course of the study period, an ILI symptom was reported 1,085 times via participatory surveillance for an average annual cumulative incidence of 45.7 per 100 people (95% Confidence Interval [CI]: 40.7 – 51.4). The cumulative incidence of reports of ILI symptoms was similar among males (46.5, 95% CI: 39.6 – 54.4) and females (45.1, 95% CI: 39.8 – 51.1). There was a slightly higher annual cumulative incidence of ILI among persons living in peri-urban (49.5, 95% CI: 31.7 – 77.3) and urban settings compared to rural areas. The January peak of the participatory surveillance system ILI estimates correlated significantly with the January peak of the COVID-19 case count data (Spearman’s correlation coefficient = 0.49; P < 0.001) (Pearson’s correlation coefficient = 0.67; P < 0.0001).ConclusionsThe ILI trends captured by the participatory surveillance system in Lesotho mirrored trends of the COVID-19 case count data from Our World in Data. Public health practitioners in geographies that lack the resources to conduct direct surveillance of infectious diseases may be able to use cell phone-based data collection to monitor trends.

BackgroundSeasonal 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.ObjectiveThis 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.MethodsFlu 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.ResultsILI 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.ConclusionsA 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.

BackgroundThe ongoing COVID-19 pandemic has emphasized the necessity of a well-functioning surveillance system to detect and mitigate disease outbreaks. Traditional surveillance (TS) usually relies on health care providers and generally suffers from reporting lags that prevent immediate response plans. Participatory surveillance (PS), an innovative digital approach whereby individuals voluntarily monitor and report on their own health status via web-based surveys, has emerged in the past decade to complement traditional data collection approaches.ObjectiveThis study compared novel PS data on COVID-19 infection rates across 9 Brazilian cities with official TS data to examine the opportunities and challenges of using PS data, and the potential advantages of combining the 2 approaches.MethodsThe TS data for Brazil are publicly accessible on GitHub. The PS data were collected through the Brazil Sem Corona platform, a Colab platform. To gather information on an individual’s health status, each participant was asked to fill out a daily questionnaire on symptoms and exposure in the Colab app.ResultsWe found that high participation rates are key for PS data to adequately mirror TS infection rates. Where participation was high, we documented a significant trend correlation between lagged PS data and TS infection rates, suggesting that PS data could be used for early detection. In our data, forecasting models integrating both approaches increased accuracy up to 3% relative to a 14-day forecast model based exclusively on TS data. Furthermore, we showed that PS data captured a population that significantly differed from a traditional observation.ConclusionsIn the traditional system, the new recorded COVID-19 cases per day are aggregated based on positive laboratory-confirmed tests. In contrast, PS data show a significant share of reports categorized as potential COVID-19 cases that are not laboratory confirmed. Quantifying the economic value of PS system implementation remains difficult. However, scarce public funds and persisting constraints to the TS system provide motivation for a PS system, making it an important avenue for future research. The decision to set up a PS system requires careful evaluation of its expected benefits, relative to the costs of setting up platforms and incentivizing engagement to increase both coverage and consistent reporting over time. The ability to compute such economic tradeoffs might be key to have PS become a more integral part of policy toolkits moving forward. These results corroborate previous studies when it comes to the benefits of an integrated and comprehensive surveillance system, and shed light on its limitations and on the need for additional research to improve future implementations of PS platforms.

Early in the COVID-19 pandemic, Denmark launched COVIDmeter, a national participatory surveillance platform collecting real-time, self-reported symptoms from a community cohort, aimed to support early signal detection of COVID-like illness. This study describes the community cohort, the reported symptoms among persons testing positive and evaluates COVIDmeter’s performance in detecting trends compared to other established surveillance indicators. A total of 143000 individuals registered as participants, of whom 98% completed at least one weekly questionnaire, resulting in approximately 5.8 million responses over the period from March 2020 to March 2023. Of those who tested positive, the most commonly reported symptoms overall were headache, fatigue, muscle or body aches, cough and fever. Trends in COVID-like illness followed similar patterns to other indicators, with COVID-like illness peaks often preceding increases in incidence and hospital admissions, suggesting early detection potential. The study demonstrated that participatory surveillance can serve as an early detection tool for tracking infection trends, particularly in the early stages of a pandemic. While subject to limitations such as selection bias and self-reporting inaccuracies and participatory symptom surveillance proved to be a rapid, scalable and cost-effective complement to traditional surveillance independent of virus testing, this highlights its relevance for future pandemic preparedness.

Declines in childhood vaccination in the U.S. have contributed to a resurgence of vaccine-preventable diseases, including a notable increase in pertussis cases. Traditional pertussis surveillance is limited by underdiagnosis and underreporting. Participatory surveillance systems such as Outbreaks Near Me (ONM) provide an additional population-level data stream by capturing self-reported symptoms. Although pertussis signals are difficult to detect due to low incidence and symptom overlap with other infections, ONM collects free-text descriptions that may contain pertussis-specific information. Advances in large language models (LLMs) enable the extraction of relevant signals from unstructured text to potentially improve forecasting. We analyzed U.S. pertussis case data from the CDC and ONM reports from 2022 to 2025. ONM reports were filtered for prolonged cough without alternative diagnoses and further refined using a two-step GPT-4-based pipeline that summarized participant reports and excluded cases inconsistent with pertussis to enhance case specificity. Three datasets were created: CDC-only cases, CDC and ONM filtered cases, and CDC and ONM cases post-LLM processing. Aggregated time series were split into a training set (2022-2024) and a test set (2025, first 7 months). We trained multiple forecasting models (ARIMA, XG-Boost, and linear regression) on the 2022-2024 data, first using CDC-only data to establish a baseline. The best-performing model was then applied to the two datasets, incorporating the ONM participatory data. Performance was evaluated using Mean Absolute Error (MAE). CDC-reported pertussis cases totaled 862 in 2022, 2,512 in 2023, 11,276 in 2024, and 5,937 in the first seven months of 2025. Of 2,741 ONM-suspected cases, 957 remained after LLM refinement. XGBoost yielded the best baseline performance (MAE 26.65). Incorporating ONM data improved performance: MAE decreased to 25.60 with filtered ONM cases and 24.69 with LLM-processed cases. Integrating LLM-processing of participatory surveillance data with traditional surveillance enhances the accuracy of pertussis outbreak forecasting. This approach introduces a novel way to leverage free-text data, offering a promising pathway to augment traditional public health surveillance systems.

BackgroundInfluenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact.ObjectiveDescribe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions.MethodsWe describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You).ResultsWISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information.ConclusionsWhile the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world.

The 21st century has seen the rise of Internet-based participatory surveillance systems for infectious diseases. These systems capture voluntarily submitted symptom data from the general public and can aggregate and communicate that data in near real-time. We reviewed participatory surveillance systems currently running in 13 different countries. These systems have a growing evidence base showing a high degree of accuracy and increased sensitivity and timeliness relative to traditional healthcare-based systems. They have also proven useful for assessing risk factors, vaccine effectiveness, and patterns of healthcare utilization while being less expensive, more flexible, and more scalable than traditional systems. Nonetheless, they present important challenges including biases associated with the population that chooses to participate, difficulty in adjusting for confounders, and limited specificity because of reliance only on syndromic definitions of disease limits. Overall, participatory disease surveillance data provides unique disease information that is not available through traditional surveillance sources.

BackgroundThe 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.ObjectiveThe 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).MethodsFrom 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.ResultsFrom 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).ConclusionsParticipatory 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.

Emerging pathogens and zoonotic spillover highlight the need for One Health surveillance to detect outbreaks as early as possible. Participatory surveillance empowers communities to collect data at the source on the health of animals, people, and the environment. Technological advances increase the use and scope of these systems. This initiative sought to collate information from active participatory surveillance systems to better understand parameters collected across the One Health spectrum. This study aims to develop a compendium of One Health data parameters by examining participatory surveillance systems active in 2023. The expected outcomes of the compendium were to pinpoint specific parameters related to human, animal, and environmental health collected globally by participatory surveillance systems and to detail how each parameter is collected. The compendium was designed to help understand which parameters are currently collected and serve as a reference for future systems and for data standardization initiatives. Contacts associated with the 60 systems identified through the One Health Participatory Surveillance System Map were invited by email to provide specific data parameters, methodologies used for data collection, and parameter-specific considerations. Information was received from 38 (63%) active systems. Data were compiled into a searchable spreadsheet-based compendium organized into 5 sections: general, livestock, wildlife, environmental, and human parameters. An advisory group comprising experts in One Health participatory surveillance reviewed the collected parameters, refined the compendium structure, and contributed to the descriptive analysis. A comprehensive compendium of data parameters from a diverse array of single-sector and multisector participatory surveillance systems was collated and reviewed. The compendium includes parameters from 38 systems used in Africa (n=3, 8%), Asia (n=9, 24%), Europe (n=12, 32%), Australia (n=3, 8%), and the Americas (n=12, 32%). Almost one-third of the systems (n=11, 29%) collect data across multiple sectors. Many (n=17, 45%) focus solely on human health. Variations in data collection techniques were observed for commonly used parameters, such as demographics and clinical signs or symptoms. Most human health systems collected parameters from a cohort of users tracking their own health over time, whereas many wildlife and environmental systems incorporated event-based parameters. Several participatory surveillance systems have already adopted a One Health approach, enhancing traditional surveillance by identifying shared health threats among animals, people, and the environment. The compendium reveals substantial variation in how parameters are collected, underscoring the need for further work in system interoperability and data standards to allow for timely data sharing across systems during outbreaks. Parameters collated from across the One Health spectrum represent a valuable resource for informing the development of future systems and identifying opportunities to expand existing systems for multisector surveillance.