Participatory surveillance for COVID-19 trends detection in Brazil

The 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 healthcare 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 collections approaches. This study compares novel PS data on COVID-19 infection rates across nine Brazilian cities with official TS data to examine the opportunities and challenges of using the former, and the potential advantages of combining the two approaches. We find that high participation rates are key for PS data to adequately mirror TS infection rates. Where participation was high, we document a significant trend correlation between lagged PS data and TS infection rates, suggesting that the former 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 horizon model based exclusively on TS data. Furthermore, we show that the PS data captures a population that significantly differs from the traditional observation. These results corroborate previous studies when it comes to the benefits of an integrated and comprehensive surveillance system, but also shed lights on its limitations, and on the need for additional research to improve future implementations of PS platforms.

Comparison of longitudinal trends in self-reported symptoms and COVID-19 case activity in Ontario, Canada

Background Limitations in laboratory diagnostic capacity impact population surveillance of COVID-19. It is currently unknown whether participatory surveillance tools for COVID-19 correspond to government-reported case trends longitudinally and if it can be used as an adjunct to laboratory testing. The primary objective of this study was to determine whether self-reported COVID-19-like illness reflected laboratory-confirmed COVID-19 case trends in Ontario Canada. Methods We retrospectively analyzed longitudinal self-reported symptoms data collected using an online tool–Outbreaks Near Me (ONM)–from April 20th, 2020, to March 7th, 2021 in Ontario, Canada. We measured the correlation between COVID-like illness among respondents and the weekly number of PCR-confirmed COVID-19 cases and provincial test positivity. We explored contemporaneous changes in other respiratory viruses, as well as the demographic characteristics of respondents to provide context for our findings. Results Between 3,849–11,185 individuals responded to the symptom survey each week. No correlations were seen been self-reported CLI and either cases or test positivity. Strong positive correlations were seen between CLI and both cases and test positivity before a previously documented rise in rhinovirus/enterovirus in fall 2020. Compared to participatory surveillance respondents, a higher proportion of COVID-19 cases in Ontario consistently came from low-income, racialized and immigrant areas of the province- these groups were less well represented among survey respondents. Interpretation Although digital surveillance systems are low-cost tools that have been useful to signal the onset of viral outbreaks, in this longitudinal comparison of self-reported COVID-like illness to Ontario COVID-19 case data we did not find this to be the case. Seasonal respiratory virus transmission and population coverage may explain this discrepancy.

Testing behaviour and positivity for SARS-CoV-2 infection: insights from web-based participatory surveillance in the Netherlands

ObjectivesWe aimed to identify populations at a high risk for SARS-CoV-2 infection but who are less likely to present for testing, by determining which sociodemographic and household factors are associated with a lower propensity to be tested and, if tested, with a higher risk of a positive test result.Design and settingInternet-based participatory surveillance data from the general population of the Netherlands.ParticipantsWeekly survey data collected over a 5-month period (17 November 2020 to 18 April 2021) from a total of 12 026 participants who had contributed at least 2 weekly surveys was analysed.MethodsMultivariable analyses using generalised estimating equations for binomial outcomes were conducted to estimate the adjusted ORs of testing and of test positivity associated with participant and household characteristics.ResultsMale sex (adjusted OR for testing (ORt): 0.92; adjusted OR for positivity (ORp): 1.30, age groups<20 (ORt: 0.89; ORp: 1.27), 50–64 years (ORt: 0.94; ORp: 1.06) and 65+ years (ORt: 0.78; ORp: 1.24), diabetics (ORt: 0.97; ORp: 1.06) and sales/administrative employees (ORt: 0.93; ORp: 1.90) were distinguished as lower test propensity/higher test positivity factors.ConclusionsThe factors identified using this approach can help identify potential target groups for improving communication and encouraging testing among those with symptoms, and thus increase the effectiveness of testing, which is essential for the response to the COVID-19 pandemic and for public health strategies in the longer term.

Digital SARS-CoV-2 detection among hospital employees: A Participatory Surveillance study. (Preprint)

BACKGROUND The implementation of novel techniques represents an additional opportunity for the rapid analysis acting as a complement to the traditional disease surveillance systems. OBJECTIVE The objective of this work is to describe a web-based participatory surveillance strategy among healthcare workers (HCW) in two Swiss hospitals during the first wave of COVID-19. METHODS A prospective cohort of HCW was initiated in March 2020 at the Cantonal Hospital of St. Gallen and the Eastern Switzerland Children’s Hospital. For data analysis, we used a combination of the following techniques: loess regression, spearman correlation, anomaly detection and random forest. RESULTS From March 23rd to August 23rd 2020, 127,684 SMS were sent generating 90,414 valid reports among 1,004 participants, achieving a weekly average of 4.5 reports per user (SD 1.9). The symptom showing the strongest correlation with a positive PCR result was loss of taste. Symptoms like red eyes or runny nose were negatively associated with a positive test. The area under the ROC curve showed favorable performance of the classification tree, with an accuracy of 88% for the training and 89% for the test data. Nevertheless, while the prediction matrix showed good specificity (80.0%), sensitivity was low at 10.6%. Loss of taste was the symptom which paralleled best with COVID-19 activity on the population level. On the resident level, using machine-learning based random forest classification, reporting of loss of taste and limb/muscle pain, as well as absence of runny nose and red eyes were the best predictors of COVID-19. CONCLUSIONS Nevertheless, we deem the presented surveillance tool highly useful in monitoring and predicting COVID-19 activity among our HCW.

FluWatchers: Evaluation of a crowdsourced influenza-like illness surveillance application for Canadian influenza seasons 2015–2016 to 2018–2019

Background: Sentinel influenza-like illness (ILI) surveillance is an essential component of a comprehensive influenza surveillance program. Community-based ILI surveillance systems that rely solely on sentinel healthcare practices omit important segments of the population, including those who do not seek medical care. Participatory surveillance, which relies on community participation in surveillance, may address some limitations of traditional ILI systems. Objective: We aimed to evaluate FluWatchers, a crowdsourced ILI application developed to complement and complete ILI surveillance in Canada. Methods: Using established frameworks for surveillance evaluations, we assessed the acceptability, reliability, accuracy and usefulness of the FluWatchers system 2015–2016, through 2018–2019. Evaluation indicators were compared against national surveillance indicators of ILI and of laboratory confirmed respiratory virus infections. Results: The acceptability of FluWatchers was demonstrated by growth of 50%–100% in season-over-season participation, and a consistent season-over-season retention of 80%. Reliability was greater for FluWatchers than for our traditional ILI system, although both systems had week-over-week fluctuations in the number of participants responding. FluWatchers’ ILI rates had moderate correlation with weekly influenza laboratory detection rates and other winter seasonal respiratory virus detections including respiratory syncytial virus and seasonal coronaviruses. Finally, FluWatchers has demonstrated its usefulness as a source of core FluWatch surveillance information and has the potential to fill data gaps in current programs for influenza surveillance and control. Conclusion: FluWatchers is an example of an innovative digital participatory surveillance program that was created to address limitations of traditional ILI surveillance in Canada. It fulfills the surveillance system evaluation criteria of acceptability, reliability, accuracy and usefulness.

A Long-Term Negative Effect of Monetary Incentives On Participatory Surveillance of Animal Disease

Background Generally animal diseases affect widely on human health, therefore practical animal diseases surveillance system is important controlling system to motivate participants in the long term. The objective of this study is to determine the effect of monetary and social motivation on the participatory surveillance of animal diseases. Based on the Fiske’s relational theory (1992), we proposed that there were basically two types of motivation; monetary incentives (monetary markets) and non-monetary incentives (social markets). Our hypothesis is that the effort by monetary motivation is higher than the efforts by social motivation during the payment period. This effort decreases at the end of the payment period, whereas the effort by social motivation is stable. Methods We analyzed the data generated by a pilot project that started in 2014 by using a smartphone application to report on the symptoms that indicate animal health malfunctions in Chiang Mai province, northern Thailand. This experiment involved the participation of 67 volunteers from 17 areas in the central part of the province. Results The results of the experiment demonstrated that monetary motivation was more effective during the payment period. However, after the termination of the payment period, the social motivation group was more effective. The volunteers given monetary motivation demonstrated not only lower effort than those given social motivation, but the group with monetary motivation was also not re-motivated immediately by social motivation after the payment period had terminated. Conclusions In the long run, the social motivation was more efficient and sustainable than the monetary motivation.

Analysis of a Vaping-Associated Lung Injury Outbreak through Participatory Surveillance and Archival Internet Data

The US Centers for Disease Control and Prevention alerted of a suspected outbreak of lung illness associated with using E-cigarette products in September 2019. At the time that the CDC published its alert little was known about the causes of the outbreak or who was at risk for it. Here we provide insights into the outbreak through analysis of passive reporting and participatory surveillance. We collected data about vaping habits and associated adverse reactions from four data sources pertaining to people in the USA: A participatory surveillance platform (YouVape), Reddit, Google Trends, and Bing. Data were analyzed to identify vaping behaviors and reported adverse events. These were correlated among sources and with prior reports. Data was obtained from 720 YouVape users, 4331 Reddit users, and over 1 million Bing users. Large geographic variation was observed across vaping products. Significant correlation was found among the data sources in reported adverse reactions. Models of participatory surveillance data found specific product and adverse reaction associations. Specifically, cannabidiol was found to be associated with fever, while tetrahydrocannabinol was found to be correlated with diarrhea. Our results demonstrate that utilization of different, complementary, online data sources provide a holistic view of vaping associated lung injury while augmenting traditional data sources.

Testing behaviour and positivity for SARS-CoV-2 infection: Insights from web-based participatory surveillance

BackgroundVoluntary testing for SARS-CoV-2 infection is an integral component of an effective response to the COVID-19 pandemic. It is essential to identify populations at a high risk for infection but who are less likely to present for testing. Here, we use internet-based participatory surveillance data from the Netherlands to identify sociodemographic and household factors that are associated with a lower propensity to be tested and, if tested, with a higher risk of a positive test result.MethodsMultivariable analyses using generalised estimating equations for binomial outcomes were conducted to estimate the adjusted odds ratios of testing and of positivity associated with participant and household characteristics.ResultsBased on five months (17 November 2020 to 18 April 2021) of weekly surveys obtained from 12,026 participants, males (adjusted odds ratio for testing (ORt): 0.92; adjusted odds ratio for positivity (ORp): 1.30, age-groups <20 (ORt: 0.89; ORp: 1.27) 50-64 years (ORt: 0.94; ORp: 1.06) and 65+ years (ORt: 0.78; ORp: 1.24), diabetics (ORt: 0.97; ORp: 1.06), and sales/administrative employees (ORt: 0.93; ORp: 1.90) were distinguished as lower propensity/higher positivity factors.ConclusionsThe factors identified using this approach can help identify potential target groups for improving communication and encouraging testing among those with symptoms and thus increase the effectiveness of testing, which is essential for the response to the COVID-19 pandemic and for public health strategies in the longer term.