Participatory surveillance for COVID-19 trends detection in Brazil (preprint)

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.

Prioritizing COVID-19 tests based in Participatory Surveillance and Spatial Scanning. (preprint)

Objectives This study aimed to identify, describe and analyze priority areas for Covid-19 testing combining participatory surveillance and traditional surveillance. Design It was carried out a descriptive transversal study in the city of Caruaru, Pernambuco state, Brazil, within the period of 20/02/2020 to 05/05/2020. Data included all official reports for influenza-like illness notified by the municipality health department and the self-reports collected through the participatory surveillance platform Brasil Sem Corona. Methods We used linear regression and loess regression to verify a correlation between Participatory Surveillance (PS) and Traditional Surveillance (TS) . Also a spatial scanning approach was deployed in order to identify risk clusters for Covid-19. Results In Caruaru, the PS had 861 active users, presenting an average of 1.2 reports per user per week. The platform Brasil Sem Corona started on March 20th and since then, has been officially used by the Caruaru health authority to improve the quality of information from the traditional surveillance system. Regarding the respiratory syndrome cases from TS, 1,588 individuals were positive for this clinical outcome. The spatial scanning analysis detected 18 clusters and 6 of them presented statistical significance (p-value < 0.1). Clusters 3 and 4 presented an overlapping area that was chosen by the local authority to deploy the Covid-19 serology, where 50 individuals were tested. From there, 32% (n=16) presented reagent results for antibodies related to Covid-19. Conclusion Participatory surveillance is an effective epidemiological method to complement the traditional surveillance system in response to the COVID-19 pandemic by adding real-time spatial data to detect priority areas for Covid-19 testing.