Participatory surveillance for COVID-19 trends detection in Brazil

Abstract 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.