Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study
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 preven...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-04-01
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| Series: | JMIR Public Health and Surveillance |
| Online Access: | https://publichealth.jmir.org/2023/1/e44517 |
| _version_ | 1797734080890011648 |
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| author | Salome Wittwer Daniela Paolotti Guilherme Lichand Onicio Leal Neto |
| author_facet | Salome Wittwer Daniela Paolotti Guilherme Lichand Onicio Leal Neto |
| author_sort | Salome Wittwer |
| collection | DOAJ |
| description |
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. |
| first_indexed | 2024-03-12T12:39:06Z |
| format | Article |
| id | doaj.art-30593f7e1a534ea08bf442da51e76a02 |
| institution | Directory Open Access Journal |
| issn | 2369-2960 |
| language | English |
| last_indexed | 2024-03-12T12:39:06Z |
| publishDate | 2023-04-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Public Health and Surveillance |
| spelling | doaj.art-30593f7e1a534ea08bf442da51e76a022023-08-28T23:57:54ZengJMIR PublicationsJMIR Public Health and Surveillance2369-29602023-04-019e4451710.2196/44517Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional StudySalome Wittwerhttps://orcid.org/0009-0006-1819-4319Daniela Paolottihttps://orcid.org/0000-0003-1356-3470Guilherme Lichandhttps://orcid.org/0000-0002-9118-1745Onicio Leal Netohttps://orcid.org/0000-0001-5785-1867 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.https://publichealth.jmir.org/2023/1/e44517 |
| spellingShingle | Salome Wittwer Daniela Paolotti Guilherme Lichand Onicio Leal Neto Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study JMIR Public Health and Surveillance |
| title | Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study |
| title_full | Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study |
| title_fullStr | Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study |
| title_full_unstemmed | Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study |
| title_short | Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study |
| title_sort | participatory surveillance for covid 19 trend detection in brazil cross sectional study |
| url | https://publichealth.jmir.org/2023/1/e44517 |
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