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

  1. Arjuna S. Maharaj
  2. Jennifer Parker
  3. Jessica P. Hopkins
  4. Effie Gournis
  5. Isaac I. Bogoch
  6. Benjamin Rader
  7. Christina M. Astley
  8. Noah M. Ivers
  9. Jared B. Hawkins
  10. Liza Lee
  11. Ashleigh R. Tuite
  12. David N. Fisman
  13. John S. Brownstein
  14. Lauren Lapointe-Shaw

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Abstract

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 20 th , 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.

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  1. Version published to 10.1371/journal.pone.0262447
  2. ScreenIT

    SciScore for 10.1101/2021.01.15.21249879: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Ethics Review Board of University Health Network and the University of Toronto and a waiver of informed consent was granted because the data were collected for public health surveillance purposes.
    Consent: This study was approved by the Ethics Review Board of University Health Network and the University of Toronto and a waiver of informed consent was granted because the data were collected for public health surveillance purposes.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Early limitations on testing to only the most severe cases caused uncertainties in true reported case counts6. Eligibility criteria for testing have changed over the course of the pandemic - most recently, testing was again restricted to those with symptoms, and only available by pre-scheduled appointment, effectively restricting the number of tests available19. Survey data that differs in demographic characteristics from those that received a test can provide valuable insights and fill knowledge gaps about groups not receiving testing. The addition of symptom surveillance information ensures that the most representative and timely data is used to inform policy decisions. Participatory surveillance data also demonstrated that an increasing proportion of those reporting CLI, and of those testing positive for SARS-COV-2, were ages <20 and 20-39 years. In April those <40 years made up ∼25% of positive COVID-19 tests. As of October 2020, approximately 50% of positive COVID-19 tests in Ontario were individuals <40 years old. Similarly, approximately 50% of those with a COVID-like illness were <40 years of age at the end of our study observation period. These findings demonstrate the utility of ONM to detect the evolving demographic distribution of COVID-19. We found that 0.12% – 0.66% of ONM respondents reported being tested for COVID-19. This was similar to the weekly Ontario population testing rate which ranged from 0.04% - 0.28% of the population. ONM respondents consistently r...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2021.01.15.21249879v1 on medRxiv