Anticipating the curve: can online symptom-based data reflect 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 Ivers
  9. Jared B. Hawkins
  10. Liza Lee
  11. Ashleigh R. Tuite
  12. David N. Fisman
  13. John S. Brownstein
  14. Lauren Lapointe-Shaw
1 evaluations Published on
Read the full article Related papers
This article on Sciety

Abstract

Background

Limitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing COVID-19 pandemic globally. Syndromic surveillance of COVID-19 is an important public health tool that can help detect outbreaks, mobilize a rapid response, and thereby reduce morbidity and mortality. The primary objective of this study was to determine whether syndromic surveillance through self-reported COVID-19 symptoms could be a timely proxy for laboratory-confirmed case trends in the Canadian province of Ontario.

Methods

We retrospectively analyzed self-reported symptoms data collected using an online tool – Outbreaks Near Me (ONM) – from April 20 th to Oct 11 th , 2020 in Ontario, Canada. We estimated the correlation coefficient between the weekly proportion of respondents reporting a COVID-like illness (CLI) to both the weekly number of PCR-confirmed COVID-19 cases and the percent positivity in the same period for the same week and with a one-week lag.

Results

There were 314,686 responses from 188,783 unique respondents to the ONM symptom survey. Respondents were more likely to be female and be in the 40-59 age demographic compared to the Ontario general population. There was a strong positive correlation between the weekly number of reported cases in Ontario and the percent of respondents reporting CLI each week (r = 0.89, p <0.01) and with a one-week lag (r = 0.89, p <0.01).

Interpretation

We demonstrate a strong positive and significant correlation (r = 0.89, p <0.01) between percent of self-reported COVID-like illness and the subsequent week’s COVID-19 cases reported, highlighting that a rise in CLI may precede official statistics by at least 1 week. This demonstrates the utility of syndromic surveillance in predicting near-future disease activity. Digital surveillance systems are low-cost tools that may help measure the burden of COVID-19 in a community if there is under-detection of cases through conventional laboratory diagnostic testing. This additional information can be used to guide a healthcare response and policy decisions.

Related articles

Related articles are currently not available for this article.