Anticipating the curve: can online symptom-based data reflect COVID-19 case activity in Ontario, Canada?
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".