Assessing the severity of COVID-19 waves: beware of surveillance bias
Bibliographic record
Abstract
Abstract Background Indicators to assess the severity of epidemic waves during the COVID-19 pandemic were influenced by differences in detection modalities over time, leading to surveillance bias. We compared four indicators across three pandemic periods to assess surveillance bias. Methods We used data from one region of Switzerland. We compared seroprevalence, cases, hospitalizations, and deaths during three periods (period 1: Feb-Oct 2020, including the 1st wave; period 2: Oct 2020-Feb 2021, including the 2nd wave; period 3: Feb-Aug 2021, including the 3rd wave and after the start of the vaccination campaign). Data were retrieved from the Swiss Federal Office of Public Health or population-based studies. We compared each indicator to a reference indicator (seroprevalence during periods 1 and 2 and hospitalizations during period 3). We also assessed the timeliness of the indicators, i.e., the duration from data generation to the availability of the information to decision-makers. Results According to seroprevalence estimates, the severity of the 2nd wave was slightly larger (by a ratio of 1.4) than the severity of the 1st wave. Compared to seroprevalence, cases largely overestimated the 2nd wave severity (2nd vs 1st wave ratio: 6.5) while hospitalizations (ratio: 2.2) and deaths (ratio: 2.9) were more suitable to compare the severity of these waves. According to hospitalizations, the 3rd wave severity was slightly smaller (by ratio of 0.7) than the 2nd wave. Compared to hospitalizations, cases or deaths slightly underestimated the 3rd wave severity (3rd vs 2nd wave ratio for cases: 0.5; for deaths: 0.4) and seroprevalence was very biased due to high vaccination rates. Across all waves, timeliness for cases and hospitalizations was better than for deaths or seroprevalence. Conclusions To assess the severity of pandemic waves accounting for surveillance bias, different types of indicators must be used across time. Key messages • Differences in detection modalities over time can skew the assessment of the severity of COVID-19 pandemic waves, leading to surveillance bias. • The effectiveness of indicators in describing the severity of COVID-19 pandemic waves depends on the type of indicator used and the stage of the pandemic.
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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 | 0.109 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".