Use of Unofficial Newspaper Data for COVID-19 Death Surveillance
Bibliographic record
Abstract
Objective. To highlight the critical importance of unofficially reported newspaper-based deaths from coronavirus disease 2019 (COVID-19)–like illness (CLI) together with officially confirmed death counts to support improvements in COVID-19 death surveillance.Methods. Both hospital-based official COVID-19 and unofficial CLI death counts were collected from daily newspapers between March 8 and August 22, 2020. We performed both exploratory and time-series analyses to understand the influence of combining newspaper-based CLI death counts with confirmed hospital death counts on the trends and forecasting of COVID-19 death counts. An autoregressive integrated moving average–based approach was used to forecast the number of weekly death counts for six weeks ahead.Results. Between March 8 and August 22, 2020, 2,156 CLI deaths were recorded based on newspaper reporting for a count that was 55% of the officially confirmed death count (n = 3,907). This shows that newspaper reports tend to cover a significant number of COVID-19 related deaths. Our forecast also indicates an approximate total of 406 CLI expected for the six weeks ahead, which could contribute to a total of 2,413 deaths including 2,007 confirmed deaths expected from August 23 to October 3, 2020.Conclusions. Analyzing existing trends in and forecasting the expected number of newspaper-based CLI deaths indicates yet-unreported COVID-19 death counts, which could be a critical source to estimate provisional COVID-19 death counts and mortality surveillance.Public Health Implications. Considering unofficial newspaper-based CLI death counts is essential to identify COVID-19 death severity and surveillance needs to advance public health research efforts to prepare appropriate response strategies for low- and middle-income countries.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".