Use of Unofficial Newspaper Data for COVID-19 Death Surveillance
Bibliographic record
Abstract
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.
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 | 0.003 | 0.018 |
| 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) | 0.001 | 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".