Mortality from COVID-19 in 12 countries and 6 states of the United States
Bibliographic record
Abstract
ABSTRACT Importance Reliable estimates of COVID-19 mortality are crucial to aid control strategies and to assess the effectiveness of interventions. Objective Project COVID-19 mortality trends to October 1, 2020, in 12 countries or regions that constitute >90% of the global COVID-19 deaths reported as of April 12, 2020. Design, Setting, and Participants The Global COVID-19 Assessment of Mortality (GCAM) is an open, transparent, and continuously updated ( www.cghr.org/covid ) statistical model that combines actual COVID-19 mortality counts with Bayesian inference to forecast COVID-19 deaths, the date of peak deaths, and the duration of excess mortality. The analyses covered a total of 700 million population above age 20 in 12 countries or regions: USA; Italy; Spain; France; UK; Iran; Belgium; a province of China (Hubei, which accounted for 90% of reported Chinese deaths); Germany; the Netherlands; Switzerland; and Canada; and six US states: New York, New Jersey, Michigan, Louisiana, California, and Washington. Results Forecasted deaths across the 12 current high-burden countries sum 167,000 to 593,000 (median 253,000). The trajectory of US deaths (49,000-249,000 deaths; median 86,000)—over half of which are expected in states beyond the initial six states analysed in this study—will have the greatest impact on the eventual total. Mortality ranges are 25,000-109,000 (median 46,000) in the UK; 23,000-31,000 (median 26,000) in Italy; 21,000-37,000 (median 26,000) in France and 21,000-32,000 (median 25,000) in Spain. Estimates are most precise for Hubei, China—where the epidemic curve is complete—and least precise in California, where it is ongoing. New York has the highest cumulative median mortality rate per million (1135), about 12-fold that of Germany. Mortality trajectories are notably flatter in Germany, California, and Washington State, each of which took physical distancing and testing strategies seriously. Using past country-specific mortality as a guide, GCAM predicts surge capacity needs, reaching more than twice existing capacity in a number of places., In every setting, the results might be sensitive to undercounts of COVID-19 deaths, which are already apparent. Conclusion and Relevance Mortality from COVID-19 will be substantial across many settings, even in the best case scenario. GCAM will provide continually updated and increasingly precise estimates as the pandemic progresses. The coronavirus disease (COVID-19) pandemic has already caused over 115,000 deaths, with global deaths doubling every week. 1-3 Mortality is less biased than case reporting, which is affected by testing policies. However, the daily reporting of COVID-19 deaths is already known to undercount actual deaths, varying over time and place. 4-6 Reliable estimates of total COVID-19 mortality, the date of peak deaths, and of the duration of excess mortality are crucial to aid responses to the current and potential future pandemics. We have developed the Global COVID-19 Assessment of Mortality (GCAM), a statistical model to project COVID-19 mortality trends to October 1 2020 in 12 countries or regions that constitute >90% of the global COVID-19 deaths reported as of April 12 th . We report also on six US states that account for 70% of the American totals to date (Supplementary Appendix). 1 We quantify the COVID-19 mortality trajectory ranges in each setting. A semi-automated website ( www.cghr.org/covid ) provides daily updates. GCAM is open, transparent, and uses a reasonably simple method that employs publicly reported mortality data to make plausible projections. The method is designed to improve as more mortality data become available over longer time periods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".