Evolution of case fatality rates in the second wave of coronavirus in England: effects of false positives, a Variant of Concern and vaccination
Bibliographic record
Abstract
1 Abstract Objective To track the statistical case fatality rate (CFR) in the second wave of the UK coronavirus outbreak, and to understand its variations over time. Design Publicly available UK government data and clinical evidence on the time between first positive PCR test and death are used to determine the relationships between reported cases and deaths, according to age groups and across regions in England. Main Outcome Measures Estimates of case fatality rates and their variations over time. Results Throughout October and November 2020, deaths in England can be broadly understood in terms of CFRs which are approximately constant over time. The same CFRs prove a poor predictor of deaths when applied back to September, when prevalence of the virus was comparatively low, suggesting that the potential effect of false positive tests needs to be taken into account. Similarly, increasing CFRs are needed to match cases to deaths when projecting the model forwards into December. The growth of the S gene dropout VOC in December occurs too late to explain this increase in CFR alone, but at 33% increased mortality, it can explain the peak in deaths in January. Seasonal effects could be in part responsible for the early December increase in CFR, and if so, the estimate of increased mortality would be reduced. There is also evidence that the prevalence of B.1.1.7 may have been slower amongst older age groups, and if this is a factor, then 33% could be an underestimate of mortality. From the second half of January, the CFRs for older age groups show a marked decline. Since the fraction of the VOC has not decreased, this decline is likely to be the result of the rollout of vaccination. However, due to the rapidly decreasing nature of the raw cases, any imprecisions in the time-to-death distribution are magnified in this time period, rendering estimates of vaccination’s effect less precise. Conclusions The relationship between cases and deaths, even when controlling for age, is not static through the second wave of coronavirus in England. An apparently anomalous low case-fatality ratio in September can be accounted for by a 0.4% false-positive fraction. The rapid growth in CFR in December can be understood in part in terms of a more deadly new variant B.1.1.7, while a decline in January correlates with vaccine roll-out, suggesting that vaccine reduce the severity of infection, as well as the risk. Summary Box What is already known on this topic The case fatality rate (CFR) is a useful measure which enables one to estimate future deaths based on current infections. In England, there was a surge in Covid-19 CFR around the beginning of December. What the study adds Using it, we monitor the case-fatality rate across time, region and age group from publicly available case data. This quantity is related to the lethality of the virus. It shows a sharp increase in December 2020, which parallels the spread of the B.1.1.7 variant. The January peak in actual deaths matches that predicted by cases if B.1.1.7 is about 33% more deadly; this estimate would be lower if there is a seasonal effect on deaths, and higher if at the peak the variant was less pervasive amongst older age groups. A steady drop in CFR from January suggests that vaccination not only reduces transmission but also the risk of serious illness among those infected. It is notable that these conclusions are reached with publicly available data independent of clinical studies.
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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".