The Influence of COVID-19 Vaccination on Daily Cases, Hospitalization, and Death Rate in Tennessee, United States: Case Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 outbreak highlights our vulnerability to novel infections, and vaccination remains a foreseeable method to return to normal life. However, infrastructure is inadequate for the immediate vaccination of the whole population. Therefore, policies have adopted a strategy to vaccinate older adults and vulnerable populations while delaying vaccination for others. OBJECTIVE: This study aimed to understand how age-specific vaccination strategies reduce daily cases, hospitalizations, and death rates using official statistics for Tennessee, United States. METHODS: This study used publicly available data on COVID-19, including vaccination rates, positive cases, hospitalizations, and deaths from the Tennessee Department of Health. Data from the first date of vaccination (December 17, 2020) to March 3, 2021, were retrieved. The rates were adjusted by 2019 data from the US Census Bureau, and age groups were stratified into 10-year intervals starting with 21 years of age. RESULTS: The findings showed that vaccination strategy can reduce the numbers of patients with COVID-19 in all age groups, with lower hospitalization and death rates in older populations. Older adults had a 95% lower death rate from December to March; no change in the death rate of other age groups was observed. The hospitalization rate was reduced by 80% for people aged ≥80 years, while people who were 50 to 70 years old had nearly the same hospitalization rate as prior to vaccination. CONCLUSIONS: This research indicates that targeting older age groups for vaccination is the optimal way to avoid higher transmissions and reduce hospitalization and death rates.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".