Estimation of time-varying reproduction numbers of COVID-19 in American countries with regards to non-pharmacological interventions
Bibliographic record
Abstract
This study aimed to estimate the basic reproduction number and the time-varying estimate of effective reproductive number of COVID-19 in American countries as they implemented non-pharmacological strategies for the containment of the SARS-CoV-2 virus. Data sources included COVID-19 epidemic data from Johns Hopkins University’ data repository and official websites of countries with a relatively high incidence of COVID-19. The maximum likelihood method was used to estimate the and . The results showed that El Salvador, the Dominican Republic, Panama, and Peru have the lowest, while the USA and Canada have the highest. Other American countries have an around 1.4. Countries could be divided into three groups based on the varied behavior of over time. The first group (Mexico, USA, Colombia and Brazil) started with a high, which decreased post-intervention. In the second group, the intervention was performed at the moment when the, is high and it decreased slowly post-intervention (Canada, Argentina, Chile Peru, Panama and Dominican Republic). In the third group (Bolivia, Peru and Guatemala), the, was erratic and could not be attributable to the intervention. There is a close relationship between and non-pharmacological interventions decreed by governments of countries for the control of the COVID-19 pandemic. There are also immediate changes in the behavior of the indicator, and therefore the progression of the outbreak, when the interventions were implemented closer to the index case for each country.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".