Second Wave Analysis and Confirmed Forecasts of the SARS-Cov-2 Epidemic Outbreak in São Paulo, Brazil
Bibliographic record
Abstract
Objective: A SEIR compartmental model was previously selected to estimate future outcomes to the dynamics of the Covid-19 epidemic breakout in Brazil. Method: Compartments for individuals vaccinated and prevalent SARS-Cov-2 variants were not included. A time-dependent incidence weight on the reproductive basic number accounted for Non Pharmaceutical Interventions (NPI). A first series of published data from March 1st to May 8, 2020 was used to adjust all model parameters aiming to forecast one year of evolutionary outbreak. The cohort study was set as a city population-based analysis. Analysis: A population-based sample of 25,366 confirmed cases on exposed individuals was used during the first study period. The analysis was applied to predict the consequences of NPI enforcements followed by progressive releases, and indicates the appearance of a second wave starting last quarter of 2020. Findings: By March 1st 2021, the number of confirmed cases was predicted to reach 0.47Million (0.24-0.78), and fatalities would account for 21 thousand (12-33), 5 to 95% CRI. A second series of data published from May 9, 2020 to March 1st, 2021 confirms the forecasts previously reported for the evolution of infected people and fatalities. Novelty: By March 1st 2021, the number of confirmed cases reached 527,710 (12% above the predicted average of accumulated cases) and fatalities accounted for 18,769 (10% below the accumulated average of estimated fatalities). After March 1st, new peaks on reported numbers of daily new infected and new fatalities appeared as a combined result to the appearance of the prevalent SARS-CoV-2 P1 variant, and the increased number of vaccinated individuals. Doi: 10.28991/SciMedJ-2021-03-SI-10 Full Text: PDF
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".