Characterizing the Dynamic of COVID-19 with a New Epidemic Model: Susceptible-Exposed-Symptomatic-Asymptomatic-Active-Removed
Bibliographic record
Abstract
Abstract The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread stealthily and presented a tremendous threat to the public. It is important to investigate the transmission dynamic of COVID-19 to help understand the impact of the disease on public health and economy. While a number of epidemic models have been available to study infectious diseases, they are in-adequate to describe the dynamic of COVID-19. In this paper, we develop a new epidemic model which utilizes a set of ordinary differential equations with unknown parameters to delineate the transmission process of COVID-19. Different from the traditional epidemic models, this model accounts for asymptomatic infections as well the lag between symptoms onset and the confirmation date of infection. We describe an estimation procedure for the unknown parameters in the proposed model by adapting the iterated filter-ensemble adjustment Kalman filter (IF-EAKF) algorithm to the reported number of confirmed cases. To assess the performance of our proposed model, we examine COVID-19 data in Quebec for the period of April 2, 2020 to May 10, 2020 and carry out sensitivity studies under a variety of assumptions. To reflect the transmission potential of an infected case, we derive the basic reproduction number from the proposed model. The estimated basic reproduction number suggests that the pandemic situation in Quebec for the period of April 2, 2020 to May 10, 2020 is not under control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".