Using reinforcement learning to forecast the spread of COVID-19 in France
Bibliographic record
Abstract
In December 2020, a new strain of coronavirus was found in Wuhan, China. The virus causes COVID-19, a severe respiratory illness. Up to date, the virus has spread rapidly to many countries, and more than 103 million cases and 2 million death has been reported worldwide. France is one of the European Union countries that has reported more than 3 million cases and 76 thousand death. Prediction of the COVID-19 pandemic growth is essential to enable governments to put new measures to slow down the spread of the virus. Due to the virus’s novelty, providing an efficient method to predict pandemic growth is a challenging task. This research applies a recent reinforcement learning-based algorithm to a recently developed model to simulate the COVID-19 pandemic in France. We provide essential information about the pandemic growth in the country in every period in which the government of France has taken action to limit the pandemic or relaxed existing restrictions. We derive the values of the pandemic parameters, including reproduction rate, which gives us essential information about the pandemic. This information will help policymakers and healthcare professionals to plan for future measures limiting community transmission. Besides, we performed sensitivity analyses to determine the most critical parameters that accelerate the pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".