A three-state coupled Markov switching model for COVID-19 outbreaks across Quebec based on hospital admissions
Bibliographic record
Abstract
Recurrent COVID-19 outbreaks have placed immense strain on the hospital system in Quebec. We develop a Bayesian three-state coupled Markov switching model to analyze COVID-19 outbreaks across Quebec based on admissions in the 30 largest hospitals. Within each catchment area, we assume the existence of three states for the disease: absence, a new state meant to account for many zeroes in some of the smaller areas; endemic and outbreak. Then we assume the disease switches between the three states in each area through a series of coupled nonhomogeneous hidden Markov chains. Unlike previous approaches, the transition probabilities may depend on covariates and the occurrence of outbreaks in neighboring areas to account for geographical outbreak spread. Additionally, to prevent rapid switching between endemic and outbreak periods we introduce clone states into the model which enforce minimum endemic and outbreak durations. We make some interesting findings, such as that mobility in retail and recreation venues had a positive association with the development and persistence of new COVID-19 outbreaks in Quebec. Based on model comparison, our contributions show promise in improving state estimation retrospectively and in real-time, especially when there are smaller areas and highly spatially synchronized outbreaks. Furthermore, our approach offers new and interesting epidemiological interpretations, such as being able to estimate the effect of covariates on disease extinction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".