A Machine Learning-enabled SIR Model for Adaptive and Dynamic Forecasting of COVID-19
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has posed significant challenges to public health systems worldwide, necessitating accurate and adaptable forecasting models to manage and mitigate its impacts. This study presents a novel forecasting framework based on a Machine Learning-enabled Susceptible-Infected-Recovered (ML-SIR) model with time-varying parameters to predict COVID-19 dynamics across multiple geographies. The model incorporates emergent patterns from reported time-series data to estimate new hospitalisations, hospitalised patients, and new deaths. Our framework adapts to the evolving nature of the pandemic by dynamically adjusting the infection rate parameter over time and using a Fourier series to capture oscillating patterns in the data. This approach improves upon traditional SIR and forecasting models, which often fail to account for the complex and shifting dynamics of COVID-19 due to new variants, changing public health interventions, and varying levels of immunity. Validation of the model was conducted using historical data from the United States, Italy, the United Kingdom, Canada, and Japan. The model’s performance was evaluated based on the Mean Absolute Percentage Error (MAPE) and Absolute Percentage Error of Cumulative values (CAPE) for three-month forecast horizons. Results indicated that the model achieved an average MAPE of 32.5% for new hospitalisations, 34.4% for patients, and 34.8% for new deaths, for three-month forecasts. Notably, the model demonstrated superior accuracy compared to existing forecasting models with like-for-like disease metrics, countries and forecast horizons. The proposed ML-SIR model offers a robust and adaptable tool for forecasting COVID-19 dynamics, capable of adjusting to new time-series data and varying geographical contexts. This adaptability makes it suitable for localised hospital capacity planning, scenario modelling, and for application to other respiratory infectious diseases with similar transmission dynamics, such as influenza and RSV. By providing reliable forecasts, the model supports informed public health decision-making and resource allocation, enhancing preparedness and response efforts.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".