Forecasting the daily COVID-19 incidence in large and small communities -- A comparative study of forecasting approaches for disease surveillance
Bibliographic record
Abstract
Introduction The COVID-19 pandemic highlighted the importance for accurate and timely forecasts to support public health preparedness. Automated surveillance systems fit with pre-built model offers efficiency, but identifying optimal model difficult due to variations in populations. This study assessed forecasting accuracy in regions with substantial differences in population-at-risk sizes. Materials and methods COVID-19 daily incidence was forecasted for Wellington-Dufferin-Guelph Public Health (WDGPH) and Toronto Public Health (TPH), Ontario, Canada with population sizes differing by a factor of approximately 20. Datasets were split into training data (18 January 2020, to 5 November 2021) and validation data (6 November 2021, to December 3, 2021). The models applied were General Linear Autoregressive Moving Average, Seasonal Autoregressive Integrated Moving Average, and Regression with ARIMA errors, Neural Network Autoregression and Random Forest. Ensembles combining several models were then generated to investigate improvement in predictive performance. Results and discussion Random Forest provided the highest 28-day forecast accuracy with Mean Absolute Scaled Prediction Error (MASE) of 0.68, Root Mean Squared Prediction Error (RMSE) of 13.66 for TPH and MASE of 0.71 and RMSE of 4.40 for WDGPH. Statistical models, although simpler to implement, did not perform as well while ensemble modeling provided no improvement in forecast accuracy.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".