Forecasting COVID-19 pandemic in Alberta, Canada using modified ARIMA models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Auto regressive integrated moving average (ARIMA) model is a popular model to forecast future values of a time series using the past values of the same series. However, if the variance of the time series varies with time, the 95% confidence interval estimated by the ARIMA will not be accurate. This study proposes a method to revise the ARIMA model to suit time series with heteroscedasticity. METHODS: Multiple historical ARIMA models were constructed with publicly available COVID-19 data in Alberta, Canada. The time series between different time periods were applied for these models. The means and their 95% confidence intervals of the differences between the forecasted values and the corresponding actual values were computed. The forecasted values of the general ARIMA models were modified by adding these differences. RESULTS: The average incident cases forecasted with the proposed method are lower than those with a general ARIMA model during the forecasted period. The 95% confidence intervals of the forecasted incidence with the proposed method are narrower. During the forecasted period (13 weeks) the average incidence was predicted to increase first and then decrease exponentially. CONCLUSION: The proposed method can be used to automatically specify the best ARIMA model, to fit time series with heteroscedasticity and to forecast longer period of the trends in the future. In the next 13 weeks, the Covid-19 incidence may decrease but not eliminate. To stop the transmission of infections eventually, persistent effects complying with accurate forecasts are necessary.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it