Predicting the weekly COVID-19 new cases using multilayer perceptron: An evidence from west Java, Indonesia
Why this work is in the frame
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Bibliographic record
Abstract
COVID-19 is a contagious disease caused by the coronavirus (SARS-CoV-2) that attacks the respiratory tract. On August 14th, 2021, 653,741 persons had been proven positive for COVID-19. The number of patients tends to increase as the number of COVID-19 cases grows. The more infected people, the more cases of COVID-19 there will be. The Bed Occupancy Ratio (BOR) in West Java reached an all-time high of 91.6 percent in June 2021, far exceeding the WHO recommendation of 60 percent, before gradually declining to 30.69 percent in August. Because of the new cases mentioned, the rate of spread of COVID-19 in West Java, the forecast of new cases is very strategic. The number of new cases in this study was predicted using a Multilayer Perceptron (MLP). The data used in this study were sourced from the COVID-19 Task Force. The data is the number of positive and new cases from 34 provinces in Indonesia from March 2nd, 2020, to August 14th, 2021. The results of the evaluation using test data on the number of active cases in the last 19 weeks, namely April 10th - August 14th, 2021, The MLP is accurate in forecasting the number of new cases 18 times for both forecast periods with APE < 15%, with the value MAPE, RMSE and MAE obtained were 5.52%, 1157,61, and 706.811. The results of this study can be helpful for the government as a reference in conditioning hospital bed capacity to deal with active COVID-19 cases in West Java in the next two weeks so that the hospital rejects no COVID-19 patients because the hospital is full.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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