A Deep Learning LSTM Approach to Predict COVD-19 Deaths in North Africa
Bibliographic record
Abstract
The countries of Tunisia, Algeria, and Morocco are part of the North Africa region, also called the Maghreb. As of October 16, 2022, there are 52413 COVID-19 related deaths reported for these countries despite the significant progress in vaccination.1 A notable interest by scholars has emerged recently to model and to forecast the spread and the lethality of the pandemic and its impact in the Middle East and North Africa region. In a recent study, spatial panel-data models were used to identify the factors for the spike of COVID-19 infections in North Africa.2 In another study, a statistical analysis was performed based on zero-inflation models and autoregressive conditional count models to forecast death counts with evidence from Tunisian data.3 Furthermore, quantitative analyses including statistical modeling and deep learning methods have also been performed to forecast the pandemic outbreak in different parts of the world.4 For instance, some authors presented long short-term memory (LSTM) based models to predict novel infections of the coronavirus in India, whereas in other studies deep learning methods were used to forecast new COVID-19 cases and death rates in Australia and Iran.5 Using COVID-19 datasets of several countries including Brazil, Germany, Italy, Spain, United Kingdom, China, India, Israel, Russia, and United States, alternative deep learning methods were studied and their results were compared in terms of forecast performance.6 In this study, we contribute to this ongoing literature, by conducting a statistical analysis with publicly available data on the coronavirus death counts for the Maghreb countries, and we show that the method of deep learning with LSTM network outperforms time series autoregressive integrated moving average (ARIMA) models in terms of forecast accuracy for the pandemic deaths.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".