Designing Evidence-based Decision Support Systems for Pandemic Management Considering both Health and Economic Situation
Bibliographic record
Abstract
During the first year of the Covid-19 pandemic, countries had no choice except Non-Pharmaceutical Interventions (NPIs) to control the spread of this infectious disease. Some of these interventions, like the stay-at-home order, were effective in controlling disease spread (Navazi et al., 2022); however, they caused adverse effects on the economic situation. So, we need a decision support system that can find effective NPIs in controlling the pandemic with low negative economic effects. This study aims to develop a multi-objective decision support system that can consider both. Since each pandemic has unique features, finding historical data to help decision-making is difficult, so the developed model should be able to consider existing evidence and best practices from other countries for providing suggestions (Navazi et al., 2024a). Since the effectiveness of NPIs is affected by society's adherence to NPIs, an indicator was added to the model to capture adherence with NPIs (Navazi et al., 2024b). The model should also consider pharmaceutical interventions after vaccine/cure development, because NPIs are still effective during the period it takes to reach herd immunity (Navazi et al., 2022). So, in this research, we used machine learning algorithms for learning from time series data gathered by Oxford University about NPI implementation levels. Moreover, a metaheuristic algorithm, an AI tool for optimization, is used to suggest the best level of NPI for the studied country based on evidence from other countries. The designed information system artifact is able to analyze time series data to discover knowledge about best practices and lessons learned for possible future pandemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".