Modelling the impact of non-pharmaceutical interventions on COVID-19 exposure in closed-environments using agent-based modelling
Bibliographic record
Abstract
Businesses can play a key role in reducing exposure to COVID-19 in closed environments. This is possible by assessing the impact of Non-Pharmaceutical Interventions (NPIs) in mitigating disease exposure. This study aims to assess the impact of NPIs on COVID-19 exposure in closed environments. This is achieved by proposing an innovative COVID-19 exposure prediction framework. The developed framework consists of three modules: Agent-Based Modelling (ABM) approach, Clustering Module (CM), and Decision Tree (DT) technique. The framework also integrates these modules considering the exposure time factor to identify the level of exposure to COVID-19 in closed environments. A supermarket based in Jordan is considered a case study to test the applicability of the proposed framework in predicting exposure levels and numbers. The impact of Individual and combined NPIs application in closed environment facilities is assessed based on the exposure level and other OIs such as opening time, body temperature measurement, and the number of people inside the supermarket. Key results show that wearing Mask, Face Shield and leaving Social Distance guarantees no exposure to COVID-19 and increases the safety level to 61.9% in a closed environment such as supermarkets with a potential exposure rate of up to 28.5% if otherwise.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".