Modelling the Spread of Covid-19 in a Typical Oil and Gas Facility Setting
Bibliographic record
Abstract
Abstract The spread of infectious diseases such as COVID-19 in a typical oil and gas facility setting, the health of employees, their families and their communities, is of real concern to the industry. In this work, a suitable predictive, agent-based model is used to predict the spread of COVID-19 in different settings as well as to evaluate strategies to block the spread. The agent-based modelling work is based on the simulation platform, Complex Organization and Bifurcation Within Environmental Bounds, or COBWEB. COBWEB simulates how a system of autonomous agents adapts to variation and sudden changes in the resource base or other features of their environment. Previous COBWEB simulation results illustrate that the tool is useful for predicting the evolution of COVID-19 spread and the effectiveness of various preventive actions including self-isolation of symptomatic people, social/physical distancing, effective PPE use, and ‘shielding’ (physical isolation) of the high-risk population. We adapted model parameters to better represent uncertainty about what might be expected in such a setting, in particular by shifting the distribution of risk severity towards ineffective PPE use, self-isolation and poor social distancing, which expectedly shows upward trend of the spread of the COVID-19. Ultimately, governments and industry can apply the predicted trends, as well as apply the model to specific settings, to make more informed decisions on the additional measures and preventative strategies to curb the spread of COVID-19.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".