Implications of COVID-19 on Public Policy, Supply Chain Disruptions, and Monitoring Methods
Bibliographic record
Abstract
Transmission of the severe acute respiratory syndrome coronavirus 2, referred to as COVID-19, has persisted beyond 2020 and led to a global pandemic with far reaching consequences. Many changes in public policy and health measures were developed and implemented with the intention of slowing the spread of the novel virus. Disruptions from the global pandemic created major supply chain consequences due to stockpiling of essential goods (alcohol-based hand sanitizers and surface disinfectants), impacts on trade routes, and limitations on modes of transportation due to border closures. Rapid increase in the use of hand sanitizers and surface disinfectants significantly affected the production capacity of high-quality ethanol (e.g., USP and FCC grade) resulting in regulatory changes in countries facing shortages. Prompt enactment of government policies allowed for use of alcohol with higher impurities to offset heightened demand and increase commercial availability. Changes in monitoring methods were also observed, where many agencies began to track viral shedding through municipal wastewater. In this chapter, we will discuss the impacts of COVID-19 on public policies and health measures, economics as it relates to supply chain disruptions, and the implementation of novel monitoring methods to survey 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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".