The Silent Epidemic: Global Threat of Antibiotic Resistance Bacteria - Carbapenem-Resistant Enterobacteriaceae (CRE)
Bibliographic record
Abstract
Mathematical modeling and optimisation using computer programs can efficiently predict the morbidity of a certain disease. Carbapenem-Resistant Enterobacteriaceae, or CRE, is a family of bacteria that are associated with difficult treatment, and, consequently, high mortality. This is a result of their resistance to all or almost all available antibiotics (“Biggest Threats”, 2016). We created a Python program to analyse the outbreaks of CRE in Canada and the USA, and whether it will become an epidemic under current conditions. We used the program, which uses a mathematical model, to compare and graph relative amounts of infected, susceptible, and dead patients. We started from a deterministic model and moved to a stochastic model. The deterministic model is the initial stage of our CRE model, which includes static rates taken from various data sources. The stochastic model is the second stage of our model, where there are dynamic rates according to other parameters, such as time or increases in infectivity rate. To determine whether CREs will become an epidemic, both the deterministic graph and the stochastic graph must meet particular criteria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".