Mathematical Modeling and Sensitivity Analysis of COVID-19 and Tuberculosis Coinfection with Vaccination
Bibliographic record
Abstract
This research combines the COVID-19 and BCG vaccination subpopulations to examine the spread of COVID-19 coinfection and tuberculosis (TB) using a compartmental mathematical model.The model analysis yields the non-endemic and endemic equilibrium points in addition to the basic reproduction number.The vaccination variable in the model can reduce the incidence of COVID-19, TB, and coinfection.A sensitivity analysis using elasticity index is conducted and the result is that the natural death rate parameter is the most influential in relation to the accelerated spread of COVID-19 co-infection with tuberculosis.Additionally, we conduct a timedependent sensitivity analysis to determine how varying parameter values influence each subpopulation.By using this technique, we calculate the sensitivity index after reaching equilibrium of several groups of parameters, and the result is that resusceptible, immunity rate, symptomatic transition rate of TB, COVID-19 recovery rate, and natural death rate are the most influential for each group of parameters on the dynamics of each subpopulation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".