Mathematical modelling of the impact of vaccination, treatment and media awareness on the hepatitis B epidemic in Burkina Faso
Bibliographic record
Abstract
Infection with the hepatitis B virus (HBV) remains a global public health issue. Particularly in Burkina Faso, HBV is a major public health concern due to its high prevalence and associated mortality. However, universal vaccination, treatment of chronic carriers, and awareness campaigns are currently employed means in Burkina Faso to combat the spread of HBV. Therefore, this paper aims to study the impact of these control measures on the expansion of this virus. This paper presents a mathematical model of vertically transmitted HBV that takes into account the progression to chronicity as a function of the age of the infected person, as well as vaccination, treatment of chronic carriers, and media awareness. After formulating the model and carrying out the mathematical analysis, we simulated the proposed model in Matlab, taking into account the various involved parameters. Finally, we presented the results of sensitivity analysis and numerical simulation. According to our model, with vaccination coverage of $30\%$, a $50\%$ success rate of awareness campaigns and $20\%$ effectiveness for the $10\%$ of treated chronic carriers, the prevalence of hepatitis B infection could decrease down to $2\%$ within thirty years in Burkina Faso.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".