Impact assessment of self-medication on COVID-19 prevalence in Gauteng, South Africa, using an age-structured disease transmission modelling framework
Bibliographic record
Abstract
Abstract Introduction: Self-medication, as a global phenomenon, remains a pressing issue that requires attention worldwide, in particular, the Global South. It has been a hindrance to infectious disease control as it, in most cases dampens interventions put forth by health authorities. It is imperative, therefore, that disease dynamics relating to self-medication are incorporated into mathematical models used to inform infectious disease-related public health interventions and policy. COVID-19-associated self-medication is well documented, and its implications for disease prevention and control are alarming. We investigated the impact of self-medication across different age groups on the transmission dynamics of the disease; the interplay of vaccination and self-medication on the spread of the disease; and the age group with the most tendency for self-medication. We used Gauteng Province, South Africa, as a case study. Methods: We employed ordinary differential equations (ODEs), formulated within an age-structured compartmental disease modelling framework. Model parameters were estimated using a Markov Chain Monte Carlo (MCMC) estimation scheme. Uncertainty and sensitivity analysis were carried out on model parameters. Results: The model implied estimates indicates that self-medication is predominant among Age group 15-64 (83.13%), followed by Age group 65+ (44.17%). Age group 0-14 records 33.82%. The mean value of the basic reproduction number, first epidemic peak, and first epidemic peak time are 3.19838, 821536, and 214.988, respectively. Conclusion: Self-medicationplays a crucial role in combating COVID-19, and that regardless of the levelof effectiveness of instituted vaccination programs, it must be put in check. Appropriate campaign against COVID-19 related self-medication is justified. It is also worth noting that campaigns should target the active population (ages 14-64)
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".