Population Forecasting with Alzheimer's Disease in Iran Using a System Dynamic Model
Bibliographic record
Abstract
Background: The prevalence of Alzheimer's disease in Iran, attributed to the demographic shift towards an aging population, holds considerable importance. We aimed to estimate the prevalence and number of Alzheimer's disease in Iran by 2029. Methods: Dynamic modeling techniques were employed to project the number of Alzheimer's disease (AD) among the elderly population in Iran by the year 2029. Two interconnected models were developed to facilitate this estimation. The initial model is a demographic model that captures the aging population's growth dynamics. The subsequent model, an AD evaluation model, that assess potential impacts on disease. This approach enables a comprehensive analysis of the factors influencing AD trends within the context of Iran's aging demographic. Results: The results show the number of individuals aged over 60 is expected to rise from approximately 9.1 million in 2020 to around 13.7 million in 2029. As the older adult population grows, the number of AD is also anticipated to increase. The number of Alzheimer's patients is predicted to grow from about 464,400 in 2020 to roughly 729,900 by 2029. Conclusion: Forecasting future trends in AD, especially in developing countries, is crucial for policymakers because of its growing impact on healthcare systems and economies globally. The findings of this study can aid in assessing the economic burdens associated with treating Alzheimer's patients, providing valuable insights for planning and resource allocation.
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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 | 0.002 |
| 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 | 0.000 |
| 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".