Mathematical modeling and analysis of export trends for certain pharmaceutical groups
Bibliographic record
Abstract
Background. The analysis of the exports of medicines of the Russian Federation (RF) by group 30 (pharmaceutical products) of the commodity nomenclature of foreign economic activity of the Eurasian Economic Union (equal to Harmonized System Code 30, HS 30) is an important task for determining the economic potential of the country in the pharmaceutical industry. Objective: building an export regression model. Development of an alternative mathematical model of pharmaceutical products’ export, suitable for making the forecast. Material and methods. Statistical data on HS 30 exports from 2010 to 2021 were taken from an open source: The International Trade Center (Trade Map). Data for 2022 and later for the Russian Federation are not available in open sources. Registration on the website is not required to collect statistics on export and import volumes based on annual data. The well-known statistical methods and methods of mathematical modeling were used. An alternative approach to regression analysis was developed. Technical data analysis was performed using MAPLE (Watcom Products Inc., Canada) and R (Bell Laboratories, USA) software. Results. Two models were constructed: Model I – a differential model based on cumulative data by year, and Model II – a model of standard regression analysis, the input parameters of which were quarterly export data, and the influencing parameters were a certain group of factors. Model I allowed considering the dynamics of changes in pharmaceutical exports over time (dynamic factors and nonlinear interactions). Model II, in turn, made it possible to determine the dependence of the volume of pharmaceutical exports on various economic indicators, such as gross domestic product, the volume of government procurement, and measures of protectionism. The relative error of Model I does not exceed 10%, which makes it suitable for forecasting. Conclusion. The construction and analysis of specified models help to provide main information about the trends in pharmaceutical product exports in the RF and assess its potential in the global market. The obtained results can be useful for developing strategies of the pharmaceutical industry development, making management decisions and forecasting future exports.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 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) | 0.005 | 0.001 |
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".