Health Security and Cybersecurity: Analysis of Interdependencies
Bibliographic record
Abstract
The development of medical digital platforms with data on patients, medical institutions and medicines, the growing use of IoT devices in medicine, the development of telemedicine accelerated by the COVID-19 pandemic, the growth of big data-based treatment technologies – all this makes it necessary to ensure reliable cyberdefence in the field of public health, maintain the confidentiality of financial information of clinics and health insurance companies, protect databases with patient records from hacking, and ensure secure communication. The article uses economic and mathematical modelling to study the relationship between two well-known international indexes and their components: The Global Health Security Index (GHSI) and the Global Cybersecurity Index (GCSI) for 190 countries in 2021. The input base included 6 cybersecurity subindexes and 7 health security subindexes for 2021. Based on the iterative divisive k-means method, all countries were grouped into 3 clusters. The first cluster includes 55 countries (medium levels of the studied indexes), the second – 49 (high levels), the third – 86 (low levels). The feasibility of dividing into clusters and choosing their optimal number is substantiated by means of variance analysis. Via the methods of Sigma-restricted parameterisation, Univariate Tests of Significance, Pareto Chart of t-Values and correlation analysis, all factors, without exception, proved to be relevant. Due to the OLS method, multiple linear regressions describing the relationships between various components of these indexes and their integral values are generated. The statistical significance of the factors included in the model is confirmed, and the model itself is tested for adequacy and accuracy. On the basis of correlation analysis, existence and magnitude of statistical relationship between the components of these indexes are revealed. The strongest correlations are observed between the integral values of these indexes, as well as between pairs of subindexes: 1) Legal Measures (GHSI component) and Prevention (GCSI component); 2) Technical Measures (GHSI component) and Detection and Reporting (GCSI component). To identify the causal relationship between the groups of factors, a canonical analysis was carried out, which showed the GHSI parameters are causal and the GCSI parameters are resultant. The linear regression model showed a significant positive relationship between the GHSI and GCSI indexes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".