INFODEMIOLOGY OF RUSSO-UKRAINIAN WAR: USING GOOGLE TRENDS TO STUDY THE GLOBAL SPREAD OF UKRAINIAN REFUGEES WITH ALLERGIC DISEASES
Bibliographic record
Abstract
The aim of this study was to assess the global distribution dynamics of Ukrainian refugees following the full-scale Russian invasion by analyzing Cyrillic-language allergy-related search queries on Google, using Google Trends (GT) as the primary tool. Materials and methods. Google Trends was used to identify allergy-related queries made in Cyrillic script, which reflect the online behavior of Ukrainian refugees. The study analyzed the dynamics of these queries in Poland, Germany, the United Kingdom, Italy, Spain, the Czech Republic, the United States, and Canada. Area-under-the-curve (AUC) comparisons were used to assess search frequency, and a mixed-effects model was applied to examine changes before and after the invasion. Correlation analyses were conducted between Google Trends data and official statistics on Ukrainian border crossings from January 5, 2020, to October 22, 2023. Data were processed using Microsoft Excel and GraphPad Prism 8.0. Results. It was found that the number of Google search queries for “Allergy”, “Runny nose”, “Dermatitis”, “Asthma”, “Ambrosia”, and “Cough” significantly varied during the study period in Poland; for the queries “Allergy”, “Runny nose”, “Urticaria”, “Dermatitis”, “Asthma”, and “Cough” – in Germany; “Asthma”, “Ambrosia” – in the United Kingdom; “Urticaria”, “Dermatitis”, “Cough” – in Spain; “Dermatitis”, “Cough” – in Italy; “Urticaria”, “Dermatitis”, “Cough” – in Canada; “Runny nose”, “Dermatitis”, “Ambrosia”, “Cough” – in the USA; “Allergy”, “Runny nose”, “Cough” – in the Czech Republic. In Spain, a strong direct correlation was established between the number of registered Ukrainian citizens and the following Google queries: “Dermatitis” (r=0.81; p<0.0001), “Asthma” (r=0.85; p<0.0001), “Cough” (r=0.85; p<0.0001), “Allergy” (r=0.86; p<0.0001), “Runny nose” (r=0.95; p<0.0001), “Urticaria” (r=0.91; p<0.0001). In Italy, a moderate direct correlation was found between the number of registered Ukrainian citizens and the following Google queries: “Dermatitis” (r=0.75; p=0.0002), “Cough” (r=0.79; p<0.0001), and a strong direct correlation with the queries “Asthma” (r=0.88; p<0.0001), “Allergy” (r=0.81; p<0.0001), “Runny nose” (r=0.91; p<0.0001), “Urticaria” (r=0.81; p<0.0001). Conclusions. The study confirms the annual increase in the number of Ukrainian refugees after the full-scale invasion in both Eastern and Western Europe, particularly in destination countries – Italy and Spain – as well as in transit countries – the Czech Republic, Poland, and Germany. These Ukrainians face the medical and social consequences of the Russo-Ukrainian war, and a significant portion of the refugees suffer from allergic diseases, thus requiring high-quality and accessible medical care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".