Environmental Analysis of the COVID-19 pandemic in Saudi Arabia: A Study in Medical Geography
Bibliographic record
Abstract
This study is one of the micro studies that is concerned with analyzing the geographical environment of a global epidemic within a specific geographical scope, in which the rates of infection with the Covid19 pandemic in the Saudi Arabia were compared with other local, regional and global regions to determine the extent of its spread and speed of arrival, and the distribution of morbidity, recovery and fatality rates was also linked.The study relied on several approaches, including the inductive approach, disease Ecology and Disease Diffusion approach, as well as statistical and quantitative analysis using the stepwise regression method, and the cartographic representation method, and the study reached a set of results, the most important of which are The success of the Saudi Arabia at the international and regional levels in dealing with the epidemic, as it was able to reduce the growth of infection cases, and fell from the forefront of the Arab countries in the number of infected cases to the third, and from the thirteenth to the thirty-fifth globally, and began to decline further after the application of vaccines For all those within its borders, it was also able to maintain average infection rates of 1041.9 for every 100 thousand people at the end of 2020, which is slightly less than the global average of about 1069 per 100 thousand people, and low death rates did not exceed 1.7%, while the global rate was 2.2%, and therefore the growth of recovery rates increased to reach 97.5% in light of Continuous prevention and examination procedures and treatment services.At the level of the Saudi's regions, regional discrepancies were found clearly, as morbidity and recovery rates increased in the east and southwest of the state and extended to the West, where the Eastern Province, Madinah and Asir recorded the highest morbidity rates, and recovery rates exceeded 98%, while morbidity rates decreased in the north and center, especially in the Al-Jawf region, Tabuk and the northern borders, and the death rates increased in the northern part, where the Al-Jawf region and the northern borders recorded rates ranging from 3.5 -4.2%.For reasons related to movement, density, and degree of crowding.When analyzing the environmental impact of some health and social factors affecting the rates of morbidity and fatality in the pandemic in the regions of the Saudi Arabia using the method of stepwise regression analysis, the association of infection with some infectious diseases such as amoebic dysentery as an indicator of disease infection came in the forefront of the independent variables and the relationship was significant, then followed by a variable availability of the health service represented in the availability of isolation beds, and the availability of chest physicians came at the forefront of the independent variables included in the regression analysis of the fatality rate variable, followed by the variable population density, Then the nursing / physician.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".