Analysis of COVID-19 infections in GCC countries to identify the indicators correlating the number of cases and deaths
Bibliographic record
Abstract
Purpose The world has faced various epidemic situations caused by different viruses such as SARS-Cov, MERS-Cov, Ebola and many more during the past few decades, SARS-Cov-2 (COVID-19) is the genetic variant of newly the discovered Coronavirus, which has been believed to spread from China during December 2019, which has created a catastrophic effect for the whole world. In the first quarter of 2020, the virus started to spread to different countries, in addition, the severity of cases, the mortality rate and the recovery rate varied between countries. In the Sultanate of Oman and different parts of the world, the COVID started to spike during the end of March 2020. In this research paper, COVID data for Gulf Cooperation Council (GCC) countries are extracted and analysis has been made based on different parameters. The analysis has been divided into two categories – the first part focuses on the total number of cases, the total number of recoveries and the total number of deaths and comparison has been made for different GCC countries, from these analyses, it gives a clear picture of the days of a particular month, which contributes to the increase of COVID cases. The second part focuses on finding out the indicators that are correlating with the COIVD-19 cases and deaths; it has been found that there is a very strong correlation between the total population and labour force of every GCC country with the corresponding COVID cases and deaths. Design/methodology/approach The entire research steps involved starts with data collection, data pre-processing and data analysis. The analysis has been divided into two categories – the first part focuses on the total number of cases, the total number of recoveries and the total number of deaths and comparisons has been made for different GCC countries. The second part focuses on finding out the indicators that are correlating with COIVD-19 cases and deaths. Findings It has been found that there is a very strong correlation between the total population and labour force of every GCC country with the corresponding COVID cases and deaths. Research limitations/implications The data set considered is limited and can be extended further. Social implications This research paper definitely provides a road map for practice, as this research provides details about the total number of active cases, death based on the days in different GCC countries. It has been observed that during the end of each month and during weekends, the total number of cases increases drastically, so by taking into consideration the governing bodies can impose a lockdown during these spike durations. In addition to it, the citizens and residents should make a practice to avoid or limit their movement during the spike durations, which was analysed by this research work. Originality/value The idea is the own idea and not copied from any other source.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".