Clustering Pandemic COVID-19 and Relationship to Temperature and Relative Humidity Among the Tropic and Subtropic Region
Bibliographic record
Abstract
The outbreak of Novel Corona Virus (COVID-19) has been spreading almost in all countries of the world and become a deadly pandemic. The infections and deaths vary from high in some countries and low in others. The weather conditions significantly affect life, including viruses. In low temperature and humidity the spreading of coronavirus is expected to be fast and massive, and on the other hand, high temperature and humidity decreases the virus. However, recent data of COVID-19 shows that in tropical region infection and deaths vary of which there is a need of thorough spreading analysis. The clustering of infections and mortality at the beginning of COVID-19 outbreak was group based on the country’s profile similarity, and associated with the meteorological factors. The result shows that countries such as China, Spain, Italy and the United States have very severe attacks of COVID-19 infection. Furthermore, countries with the potential real threats of COVID-19 infections are Austria, Australia, Azerbaijan, Belgium, Bahrain, Brazil, Belarus, Canada, Switzerland, Czech Germany, Denmark, Dominican Republic, Algeria, Ecuador, Estonia, Egypt, Finland, France, Georgia, Croatia, Indonesia, Ireland, Israel, India, Iraq, Iran, Japan, Cambodia, South Korea, Kuwait, Lebanon, Sri Lanka, Lithuania, Monaco, Macedonia, Mexico, Malaysia, Nigeria, Netherlands, Norway, Nepal, New Zealand , Oman, Philippines, Pakistan, Qatar, Romania, Russia, Sweden, Singapore and Thailand. The threat of COVID-19 is not only in dry and humid sub-tropical countries, but it cannot be undermined the effect to some warm and humid tropical countries such as Brazil, Ecuador, Indonesia, Malaysia and the Philippines, which are massively infected, and the mortality rate compared to the population are very high. The study also found that dynamic humidity is a factor that must be considered, especially in the tropics. HIGHLIGHTS The COVID-19 pandemic that originated in Wuhan, China spreads rapidly around the world Demographics and weather are thought to influence the spreading and death of COVID-19 Clustering of demographic and weather factors on COVID-19 shows that countries such as China, Spain, Italy, and the United States are experiencing severe attacks of COVID-19 infection Covid threatens countries with high population density or large populations Although warm and humid temperatures in the tropics such as Brazil, Ecuador, Indonesia, Malaysia, and the Philippines can a little slow the spreading of infection, the risk of COVID-19 infection remains high GRAPHICAL ABSTRACT
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".