Clustering Pandemic COVID-19 and Relationship to Temperature and Relative Humidity Among the Tropic and Subtropic Region
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».