Can Digital Financial Behavior Improve the Effect of Prevention and Control of COVID-19 in China?
Notice bibliographique
Résumé
BACKGROUND: Before December 2019, white lung patients with symptoms similar to COVID-19 pneumonia were found in the United States and some European countries. After COVID-19 pneumonia was diagnosed in Wuhan, China, in December 2019, almost all countries or regions in the world have successively reported cases of COVID-19 pneumonia. 2020 is the most critical year for all mankind to fight against the new crown pneumonia epidemic. There are many prevention and control measures adopted by various countries or regions for the epidemic of new coronavirus pneumonia, and digital financial behavior (DFB) is an important evaluation index for effective prevention and control measures, which is of very Chinese characteristics. METHODS: DFB is defined by the Digital Financial Inclusion Payment Index, although there may be various versions of the understanding and definition of DFB. The data of the new crown pneumonia is calculated and accumulated through the real-time monitoring data published on the website of the health commissions of 31 provinces and municipalities directly under the central government every month, and is the first-hand raw data. Under the strict prevention and control measures adopted by China, these real-time data on new crown pneumonia released by various places are objective, true and comprehensive. The analysis methods adopted in this paper mainly include statistical analysis methods, econometric models such as logarithmic linear regression model, exponential model simulation method, etc. RESULTS: The diagnosis of COVID-19 pneumonia infection is a random variable, and there is a nonlinear random exponential relationship between it and DFB. The empirical study found that the least square estimation of COVID-19 infection diagnosis and DFB constitute a statistically significant exponential function relationship. This index model has successfully measured the effect of COVID-19 epidemic prevention and control in China with statistical significance, which indicates that DFB plays a positive role in improving the effect of COVID-19 epidemic prevention and control in China. The nonlinear exponential relationship between individuals with cumulative diagnosis of COVID-19 infection and DFB in 31 provinces and municipalities directly under the Central Government of China, excluding Hong Kong Special Administrative Region, Macao Special Administrative Region and Taiwan Province, has proved that this relationship is robust. Through regression analysis, it is found that the number of people infected with COVID-19 is significantly increased by one person for every additional unit of DFB. However, the similarity of DFB in 31 provinces and municipalities directly under the Central Government of China indicates that the number of COVID-19 infected individuals in 31 provinces and municipalities directly under the Central Government is increasing slowly. This result is very consistent with the distribution of actual statistical data, although the relevant data have certain regional differences. CONCLUSION: With extensive and in-depth practical basis and practical significance in all levels of Chinese society, DFB can measure the positive effect of the prevention and control of COVID-19 epidemic in China. Based on the positive role of DFB, there is every reason to believe that DFB will be one of the indispensable and trustworthy factors to improve its prevention and control performance in the face of similar social highly infectious diseases that may occur in the future. Whether the research method here has wider applicability, that is, whether it has a statistically significant positive effect on the prevention and control of epidemics in other countries or regions, should be a question that needs to be further explored in the future.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 source (Gemma direct ou Codex distillé), 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 ».