Exploring geographical differences and disparities of COVID-19 cases and understand the gaps in responses in South Asian countries: A three-month analysis of cases and responses
Notice bibliographique
Résumé
Background and Aim: Response in a beginning of an infection is important to prevent and control any infectious disease. It has never been studied how the countries in South Asia responded in the beginning of COVID-19 infections. The aim of this study was to explore the gap in responses by geographical variations and inequalities of COVID-19 cases in South Asian Countries.Methods: Covid-19 cases, geographic and demographic data for South-Asian countries were abstracted from the news medias, Johns Hopkins University dashboard, and countries government websites. The coverage period was until May 7, 2020. Descriptive analyses of COVID-19 cases were stratified by gender and age group. Clustering and spatial analysis was performed to show the COVID-19 case distribution.Results: Over 100000 confirmed cases were found in South-Asian countries until May 7, 2020, and 95% of them are in India, Pakistan, and Bangladesh. Alarmingly, a sharp increase in new cases was observed in Bangladesh and India in early May. In this region, India reported 56% of total cases, with the highest case fatality rate of 3.4%. Approximately 70% of infected cases in this region were found in men. Approximately 42% of confirmed cases were found between the ages of 20-40, and about 20% of infected cases were found over 50 years or older. All big, economically important cities in this region were mainly infected. Bangladesh and Afghanistan reported a slow rate of recovery with 16% and 13%, respectively while India reported 29%. Afghanistan used only four tests to detect a case while India used 25 tests to detect a case showing poor numbers and insufficient test facilities in Afghanistan. Conclusion: The biggest and most economically-important cities in every South-Asian country were infected with COVID-19, where returning the migrant workers to work was a significant challenge after lifting the restrictions. Data from India, Pakistan, and Bangladesh suggest that these countries did not show the peak in the first six months. In South Asia, men were at higher risk for both infection and death, regardless of age. There were many underreported cases in these regions. Scale up services to improve the testing facilities and start a surveillance system to identify the cases rapidly especially from the marginalized population and women could reduce the burden of any infections.
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 | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».