Web-Based COVID-19 Dashboards and Trackers in the United States: Survey Study
Notice bibliographique
Résumé
BACKGROUND: The SARS-CoV-2 pandemic provided an opportunity to use public-facing web data visualization tools to help citizens understand the evolving status of the outbreak. Given the heterogeneity of data sources, developers, tools, and designs used in this effort, it raised questions about how visualizations were constructed during a time when daily batches of data were available, but issues of data quality and standardization were unresolved. OBJECTIVE: This paper surveyed web-based COVID-19 dashboards and trackers that are likely to be used by the residents of the United States to monitor the spread of infection on a local, national, and global scale. This study is intended to provide insights that will help application developers increase the usefulness, transparency, and trustworthiness of dashboards and trackers for public health data in the future. METHODS: Websites of coronavirus dashboards and trackers were identified in August 2020 using the Google search engine. They were examined to determine the data sources used, types of data presented, types of data visualizations, characteristics of the visualizations, and issues with messy data. The websites were surveyed 3 more times for changes in design and data sources with the final survey conducted in June 2022. Themes were developed to highlight the issues concerning challenges in presenting COVID-19 data and techniques of effective visualization. RESULTS: In total, 111 websites were identified and examined (84 state focused, 11 nationwide, and 16 with global data), and this study found an additional 17 websites providing access to the state vaccination data. This study documents how data aggregators have played a central role in making data accessible to visualization developers. The designs of dashboards and tracker visualizations vary in type and quality, with some well-designed displays supporting the interpretation of the data and others obscuring the meaning of the data and potentially misleading the viewers. Five themes were identified to describe challenges in presenting COVID-19 data and techniques of effective visualization. CONCLUSIONS: This analysis reveals the extent to which dashboards and trackers informing the American public about the COVID-19 pandemic relied on an ad hoc pipeline of data sources and data aggregators. The dashboards and trackers identified in this survey offer an opportunity to compare different approaches for the display of similar data.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».