Web-Based COVID-19 Dashboards and Trackers in the United States: Survey Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".