Because it is possible that public data releases will be interrupted in the future, we recommend that the CDC immediately begin public releases of their entire COVID-19 data so academia can drive further innovation.Note: Case numbers are aggregated using a seven-day rolling average. We hope that by making the code used by this Web resource available to the public, developers will submit ideas for improvement. The Web application’s pipeline was developed to be extendable, and additional data sources will be added as they become available. In conclusion, we developed the COVID-19 Watcher to communicate up-to-date COVID-19 information to the medical community and general public. As of April 13, >40% of tests in New York came back positive, indicating that more testing is needed to understand the full scope of the outbreak. There is a clear correlation between the number of tests completed and the number of confirmed cases reported. States with the worst outbreaks, such as New York and Louisiana, also had the most tests per capita. There is high interstate variability in the volume of testing, timeliness of results, and disclosure of the number of negative test results. However, counts for confirmed cases and deaths are likely to be underestimates because testing is limited. Despite the differences in each source’s approach, case counts were relatively similar to one another, indicating that data sources appear to reliably report available data. Current practices for aggregating data generally involve combining government reported data with unofficial, but reputable, media releases from public officials. The procedures for reporting COVID-19 data need to be standardized. In particular, we would like to see community contributions related to geo-personalization of the website visualization, various analytics modeling, data points such as addition of countries, and timeline augmentation.Īlthough these datasets reviewed in Table 1 are the best that are available, they have major limitations. These modifications will be reviewed and, if judged to be suitable, merged into the main code. 15 Alternatively, contributors can make improvements to the code itself by forking the repository, modifying their copy of the code, and submitting pull requests back to the authors. The GitHub repository has a section for issue tracking where users can submit comments about the Web resource. The authors welcome community feedback, ideas for further development, and contributions. To make projections, these data should be used in conjunction with the University of Washington Institute for Health Metrics and Evaluation model, 13 the University of Pennsylvania’s COVID-19 Hospital Impact Model for Epidemics model, 14 or other susceptible-infected-recovered models. Users should take caution in using these data to forecast future events. The logarithmic scale shows the rate of spread, and flattening the exponential curve indicates the spread of the virus is slowing. Normalizing data by an area’s population shows the relative proportion of the population that have been infected. The data displayed by the COVID-19 Watcher can be used to evaluate the effectiveness of mitigation efforts. Visualizing COVID-19 data in real time through online dashboards is a pragmatic way to meet the medical community’s demand for up-to-date information. While these datasets are publicly available, they require informatics and data visualization to extract and display information because of their complexity and continual updates. In the absence of a uniform government standard for tracking COVID-19 outbreaks in the United States, academic and newsgroup-based data repositories have become the de facto standard. The purpose of this website was to make this information more accessible to the public, and to allow for more granular assessment of infection spread and impact. Our team developed a methodology to aggregate county-level COVID-19 data into metropolitan areas and display these data in an interactive dashboard that updates in real time. 1–4 However, it has become apparent that tracking outbreaks at the city level is critical, as the outbreak in China was centered within and surrounding the city of Wuhan, in Italy around Lombardy, in Spain around Madrid, and in the United Kingdom around London. Several online tools track COVID-19 outbreaks at the county, state, and national levels. 1 At this date, New York City was the epicenter of cases in the United States, but large outbreaks were present in several other major metropolitan areas, including New Orleans, Detroit, Chicago, and Boston. As of April 13, 2020, the United States had 30% of novel coronavirus disease 2019 (COVID-19) cases worldwide, the most of any country.
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