RAPID: Development of an Interactive Web-based Dashboard to Track COVID-19 in Real-time

Engineering - This Rapid Response Research (RAPID) grant will be used to support the management and development of the COVID-19 online interactive dashboard hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The dashboard was first released publicly on January 22, 2020 to visualize and track the COVID-19 outbreak in real-time, and has since served as the prominent centralized source of COVID-19 epidemiological data throughout the COVID-19 outbreak to date. The dashboard illustrates the location and number of confirmed cases, deaths and recoveries for all affected countries, with additional features added over time. Further, all the data collected and displayed is made freely available to researchers, public health authorities and the general public in a GitHub repository, along with the feature layers of the dashboard. The availability of this data at the initial stages of the outbreak enabled the public health and research community to implement modeling tools, specifically the calibration of parameters for estimating transmission and spread at the earliest stages of the outbreak, when more formal situation reports were lagging, and thus gain a better understanding of the disease characteristics early on, which allowed for evidenced based decision making. Further, members of the public health community, including national and local level governmental organizations and public health offices around the world, continue to rely on the dashboard for informing, planning, and decision making regarding public safety, such as clinical staffing and resource allocation.

Given the popularity and impact of the dashboard to date, there is a need to continue to maintain it, and further build out its capabilities. Specifically, this includes the collection and curation process to capture data from a broader set of sources, enabling an increase in the spatial resolution (ideally to the city level) of the reporting. Introduction of additional features, including health-related context and confidence measures for the data and information presented, will increase the dashboard?s utility in supporting awareness and decision-making. Additional model and analytic research and development will enhance detection and classification, and more broadly, relative risk determination of events of concern from all sourced health related data. Longer term, systems engineering activities associated with this development will ensure an enduring capability established, vetted, and well-used prior to the next potential pandemic. Thus, the research efforts are critical to provide an early-warning system for future public health events, and help inform control measures in the current and future outbreaks.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.