COVID-19 Case Count Trajectory Starter Dashboard | Starschema

COVID-19 Case Count Trajectory Starter Dashboard

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Everyone wants to know when America could once again open up for business. To better understand factors influencing this decision, The White House Coronavirus Task Force presented a data-driven approach to state-by-state relaxation of public health measures. This includes downwards trending trajectories of influenza-like illnesses, COVID-19-like syndromic cases, diagnosed cases, and declining positive test percentage in the presence of a robust testing program for at-risk workers and vulnerable populations.

To give citizens, enterprises, and NGOs a view of progress towards meeting the quantitative gating criteria in every state, we created the Case Trajectory Status Visualization. This is a publicly available interactive data visualization that indicates the trajectory of cases as well as the trajectory of positive cases as a percent of total cases.

This is also available as a free-of-charge starter dashboard that can be integrated with your company's own data sets for deeper more company-specific analysis.

This dashboard uses up-to-date information from the Starschema COVID-19 data set, a public free-of-charge resource available on the Snowflake Data Marketplace. We will continue to integrate additional functionality in this dashboard and introduce complimentary dashboards, with the intent for it to be a source of situational awareness to help users assess how close states of their concern are to meeting the criteria for re-opening. This glimpse into when a state may be eligible for re-opening under the federal guidelines can inform decision-making on a wide range of issues — from managing supply chains to determining when employees may be ready to go back to work.

Ask the Expert

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Kristof Csefalvay

Kristof Csefalvay is Starschema's VP for Special Projects, having previously served as Principal Data Scientist at Starschema. As a data scientist with over 10 years' experience, he has pioneered AI approaches in epidemiology, earth observation, and digital signal processing. Educated at Oxford and Cardiff, he has worked in data science roles for companies across Europe and the Americas and holds a number of patents in the field of machine learning, AI, and DSP.

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