COVID-19

TG’s Comparative Risk Resource

While businesses find their new normal, our team at TG was interested in using publicly available resources to provide insight into how states and specifically counties are trending in terms of cases, impacts in terms of deaths, and specifically how business and community leaders can make informed, contextual, and data-driven decisions.  

We attempt to do this below with the use of a set of visualizations which enable the user to (i) understand the historical progression of the pandemic across the U.S., (ii) its relative current impacts – risk - at the state and county level, and (iii) the opportunity to simulate potential future outbreaks of the virus. 

Our visualization and simulation capabilities are meant to be easy to use and intuitive with drop-down links and clearly identified data labels.  They are designed to educate and inform users and have not been developed to replace official local, county, state of national sources of information – public or private. Links to our data sources, updated frequently, and additional resources can be found at the bottom of this page, please let us know your thoughts and ideas for improvement.

To view our dashboards, please use these login credentials:

https://tableau.teschglobal.com/#/home

Username: Guest

Password: Covid19-2020

Dynamic Timeline of Virus Spread

The dynamic national timeline visualization can be used to assess and understand the spread of the virus across the U.S.  In so doing it highlights the role of population density in our cities, and how transportation and commuting routes impact how to think about virus spread at lower levels of geography, such as county or the school district level.

This visualization can be “activated” at the bottom of the chart array, and displays the spread of the cases across the U.S., at the county level, geography from its start to the current period.  Additionally, it animates the discrete count of total national cases (bottom) over time, and a relative rank ordering of state impacts over the time period - right hand side of the chart array.

COVID 1.png

Discrete & Cumulative Case Trends by State/County

The discrete and cumulative trends by state / county visualization allows the user to understand how the virus has impacted a local county geography over time. By selecting the respective state and county dropdowns at the top of this chart array, the visualization will render cases and deaths over time for the geography selected.

Rank Ordering Relative County Risk

To offer a rudimentary understanding of county level risk, the rank ordering chart offers an understanding of how a particular county “stacks-up” relative to other counties in the state. Once the state is selected at the top of the chart array, all counties in that state are displayed in rank ordered fashion.  In assessing relative intra-state risk, the capability also offers the opportunity to prospectively “baseline” a target county geography against similar state/county geographies all across the U.S., to stimulate further insights and comparative understandings.      

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SEIR Modeling - Opportunity to Simulate Local Virus Outbreaks 

To round-out our suite of tools, we offer what is known by epidemiologists as a SEIR (Susceptible → Exposed → Infected → Removed) model for the simulation of infectious disease.  The dynamics of the model presented below are based on a set of differential equations corresponding to the virus progression in a geography.  The reader is directed to the following site for more details on the model’s operation (http://www.idmod.org/docs/general/model-seir.html). 

 In brief, this simulation characterizes a viruses progression, subdividing people into mild (people who recover w/out hospitalization), moderate (people requiring hospitalization but not terminal ) and fatal (people requiring hospitalization and are terminal). Each of these variables follows its own trajectory to the final outcome, and the sum of these population components add up to the values predicted by a SEIR model.  The dropdowns driving the model have been pre-populated are:

  • State (from State Selected Above)

  • County (from County Selected Above)

  • Total Population (from County Above)

  • Cases: Derived from State/County Population

  • Recovered: Derived from State/County Population

  • Deaths: Derived from State/County Population

  • Days:  (integer: 10 – 600 | default = 150)

  • Days to Recovery, i.e., Gama:  decimal: 2 – 14 | default = 5)

  • Rate of Reproduction, i.e., R0 – Beta: decimal: 1 – 10 | default = 2.9)

  • % of Normal Contact i.e., % of people contacts:  decimal: 0 – 1 | default > 0

  • % of Isolators, i.e., % social distancing: decimal: 0 – 1 | default = > 0

COVID 9.png

Data Sources

 Johns Hopkins – Center for Systems Science and Engineering (CSSE) Site (up until July 14, 2020)

Google Cloud Platform COVID-19 Open Data Set

GitHub Open COVID-19 Data Set

Additional Information

World Health Organization (WHO) 

https://covid19.who.int/?gclid=EAIaIQobChMI6_7G3qPo6QIVHz2tBh3yAQjxEAAYASAAEgK4q_D_BwE

Centers for Disease Control (CDC) Site

https://www.cdc.gov/library/researchguides/2019novelcoronavirus/websites.html

https://www.cdc.gov/library/researchguides/2019novelcoronavirus/datastatistics.html

Institute for Health Metrics and Evaluation (IHME)

https://covid19.healthdata.org/united-states-of-america

University of Maryland – National Impact Analysis Site

https://data.covid.umd.edu/

Blue Cross Blue Shield of America – Social Determinants of Health Site

https://www.bcbs.com/the-health-of-america/health-index/national-health-index

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