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Data tool can determine COVID-19 Risk for Nebraskans going to Thanksgiving gatherings

(WLUC/CDC)
Published: Nov. 16, 2020 at 8:04 PM CST
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LINCOLN, Neb. (KOLN) - Many people in Lincoln and all throughout Nebraska are trying to figure out how to celebrate Thanksgiving this year. Around 100,000 cases of COVID-19 have been reported in Nebraska and 13,000 cases in Lancaster County. Many are debating whether a family gathering is worth the risk.

Georgia Tech has provided an easy tool to assess the COVID-19 Thanksgiving risk.

Data scientists at Georgia Tech have developed a tool using the number of COVID-19 cases over the past 10 days in each county to allow people to assess how risky a person’s plans may be. Physicians at Nebraska Medicine, including Dustin Krutsinger, advise people take a look at to determine their risk.

For an example, let’s say 15 individuals go to a Thanksgiving dinner.

If everyone was from Lancaster County, the percent chance at least one person at the table has an active COVID-19 infection would be 41%. If everyone was from Scotts Bluff County, 86%, and if everyone was from Douglas County, 50%.

All gathered around the table, there is a 41% to 86% chance that one person would have an active COVID-19 infection.

According to Krutsinger, 50% of adult Americans have one or more risk factors which put them at a greater risk when contracting COVID-19. Factors associated with severe disease include:

  • Diabetes
  • Heart disease
  • Kidney disease
  • Chronic obstructive pulmonary disease (COPD)
  • History of smoking
  • Asthma
  • High blood pressure
  • Cancer

According to the Georgia Tech website, based on data and increases in testing, by default they assume there are five times more cases than are being reported (5:1 ascertainment bias). In places with less testing availability, that bias may be higher. Georgia Tech is evaluating the inclusion of lower ascertainment biases based on increased testing.

Individuals can check out the information used in calculating the data, as well as adjusting the ascertainment bias of the data on the website.

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