EXTRAS
Compare change in various metrics during any time window
% Change in Cases vs. Testing During Previous 2 Weeks
Risk of encountering a person with COVID-19 at an event, based on current community spread
Date of Case Confirmation vs. Date of Symptom Onset
Comparing Epidemic Curves #1 (date of case confirmation versus date cases were reported)
Comparing Epidemic Curves #2 (date of case confirmation versus date of symptom onset)
Comparing Cumulative Epidemic Curves (date of case confirmation versus date of symptom onset)
At the bottom right of each visualization is a "Full Screen" iconThis is the best option for taking a screen shot of a visualization1. Compare change in various metrics during any time window
Uses 7-day rolling averages and calculates the % change between the two dates
2. % Change in Cases vs. Testing During Previous 2 Weeks
Each day compares 7-day rolling averages between date and 2 week prior
C = cases, T = total people tested
3. Risk of encountering a person with COVID-19 at an event,
based on current community spread
1. Type in how many people are estimated to be at the event (from 10 to 100,000).
2. Select a time frame for what constitutes 'active infections' (past 7, 10, or 14 days)
3. Select an ascertainment adjustment factor (from 2 to 10)
(a value of 5 would assume that for every detected cases, we are missing 4 that have gone undetected)
4. The "risk" pertains to the probability that 1 or more people with COVID-19 will be at the event.
Please note: THIS DOES NOT REPRESENT A PERSON'S RISK OF TRANSMISSION.
That must take into account characteristics of people at the meeting (e.g., age, job, socioeconomic standing, mitigation behaviors), and of the meeting itself (e.g., outdoors, masking, distancing, ventilation). This is meant to be used as a loose gauge for planning purposes and to translate daily cases and rates into a more interpretable metric. Because zip code-level data are not reliable in Florida, and socioeconomic indicators of cases are not provided at the case level, the tool must assume cases are evenly distributed throughout a county, which they are most often not.
4. Date of Case Confirmation vs. Date of Symptom Onset
Understanding the difference between the two
5. Comparing Epidemic Curves #1
Based on date of case confirmation versus date cases were reported
6. Comparing Epidemic Curves #2
Based on date of case confirmation versus date of symptom onset
7. Comparing Cumulative Epidemic Curves
Based on date of case confirmation versus date of symptom onset
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