Weekly average daily cases per 100k Last week | This Week |
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| June 29th, California | | | |
| June 28th, San Diego Weekly average daily cases per 100k | July 5th, San Diego Weekly average daily cases per 100k | |
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June 29th, US Mainland
Weekly average daily cases per 100k | July 6th, US Mainland
Weekly average daily cases per 100k |
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Weekly average daily cases per 100k, California
June 26th | Weekly average daily cases per 100k, California
July 2nd
| Weekly average daily cases per 100k, California July 9th | | |
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Quarantine Pilot Covid 19 Dashboard
Supported Annotations
We provide the following annotations in the current deployed version.
Spread rate
- Spread rate = (Log[Confirmed cases at time t] - Log[Confirmed cases at time t-delta]) / delta
Daily Growth rate
This metric provides an indicator for the inflection point. A value switch from greater than 1.0 to less than 1.0 and staying below 1.0 indicates that we are close to an inflection point.
- Daily growth rate = [New confirmed cases at day D / New confirmed cases at day [D-d] ](1/d)
Fatality rate
Since there is a lag between the confirmed cases and the resolution of those confirmed cases by several weeks. We provide a crude measure for now
- Confirmed fatality rate = Cumulative deaths at time t / Cumulative confirmed cases at time t
Daily Cases per 100k
We are using a measure provided by ESRI for epidemic control assessment. An epidemic is considered controlled when there is maximum 0.5 new case per 100,000 people for a period of 21 days.
Credits
Date Sources
Development kit
The analytics portion of this work has been developed in Scala.
The UI is driven by the Angular framework.
The charts are provided using non-profit license given graciously by AnyChart.
We are using Netlify for CICD.
There are a few countries and counties missing due to data sanitizing issues that would require significant effort to include - so we have settled with 95% of the locations from the data sources. All work shared here is for educational and informational purpose and users should use their own judgment in using the metrics and trends from this dashboard for any COVID-19 related social engagement strategies.
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