We just noticed that it has been a full year since KDD Analytics started posting daily updates to the San Diego County COVID-19 dashboard.

This dashboard tracks the San Diego COVID experience: new cases, tests, and positivity rates at the county-level as well as new cases for each of the county’s ZIP Codes.

On this first-year anniversary of these daily postings, we thought we would look back at this roller coaster year.

And, although our dashboard does not include US data, we thought that comparing the San Diego COVID experience with the national average would be insightful.

San Diego COVID experience vs the nation

The following figure shows the 7-day moving average of daily new cases per 100,000, for both San Diego County and the entire US.[1]

 

San Diego COVI-19

 

As shown in the above figure, as a nation we have been through 3 waves with it being too soon to tell if the 4th wave has crested. San Diego County’s experience was generally similar except for the 4th wave.

 

Wave #1

The initial rise in daily new cases crested at a 7-day average of 10 per 100,000 for the US on April 12, 2020. San Diego’s first wave crested about a week earlier on April 4th at about 4 per 100,000.

The US new case 7-day average fell to 6 per 100,000 by mid-June. San Diego’s briefly fell a bit but then rose back up to a daily rate of 3 to 4 per 100,000 till mid-June.

So, San Diego did not really experience the same recovery from the first wave as the US.

 

Wave #2

For both the US and San Diego, the second, much larger wave began in mid-June 2020. The US new case rate increased from a 7-day average of about 6 per 100,000 to a peak of 21 per 100,000 on July 23rd. San Diego’s rate increased from about 4 per 100,000 to a peak of 16 per 100,000 on July 2nd.

Both the US and San Diego new case rates declined through the end of the summer. The US new case rate bottomed at a 7-day average of 13 per 100,000 on September 13th. San Diego’s bottomed at the end of August at about 8 per 100,000 and stayed essentially flat till mid-October.

 

Wave #3

The US third wave began in mid-September – a full month before San Diego was hit. Rising from a 7-day average low of about 11 per 100,000, the US new case rate increased throughout the fall and early winter, peaking at 76 cases per 100,000 on January 11, 2021.

San Diego’s third wave began in mid-October. From a 7-day average low of about 8 new cases per 100,000 on October 20, the new case rate peaked at a high of 109 per 100,000 on the same day that this third wave peaked for the country.

As the figure shows, San Diego (as well as Los Angeles) suffered much higher new case rates than the national average.

But daily new cases started to decline just as steeply as they increased. San Diego’s 7-day average new case rate fell from this high of 109 to around 7 per 100,000 by April 20, 2021.

The US new case rate fell as well, from a 7-day average of 76 to about 16 by March 19, 2021.

 

Wave #4

Until this point, San Diego’s experience, though different in severity, matched the general pattern of the country. However, a 4th US wave began in mid-March 2021, driven by new outbreaks in Michigan and New Jersey. It is too soon to tell if this 4th wave has crested but the most recent peak is a 7-day average of 21 new cases per 100,000 on April 13th.

San Diego has been fortunate to escape this 4th wave (so far).

Fingers crossed…

 

[1] San Diego new case data are from the San Diego County Health Department. US new case data are from the CDC.  The 7-day moving average is the average of the current and preceding 6 days. 2019 population is used to normalize case counts so we can compare San Diego with the nation.

Kevin Duffy-Deno

Written by Kevin Duffy-Deno

Dr. Kevin Duffy-Deno, with over 30 years of experience in quantitative research and analysis, is an expert in econometric, data, statistical and predictive modeling and data visualization. Both with Bintel and prior organizations, Dr. Duffy-Deno has been engaged in numerous projects requiring data acquisition from proprietary and public sources, the normalization of such data and the integration of data into a format supporting analytic analyses and products.