What Does Bay Area Doubling Rate Depend on?
See how much your county has been able to flatten its curve.
Click through the plots above to see how your county has been able to flatten the curve. The trendlines are projected forward from the most recent 9 days of COVID-19 case counts. I’ve also used the trend line to calculate the number of days to double for each county and sorted them from worst to best. I then started to ask the question why is there the disparity in the epidemic’s growth rate. For example Contra Costa county is doubling at twice the rate of Marin county. (For a discussion of Napa county outlier see the very bottom of this post.) By the way all of these numbers are much better than the 4 days to double for SF and the 2 days to double for the US that I was seeing in March.
| Region | Days to Double (sorted) | Number of Tests per 1000 | Median per Capita Income |
|---|---|---|---|
| United States | 6.06 | - | $31,099 |
| California | 6.18 | - | $32,482 |
| Bay Area | 8.74 | - | help me find |
| Contra Costa | 6.29 | 5.22 | $38,770 |
| Alameda | 7.08 | - | $36,439 |
| Solano | 7.29 | - | $29,132 |
| San Mateo | 7.36 | - | $47,198 |
| San Francisco | 10.31 | 6.80 | $49,986 |
| Sonoma | 10.39 | 5.65 | $33,361 |
| Santa Clara | 10.65 | 6.11 | $42,666 |
| Marin | 11.91 | 6.88 | $58,004 |
| Napa | 10.91 | 2.69 | $35,092 |
First Look for Testing Bias: There Probably isn’t any.
The first thing I wanted to check on was to make sure that no county had a high rate of increase because because of a high rate of testing. A high rate of testing would catch more cases and cause the curve to rise but not be indicative of an actual growth of the epidemic. The rate of testing is remarkably consistent across those counties that report the amount of testing being done. There are about 5.5 to 7 tests being done for every 1000 residents. It is interesting to note that all the counties that are not reporting the amount of testing being done have higher rates of increase.
Wealth Generally Seems to Allow for Better Social Distancing
When you compare the days to double rate for a county with that county’s median income, there seems to be a rough trend. The wealthier the county the slower the growth of the epidemic. Marin has a median income 50% higher than Contra Costa county and has a much slower growth rate of COVID-19. This correlation between wealth and growth rate seems to be the case for most of the Bay Area’s counties.
Look to Learn from the Interesting Exceptions: San Mateo and Sonoma
The two counties that don’t follow the trend are Sonoma and San Mateo. Sonoma is doing quite well despite its low median income. What is this county doing well? Why is Sonoma county particularly effective? If Sonoma county’s dashboard is any indication, their Public Health department seems to have been on top of this crisis from the start. Bay Area Dashboard Rankings (This link needs updating given the changes in County dashboards but Sonoma’s dashboard has been consistently good.)
Conversely San Mateo has a fairly high median income and yet the epidemic is growing more rapidly there than in counties with similar incomes. Why?
Napa County is an Outlier in Many Ways
Napa county has much less testing than other counties and yet has a lower growth rate. Napa county also has a lower median income and yet still has a slow growth rate. My best guess to synthesize all these discrepancies is that Napa county currently has a very small case load compared to all of the other counties. I’m guessing that Napa’s case load is small enough and shelter in place started before the cases in the county had a chance to really grow. Now that it is blessed with a relatively low number of cases it can do infection contact tracing to keep its case count low.