Why is CA Doing so much Better than NY?
Many
analysts have asked why is California (CA) doing so much better than New York (NY)
in this pandemic. NY experienced its
first case on March 1st, while CA experienced its first case earlier
on January 26th. In both
states, the infection did not become obviously serious until March 11th
when the infection count crossed 200 cases in both states. On March 19th when CA confirmed case
count reached 25 per million population Governor Newsom declared a lockdown
in CA. On March 22nd,
NY had already reached an infection rate of 812 per million before Governor Cuomo
declared a lockdown. Thus, even though
NY was only 3 days behind in enacting strong mitigation measures, it was 33 times more infected by then and that much further along the exponential growth
curve. In the early days of an infection’s
exponential growth phase quick and decisive action is crucial. NY's slower response then has led it to have a measured infection rate that is 16.2 times worse than CA now.
In addition the NY situation was complicated by its strong infection epicenter in metro-New York City (NYC) which involved a strong-willed mayor and two other governors who needed to cooperate to shut down the entire region to prevent cross-infection. This did not actually happen until March 23rd when CT locked down and NY state infection had already reached 1073 per million. These delays have cost NY thousands of unnecessary lives. We had urged the government to act forcefully on Mar 9th. Had any of them acted then, much of the tragic loss of lives could have been avoided and we would have been 2 weeks further along on our recovery.
The magnitude of the difference between the two states is actually more than a factor of 16.2. NY’s death count per million is 28.1 times worse than CA. Some of this difference may be due to the younger population in CA (36.8 vs 39.0 yrs) especially in Santa Clara County where many of the early CA cases were located. But the delayed reaction in NY that led to overwhelmed hospitals and testing facilities in NY might have also played a part. When you look at the number of tests performed in NY vs CA it might look like NY is doing better with 2.6% of its population now tested against just 0.5% of the CA population tested to date. But this not the best metric to compare. When the infection rate is higher you need to test more to prove that you have tested all the mild and asymptomatic cases. The percentage of all tests that yielded a positive result is a measure of how thoroughly a state has tested its population. On this measure, CA is doing better than NY with 13% of completed tests yielding a positive result compared to NY’s 42%. NY's number shows that it is probably only testing the most seriously ill patients and not probing the true spread of the disease in NY. Thus the true infection rate in NY might be 2% of its population and the ratio between the 2 states may well be not just 16.2 times but 25 to 35 times worse in NY. This would mean that NY with an infection rate that was 33 times worse at the time of lockdown has continued to maintain that same ratio of infection disadvantage 3 weeks later. Other considerations such as socioeconomic factors like race, income, and behavior, as well as population density, probably also matter but we argue that all the observed differences could be just due to the math of exponential infection.
NY suffers from the terrible legacy of delayed and botched testing and delayed and weak lockdown decision-making during a pandemic which has carried through to this day. Of course, things could have been much worse for both states if the local and state governments had waited for the President to act. Both states would have been still experiencing exponential growth instead of showing clear signs of a top last week (see figure above).
| State | Infections | Infections | Deaths | Deaths | Mortality | Mortality | Testing | % test | Median |
| /million | /million | Coincident | Estimate | Positive | Age | ||||
| NY | 10,441 | 203,123 | 10,834 | 557 | 5.3% | 7.1% | 2.6% | 41% | 39.0 |
| CA | 646 | 25,536 | 782 | 20 | 3.1% | 4.2% | 0.5% | 13% | 36.8 |
| NY/CA | 16.2 | 28.1 |
In addition the NY situation was complicated by its strong infection epicenter in metro-New York City (NYC) which involved a strong-willed mayor and two other governors who needed to cooperate to shut down the entire region to prevent cross-infection. This did not actually happen until March 23rd when CT locked down and NY state infection had already reached 1073 per million. These delays have cost NY thousands of unnecessary lives. We had urged the government to act forcefully on Mar 9th. Had any of them acted then, much of the tragic loss of lives could have been avoided and we would have been 2 weeks further along on our recovery.
The magnitude of the difference between the two states is actually more than a factor of 16.2. NY’s death count per million is 28.1 times worse than CA. Some of this difference may be due to the younger population in CA (36.8 vs 39.0 yrs) especially in Santa Clara County where many of the early CA cases were located. But the delayed reaction in NY that led to overwhelmed hospitals and testing facilities in NY might have also played a part. When you look at the number of tests performed in NY vs CA it might look like NY is doing better with 2.6% of its population now tested against just 0.5% of the CA population tested to date. But this not the best metric to compare. When the infection rate is higher you need to test more to prove that you have tested all the mild and asymptomatic cases. The percentage of all tests that yielded a positive result is a measure of how thoroughly a state has tested its population. On this measure, CA is doing better than NY with 13% of completed tests yielding a positive result compared to NY’s 42%. NY's number shows that it is probably only testing the most seriously ill patients and not probing the true spread of the disease in NY. Thus the true infection rate in NY might be 2% of its population and the ratio between the 2 states may well be not just 16.2 times but 25 to 35 times worse in NY. This would mean that NY with an infection rate that was 33 times worse at the time of lockdown has continued to maintain that same ratio of infection disadvantage 3 weeks later. Other considerations such as socioeconomic factors like race, income, and behavior, as well as population density, probably also matter but we argue that all the observed differences could be just due to the math of exponential infection.
NY suffers from the terrible legacy of delayed and botched testing and delayed and weak lockdown decision-making during a pandemic which has carried through to this day. Of course, things could have been much worse for both states if the local and state governments had waited for the President to act. Both states would have been still experiencing exponential growth instead of showing clear signs of a top last week (see figure above).
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