In my previous post on COVID-19, I wrote about the importance of putting the spread of the coronavirus in perspective, which requires careful consideration of the data we have and the data we do not have.
In this column, I am encouraging everyone to ask elected officials and the news media to fill in these data gaps so that we can more carefully assess how fast the virus is actually spreading, whether the government’s modeling is accurate, and whether its public health measures are working.
I believe that if the government and media begin to answer the following questions, we will gain valuable perspective on the actual risks posed by the virus and put ourselves on a track to gradually return to more normal life in California sometime shortly after April 17. Please put these questions to your local elected officials and media representatives as soon as possible.
And please practice maximum social distancing for the next 10 days! If we honor the stay-at-home order for a full two weeks, there is a very good chance the data will show a dramatic flattening of the curve and the ability to implement less disruptive social distancing measures.
1. Why aren’t we reporting the number of COVID-19 patients requiring hospitalization and daily hospital capacity?
The stated goal of California’s extraordinary stay-at-home order is to “flatten the curve” so that the number of hospital admissions for COVID-19 does not exceed health-care capacity and our health-care professionals can provide the optimal level of care for each patient (whether COVID-19 or otherwise).
Given this rationale, these two pieces of data are the most important reporting the government could be doing, not only to reassure the public, but to assess the extent to which social distancing measures and the stay-at-home order are actually flattening the curve.
Yet, the government is not reporting this data. We should insist that the government begin reporting this data immediately.
Ideally, the government would also report average hospital stay duration for COVID-19 patients. This has a major impact on actual hospital capacity — the longer the average stay, the less capacity we have in the system (and vice versa).
2. Why aren’t we reporting the rate of increase, rather than just the amount of increase, with respect to all COVID-19 statistics (number of tested, number of confirmed cases, number hospitalized)?
The same data regarding the outbreak can paint two very different pictures. In California, we confirmed 265 new cases on March 22. A week before, we confirmed 57 new cases on March 15. That’s 4.6x growth in one week.
That looks like an explosive trajectory, but the daily growth rate was 17 percent on March 15 and 18 percent on March 22. This is more of a constant trajectory, which could be easily demonstrated graphically to help provide the public with more perspective on the spread of the virus.
3. Why aren’t we reminding the public that it takes roughly two weeks for the benefits of public health measures to appear in the data?
The virus is said to have a roughly two-week incubation period. This effectively means that measures put in place on a given day will not produce their maximum public health benefits for two weeks.
In California, the first stringent public health measures were put in place in various localities on March 8. This means that the benefits of these measures should start appearing in the data on March 23.
On March 19, Gov. Gavin Newsom issued the statewide stay-at-home order. This means that from March 23 to April 2, the data should demonstrate the impact of social distancing measures undertaken before the statewide order went into effect.
From April 3 to April 17, we should see the impact of the statewide self-quarantine order. If hospitalizations remain below system capacity after April 3, we will have successfully flattened the curve.
4. Why are there far fewer fatalities in California when compared to Italy, given our rough similarities in population and health-care systems and the fact that COVID-19 has been present for roughly the same period of time in each area?
California and Italy both documented COVID-19 cases in January. Both have excellent health-care systems. California has roughly two-thirds of the population of Italy, but as of March 23, California had 27 deaths from COVID-19, and Italy has had more than 6,800, tragically.
To what extent can this enormous difference in fatalities be explained by demographic differences, such as differences in median age (California’s is younger) and cigarette smoking rates (California’s is less than half of Italy’s and one of the two lowest in the United States.).
What other factors could explain this dramatic difference in fatalities? What does this mean for our response efforts?
5. Will the government publish the model it is using to predict the potential curve that must be flattened (i.e., daily confirmed cases, daily hospitalizations and average duration of hospital stays) and the flattening that should result from the stay-at-home order?
Peer review is critical for scientific advances. California is home to some of the finest universities and scientific minds in the world. We should be subjecting our state model to peer review by our premier epidemiologists and statisticians to test and refine its data and assumptions, thereby improving its overall accuracy and utility.
This should be done at regular and short intervals (i.e., every three days).
To help people understand why the number of confirmed cases is nowhere close to overwhelming California’s health-care capacity today (but could be weeks from now under certain scenarios), I will be building and publishing a very rough model later this week to illustrate how many new daily confirmed cases would be required to generate hospitalizations that exceed system capacity under various assumptions. (I recognize that certain localities may face capacity constraints earlier; I’m focusing on the state.)
6. How often is the government updating its model and refining assumptions with actual data from California and jurisdictions with demographics similar to California?
Models are only as good as the data and assumptions upon which they are built. In California, we are just beginning to see larger numbers of confirmed cases, which may make it possible to develop (1) a reasonably accurate hospitalization rate (i.e., number of hospitalizations/number of confirmed cases) and (2) average duration of hospital stays.
Given limited testing for the coronavirus, these are the two most important pieces of data for modeling the curve. We will have substantial changes in our data roughly every three days, so updating the model on a similar timeframe will be critical.
Insofar as we seek to supplement California data with other data, given our relatively small sample, it is critical that we pick data from jurisdictions that are very similar to California in terms of health-care quality, average age, smoking rates, etc. Otherwise, we will substantially degrade the accuracy of the model.
7. What are the criteria for lifting the statewide stay-at-home order?
How will California determine whether the stay-at-home order remains necessary? Presumably, the key factor will be whether there is sufficient health-care capacity to begin gradually relaxing stay-at-home mandates while maintaining a flattened curve.
But the government should be clear on the criteria it is going to use and the amount of capacity it deems necessary before the order will be lifted.
8. When will California begin assessing whether those criteria have been satisfied?
The data needed to assess the effectiveness of the statewide self-quarantine order will be available beginning April 3. By April 17, we will have the data we need to assess the maximum effectiveness of the stay at home order (i.e., the maximum rate of flattening of the curve).
When will the government complete its analysis and inform the public whether the order will be lifted? We are asking Californians to make enormous sacrifices to ensure access to the health-care system for COVID-19 patients and others who are seriously ill. The public deserves to know how long this period will last.
9. What is the long-term plan for managing the COVID-19 pandemic?
There is a popular misconception in the news media and public that there are essentially only three options for managing this pandemic:
» Do nothing.
» Perform widespread and random testing with contact tracing and quarantining.
» Issue self-quarantine orders for virtually all residents.
We know that option 2 is unlikely in the United States, given the limited availability of test kits and the fact we already have many cases. But this does not mean we default to option 3.
Social distancing measures exist on a spectrum from not shaking hands (least social distancing) to everyone staying at home (maximum social distancing). It is quite possible that a combination of moderate and tailored social distancing measures may be sufficient to keep COVID-19 cases from overwhelming the health-care system (and the data from March 23 to April 2 may help demonstrate this).
We should insist that the government develop and model alternatives on the assumption that the stay-at-home order will successfully flatten the curve. We need to enter our “new normal” phase of managing the coronavirus as soon as possible to reduce the massive collateral costs of near-maximum social distancing.
— Brian Goebel served as a senior official in the Treasury and Homeland Security departments following 9/11. Since 2005, he has founded successful consulting and analytics firms serving governments around the globe; launched 2040 Matters, a nonpartisan public policy blog dedicated to restoring the American Dream for younger Americans; and was elected to the Montecito Water District Board of Directors in 2018. Click here for previous columns. The opinions expressed are his own.