The rationale for California’s extraordinary stay-at-home order is to ensure that the number of COVID-19 cases requiring hospitalization does not overwhelm the capacity of the health-care system.

This raises a number of modeling questions:

» What data do we need to model hospital capacity during the coronavirus pandemic?

» When would the state have reached health-care capacity if the stay-at-home order had not been issued?

» Could we have reached health-care capacity under the public health measures put in place before the statewide stay-at-home order?

» What will happen to health-care capacity under the stay-at-home order? If we do not reach full capacity, how much unused capacity will we have?

The state has not yet answered these questions. As a result, we have no way of assessing the success of the stay-at-home order.

The government, in effect, is telling us that it “will know success when it sees it.” We should strive for something more objective and timely.

As the following analysis makes clear, we should be able to demonstrate by April 6 whether we have sufficient health-care capacity, and I am increasingly confident the answer will be that we do.

This post provides one methodology for answering these critical questions (based on my experience as the founder and CEO of a successful applied analytics firm and a baseball analytics nerd). It then summarizes the results of a few health-care capacity models to help the public understand the pressure our health-care system may — or may not — be under in the next three-to-four weeks.

It is important to note at the outset that the emphasis in the news media on total confirmed cases and total number of beds does not provide much insight into health-care system capacity. This is because confirmed cases may not require hospitalization and, for those that do, new hospital admissions will be partially offset by patient discharges over the course of the pandemic.

In addition, there is a substantial backlog in test reporting and, to make matters more complicated for modeling, it appears that many of the unreported tests were likely negative (i.e., there is selection bias in reporting results).

We also do not know the precise lag between testing and reporting. We need to build a “flow” model with many assumptions until the government improves its reporting and we have more reliable data.

It is also important to note that this is a statewide model. I recognize that localities may face very different circumstances regarding confirmed cases and hospitalization rates (e.g., Santa Clara County versus Santa Barbara County). For this reason, municipalities and counties should be performing their own modeling using their local data.

So with these key points in mind, how do we make sense of the trajectory of the pandemic and its potential impact on health-care capacity in California?

What Data Do We Need to Model Hospital Capacity During the COVID-19 Pandemic?

Gov. Gavin Newsom initially stated we needed 20,000 dedicated hospital beds to handle the COVID-19 pandemic. In his most recent comments on this topic, he said that we needed 50,000 hospital beds.

For the purpose of my modeling, I am going to assess whether 20,000 hospital beds — devoted solely to COVID-19 patients — is sufficient to withstand the pandemic.

In order to build a model, I need to make a number of assumptions (which may be revised as actual data are reported):

We do, in fact, have 20,000 beds throughout California that can be devoted to COVID-19 cases. (Another modeling effort estimated 22,800 were available.) If we actually have fewer beds, this will obviously reduce our capacity.

As I have written previously, I would like the state to confirm its actual bed capacity for COVID-19 cases (while simultaneously meeting the needs of all other candidates for hospitalization) and begin reporting on daily COVID-19 hospital admissions in the state.

Modeling normal hospital beds is the core capacity issue. I am not modeling normal hospital beds versus ICU beds. To do so would require even more data from the state.

The hospitals are fully resourced and staffed. Thus, we can utilize all necessary beds indefinitely (in theory). If we are under-resourced and cannot use all the beds we have, then our capacity will be less than total beds.

The 20,000 beds reflect all the capacity gained through social distancing, which should substantially curtail the transmission of other communicable diseases and thus free up bed space that would have otherwise been needed to treat these diseases. If this has not been taken into account, then our actual capacity may be higher. (I could not determine whether other models are accounting for this possibility.)

I would like the state to report on this as well. This is just one of the many reasons I have previously called on state officials to release California’s model for peer review.

The average duration of a hospital stay is 11 days. This is an estimate. There is limited data on this issue, despite the fact that it is central to accurate modeling.

The longer the average duration, the less capacity we have — and vice versa. Another model suggests 12 days; I picked 11. The model will be revised if there is sufficient public evidence to calculate a more accurate average.

There will not be any therapeutics available between now and April 17 to reduce the hospitalization rate and/or shorten the average hospital stay. If successful therapeutics are introduced during this window, this will substantially increase hospital capacity and the model will need to be updated.

Testing continues to be administered primarily to people who are symptomatic and does not extend to random sampling or widespread community testing before April 17. If the testing rate increases dramatically outside the symptomatic population during this time, then hospitalization rates for confirmed cases will decrease dramatically and the model will need to be revised to address changes in these two data elements.

The testing backlog (which currently stands at roughly 48,600) will be cleared over the next 15 days and then testing and reporting will be timely.

» There is selection bias in the testing backlog — test providers have reportedly delayed reporting negative test results. I am estimating the positive test results in the current backlog to be 1,800 (4 percent), well below the positive rate on reported tests, which is trending upward toward 16 percent.

» These 1,800 positives will be included in my model between March 26 and March 30 on the assumption that if the symptoms of those already tested become serious, they will seek hospitalization during this window. If confirmed positives increase by more than this amount, I will update my model.

There are virtually no false positives, which would erroneously reduce the hospitalization rate, and virtually no false negatives, which would erroneously increase the hospitalization rate.

The hospitalization rate for confirmed positives is at least 12 percent according to a preliminary Centers for Disease Control and Prevention report from March 18.

I performed a survey of nine California counties. It shows that the average hospitalization rate is 20 percent (skewed by Santa Clara County) and the median is 14 percent. I assumed 14 percent in my model. If there is sufficient public evidence that a different number should be used, then the model will be revised.

Based on the above and actual confirmed cases, I am estimating 61 new daily hospitalizations throughout California on March 24. This is the starting point for my model. If the state starts reporting the actual number of new daily hospitalizations, I will update the model accordingly.

A few caveats before getting to the models:

Normally I would test a model before publishing it, but I do not have the data needed to test nor the time to devise a creative work-around. The model could undoubtedly be more precise if I had more time:

» I believe that total bed capacity will not be the problem in California; rather, it will be bed location (the state will have unused capacity in certain areas and insufficient bed numbers in others).

» Normally, when working on a business process model, I would interview experts in the business process. Interviewing medical professionals at this stage of the pandemic is simply not possible.

Armed with more business process data, I could potentially develop a more precise model for hospital admissions and ICU stays and the associated capacity constraints. But then I would still run into the data challenges noted above.

» I am sure there is a lag between a positive test and a hospital admission in many instances. But I do not have any data on this issue, so my model effectively assumes none. What this means for the model is that hospital admissions could increase for a few days at the end of the model period even though confirmed positive cases have stabilized. We may see this in the data.

Models are meant to be iterative. This is version 1.0.

When Would the State Have Reached Health-Care Capacity if the Stay-at-Home Order Had Not Been Issued?

To answer this question, we need to make two more assumptions:

Assumption 1: The number of confirmed cases would increase by 1.3 times per day. This rate roughly corresponds to the estimates of the unchecked rate of virus spread (number of infected roughly doubles every three days).

Assumption 2: The number of confirmed cases would increase by 1.2 times per day. This rate closely corresponds to the rate of increase that would likely occur if California simply froze its state and local public health measures on March 18 and made no further changes in public health policy (we can see this 1.2 times per day rate of increase in confirmed cases from March 22-25).

» Critically, once the measures reached maximum effectiveness after roughly 14 days, the curve would be flat as long as those measures were maintained. This does not mean the number of new cases each day will be precisely identical, but rather that the numbers would fall within a narrow range.

This type of “flattened curve” is starting to appear in the daily new case totals in Italy, which are falling within a fairly narrow range of their average of 5,400 new cases per day. Put simply, I am assuming it is possible to flatten the curve.

Under Assumption 1:

With the unchecked rate of spread, the California health-care system would have reached maximum capacity on April 10. With 35,000 beds, we would reach capacity two days later.

In this scenario, once reaching maximum capacity, the health-care system would have been unable to provide hospital beds for many COVID-19 cases requiring hospitalization. This capacity crisis would have lasted for the foreseeable future. This is the outcome that Newsom sought to avoid by issuing the statewide stay-at-home order on March 19.

Thus, there is no question the state and local governments needed to act to prevent the unchecked spread of COVID-19 to flatten the curve. Without action, the outbreak would have overwhelmed the health-care system. This is clear from the data. The question is whether the state needed to adopt a statewide order to achieve this goal.

Under Assumption 2:

In this scenario, the state did not need to issue the statewide stay-at-home order. With the March 18 public health measures frozen in place, the California health-care system would never have reached its maximum capacity for responding to COVID-19. We would have peaked at 4,664 new daily confirmed cases on April 6, and we would have needed roughly 7,300 beds to handle the outbreak indefinitely under this scenario. (Reminder: This is at the state level; localities may be different.)

This finding is crucial for assessing our policy options going forward. By April 4-6, we should know whether and to what extent the pre-March 19 state and local measures flattened the curve. If they did, the state should have substantial flexibility to lift the stay-at-home order and adopt more narrowly tailored public health measures to manage the outbreak.

Could We Have Reached Health-Care Capacity Under the Public Health Measures Put in Place Before the Statewide Stay-at-Home Order?

Yes, but these conditions would require substantially changing the assumptions made above:

» Increase the hospitalization rate for confirmed cases to 20 percent.

Under the pre-March 19 public health measures, if the hospitalization rate was 20 percent of all confirmed cases, the health-care system would never exceed capacity. We would need roughly 10,400 beds to handle the outbreak indefinitely while the measures were in place.

» In addition to the assumption above, increase the average duration of a hospital stay to 12 days.

Under the pre-March 19 public health measures, if the hospitalization rate was 20 percent of all confirmed cases, and the average hospital stay was 12 days, the health-care system would never exceed capacity. We would need roughly 11,300 beds to handle the outbreak indefinitely while the measures were in place.

» In addition to both of the assumptions above, increase the rate of daily confirmed cases from 1.2x/day to 1.24x/day before flattening the curve.

Under the pre-March 19 public health measures, with the three assumptions made above, the health-care system would never exceed capacity. We would need roughly 17,300 beds to handle the outbreak indefinitely while the measures were in place.

» In addition to all of the above assumptions, assume the curve temporarily flattens for 10 days before starting to rise again, gradually.

Under these assumptions, the health-care system would run out of capacity on April 19. (Of course, with additional beds, this analysis would be different.)

What Will Happen to Health-Care Capacity Under the Stay-at-Home Order? If We Do Not Reach Full Capacity, How Much Unused Capacity Will We Have?

Although the data will reveal the effectiveness of the pre-March 19 public health measures, those measures were replaced by the statewide stay-at-home order. The key question facing us today, therefore, is: What will happen to our health-care capacity under this order?

To answer this question, we need to make an assumption about how effective the stay-at-home order will be when compared to the pre-March 19 measures that were in place.

My model conservatively assumes the stay-at-home order reduces the rate of spread by an additional 10 percent.

In this scenario, the health-care system will never reach system capacity while the order is in effect. We will need roughly 6,300 beds to handle the outbreak during this period, creating unused capacity of roughly 13,700 beds.

Under my model (which I will refer to the “March 26 Model” to distinguish it from any updated models going forward), and using the assumptions listed above, the state will peak at 4,507 daily confirmed new cases on April 6, drop to 4,200 daily confirmed new cases on April 8, and then maintain new daily confirmed cases near this level (i.e., a flat curve) while the stay-at-home order is in place. By way of comparison, as of March 25, there were 433 daily confirmed new cases in California.

I will publish the key predictions from my March 26 Model in a separate post. I will track its predictions against published data from the state, and will do the same for any updated models I develop.

If my model is accurate, it does not mean that the state could abandon all public health measures on April 16. If the state adopted this approach, the virus would begin to spread rapidly again, imperiling system capacity in a short period of time.

But the state would have the ability to start implementing less disruptive public health measures while preserving system capacity. The impacts of less disruptive measures would need to be modeled and then monitored once implemented to ensure ongoing system capacity.

We will have clues in the data from March 22 to April 6 to help guide this effort. I am confident that with the right data surveillance and reporting, this could be managed effectively through regular modeling updates and incremental policy changes.


The key question we face today as a state is whether 20,000 hospital beds constitute the health-care capacity we need to withstand the COVID-19 outbreak. As of March 26, the answer is: “We probably have enough hospital beds, but it depends on your assumptions.”

Had we done nothing to manage the outbreak, the answer would be “no.”

But we have taken many steps to curb the outbreak and we now need to see how those steps translate into confirmed cases, hospitalization rate and average duration of hospital stays. If the state collects and reports data on these variables, we should know by April 3 through April 6 whether we have sufficient system capacity. I believe we do.

Of course, if the state increases the number of beds that can be dedicated to COVID-19 to an amount above 20,000, and if those beds can be resourced and staffed, then my qualified “probably” would become something more like a “quite likely or almost certainly.”

What Do We Do Now?

Based on my modeling, my advice to the government today is simple: Collect and publish the data we need to build more precise models and assess the success of public health measures. Plan for success. Begin modeling less disruptive approaches to flattening the curve so that we can begin returning to more normal life as early as April 17.

My advice to the public at this time is simple as well: Do not assume that our health-care system will be overrun. It will have ample capacity under a wide array of realistic assumptions.

Do your part to maximize the chances it will not be and comply with the stay-at-home order. Starting on April 3, pay very close attention to the data.

We should know by April 6 how well the stay-at-home order is working (unless there are substantial test reporting backlogs at that time). If it is succeeding, you should call your elected officials and insist that they lift the stay-at-home order and replace it with more carefully tailored public health measures that will manage the pandemic while we gradually return to normal life.

Until then: Be smart. Stay healthy.

— 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.

— 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.