This column outlines the revisions in methodology that constitute my Model 3.0 for predicting the number of hospital beds California requires to manage the COVID-19 outbreak. (I developed a Model 2.0 earlier this week, but it immediately became obsolete when California changed its reporting of pandemic statistics on March 31).
Background
The key justification for Gov. Gavin Newsom’s extraordinary statewide stay-at-home order on March 19 was that it was necessary to “flatten the curve” in the number of new COVID-19 cases each day to ensure the health-care system had the necessary number of beds to properly care for coronavirus (and all other) patients.
Given this rationale, the critical question is how many beds is California likely to need given the actual trajectory of the virus.
Using publicly reported data on new cases, hospitalizations and fatalities, I built a model to answer this exact question. The state has predicted it will need 20,000 to 50,000 beds through May. It reiterated those predictions on March 30.
My modeling shows we need far fewer beds. I do not believe the state’s prediction is supported by the publicly released data.
(In certain locations, it is possible that there could be a bed shortage, but this is becoming less likely — and less likely to be an ongoing constraint— as we enter our 14th day under the stay-at-home order.)
Model 1.0
On March 27, Noozhawk posted my California health-care capacity Model 1.0 column and the assumptions upon which it is based.
The key predictions from Model 1.0:
There are essentially three waves of the COVID-19 pandemic in the data based on significant changes in public health measures through March 19, when the stay-at-home order was issued.
» Wave 1 ended on March 8 when decentralized public health measures began throughout California.
» Wave 2, which was marked by decentralized public health measures, ended on March 19 with the issuance of the stay-at-home order.
» Wave 3 (which will end whenever the stay-at-home order is replaced with less disruptive public health measures).
» It will take roughly 18 days from the end of Waves 1 and 2 for their impact to be fully represented in the data.
» As a result of these three waves, new daily confirmed cases would peak on April 6 (end of Wave 2) at 4,579 and then decrease slightly as the March 19 stay-at-home order reached its maximum effectiveness on April 9.
» California would need roughly 6,400 hospital beds to manage the pandemic indefinitely under the stay-at-home order.
As I noted at the time, the model would be updated based on better available data.
Model 1.0 has performed reasonably well.
But as I anticipated, there have been significant changes in the quality and quantity of data reported over the past several days, including the introduction of new statistics by the state. As a result, I am updating my model to version 3.0.
Model 3.0
The key changes in assumptions and caveats from Model 1.0 to Model 3.0 are:
Rate of Spread
» Revised the initial (end of Wave 1) rate of spread slightly higher in Model 2.0
» Revised the rate of spread for Wave 2 downward on a gradually reducing curve
» Revised the rate of spread downward for Wave 3 based on the slower spread now reflected in Wave 2 because the stay-at-home order will likely have greater impact than prior decentralized public health measures
Hospital Admissions
» Hospital admissions have been revised significantly to conform to the state’s new reporting. There are two categories of hospital admissions: “Suspected” and “Confirmed.” The state has offered little guidance on how these categories are defined. I am assuming the following definitions and relationships:
» Suspected: There are “suspected” COVID-19 cases in the hospitals that have not yet tested positive for the virus, but are being treated as if they are positive.
» The model assumes a three-day lag between hospitalization and a test result.
» The model assumes that .4 of this population will test positive and that .6 will test negative. This is an assumption for calculating certain statistics. Positives are added to confirmed cases. Negatives are not. However, this differentiation does not alter the combined bed total calculations as set forth below.
» Confirmed: There are positive tests that are followed by hospitalization. For simplicity, the model assumes that a person who tests positive will develop symptoms requiring hospitalization on the day the test results are confirmed (three-day lag). This does not have a meaningful impact on bed capacity. In this population, the model assumes a .2 hospitalization rate (rather than .14 as in Model 1.0).
In order to build the model, I had to estimate certain starting values for the new statistics the state is reporting by working backward in time from limited known data. These starting values may need to be revised.
Backlog
» Structural backlog. The difference between tests conducted and reported each day remains accounted for in the model by separately calculating and reporting “suspected” COVID-19 cases in a separate category, just as the state is now doing.
» I believe that any actual positives resulting from the 39,000-test backlog that suddenly appeared in the data on March 25 would now be accounted for in either actual or suspected cases. I made no additional allocation for this backlog as there has been no additional guidance from the state on how to view these cases.
» Average hospital stay duration: I increased this to 12 days.
» I did not calculate the intensive-care unit bed requirement, but it appears the state will need .4+ ICU beds for every hospital bed required for a confirmed case. It will likely need a lower ratio for suspected cases because at least some percentage of this population will test negative and not require such extraordinary care.
What Is Now Being Reported from Model 3.0
I have predictions running past April 16 in my model, but they are not shown yet because I predict the curve will be flat or bent by April 9 — a condition that will continue so long as the stay-at-home order is in place.
The key predictions from Model 3.0:
» New daily confirmed cases would peak on April 6 at 1,699 and then decrease to 1,597 per day as the stay-at- home order reached its maximum effectiveness.
» California would need roughly 1,700 hospital beds to manage confirmed cases resulting from the pandemic indefinitely under the stay-at-home order.
» The state would need roughly 2,800 hospital beds to manage suspected cases resulting from the pandemic indefinitely under the stay-at-home order.
» In total, the state needs roughly 4,500 hospital beds to manage the pandemic while the stay-at-home order is in place.
Even if my estimates and assumptions are wildly inaccurate — a view that plainly runs contrary to the initial performance of Model 3.0 — it is difficult to understand from the publicly reported data how the state believes 50,000 hospital beds will be required for COVID-19 patients in May, let alone 20,000.
I will track the performance of Model 3.0 in my separate Model 3.0 Performance Tracker.
— 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.