Last week, we began forecasting the potential levels of demand for hospital resources that could result from the outbreak with COVID-19. The timing of this forecasting effort coincided within two days of the admission of the first COVID-19 patient into the UPenn health system. Since we didn’t yet have many data points for the model, we instead set parameters from regional reports and various publications.
Now that a little over a week has passed, we can take advantage of better regional data to produce more accurate forecasts from CHIME.
The aim of this blog post is to demonstrate our thought process, and to show analysts how to recalculate parameters used as inputs by CHIME based on local observations to date. Specifically, we’ll consider:
- Hospitalization %(total infections) - The total % of COVID-19 patients requiring hospitalization
- Doubling Time estimate
The method we use to calculate both parameters is similar to a manual least-squares approach.
Let’s start with some motivation to explore these parameters:
- Our current assumption that 5% of all infected patients will be hospitalized comes from the Verity et al paper. However, more recent studies cited by a Nature news article suggest that it may be important to consider the impacts of the ratio of asymptomatic and symptomatic infected patients. These studies suggest that the percentage of mild and asymptomatic infected patients could be as high as 50%. This has implications for how we estimate the percentage of infected patients requiring hospitalization. To take this into account, we’ll explore updates of our default value for Hospitalization %(total infections).
- Our clinical partners have stated they believe the doubling time is faster than our default of six days. In addition, there are publications that cite doubling times between 2 and 4 days early in the spread.
Using a Spreadsheet to Estimate Hospitalization % of total infections
Create a spreadsheet with columns like the one below:
1) Populate the spreadsheet with the past week’s (or month’s or whatever) observations (these are columns C and L above, in green).
2) Enter the assumed constants in the blue columns. While it’s likely that different regions have different values, the values we’re currently using are as follows:
- (Col E) Estimated Market Share = 15%
- (Col G) % of Symptomatic that are Hospitalized = 5
- (Col H) % Symptomatic Infected = 100
- (Col I) % Asymptomatic Infected = 0
- (Col K) Rate of Detection = 10%
3) Fill in the last remaining columns (orange ones) with the following equations:
- (Col F) Estimated % Hospitalizations of Total Infected = % of Symptomatic that are Hospitialzed * % Symptomatic Infected (Col G * Col H)
- (Col J) Estimate of Total Infected = Regional Known Infections/Rate of Detection (Col K/Col L)
- (Col D) Estimated Current Hospitalizations = Estimate of Total Infected * Estimated Market Share * Estimated % Hospitalized (Col J * Col E * Col F)
4) Now compare the error between the Estimated Current Hospitalizations (Col D) and the Observed Current Hospitalizations (Col C).
Goal: Can you adjust the parameters in blue to reduce the Estimated Error in column B? One could use various parameters–such as Market Share or Assumed Rate of Detection–to reduce the error.
5) Our Strategy, inspired by the Nature news article mentioned above: Only modify % Asymptomatic Infected (Col I) to values around 40%-60%, and hold these parameters constant: Market Share and Assumed Rate of Detection.
6) We discovered that setting % Asymptomatic Infected (Col I) between 50% and 60% minimized the Estimated Error, resulting in % Hospitilized between 2.5 and 2.0. We selected 2.5 as our hospitalization rate of infected patients since 50% was the high end estimate from the Nature news article.
Using CHIME to Estimate a More Accurate Doubling Time Parameter
Now we’re going to get a better estimate of the Doubling Time parameter by comparing CHIME’s forecasted hospitalizations to the actual Observed Current Hospitalizations.
In short, we’re going to adjust the Doubling Time parameter in CHIME and re-run CHIME until its output matches Observed Current Hospitalizations.
A Note On the Social Distancing Parameter:
Because the Observed Current Hospitalizations are telling us who was infected 5-7 days ago, the Social Distancing parameter may need to be changed accordingly. The effects that Social Distancing has on the spread of the disease often takes a week (or more) before they are realized.
In our case, since we’re looking at March 14 through March 21, we’ll set the Social Distancing to zero (Do Nothing) over this period.
See our blog post for a detailed exploration of social distancing.
|Day 0||March 14, 2020|
|Downtown Current Hospitalizations||2|
|Doubling Time||First run 6, eventually we tried 4|
|Hospitalization %(total infections)||2.5|
|ICU %(total infections)||0.75 (30% of Hospitalized patients)|
|Ventilated %(total infections)||0 (Only considering ICU)|
|Hospital Length of Stay||8|
|ICU Length of Stay||16|
|Vent Length of Stay||N/A|
|Hospital Market Share (%)||15|
|Currently Known Regional Infections||33|
In the first run of CHIME, we set the Doubling Time to 6, based on the publication we were using at the time.
|Doubling Time Six||Doubling Time Four|
The doubling time scenario of six has slightly less error then the doubling time of four. For now, we’re opting to run scenarios bounded by a doubling time of four and six.
While some sources are citing doubling times between two and four days early in the spread, it is essential to remember that this four-day estimate is the estimated doubling time before March 14. Since then, state and local government officials enacted significant distancing measures (starting on March 15), that will slow the spread and increase the doubling time. Furthermore, doubling time in the simulation is applied to rate of growth of all new infections, not only the number of confirmed cases. As testing effort increases, confirmed cases can grow faster than the actual infections. We remain on the lookout for definitive references that can address the rapid growth of positive cases in the US and Europe while accounting for the bias of missing positive cases.
Due to these social distancing policies, we’re currently estimating a reduction of social contact by 30-40%. In the coming week, we expect to begin to see the impact of those social distancing policies. We will then run another simulation with day zero set to March 22, 2020, and repeat the process described in this blog post. If the estimated distancing effect holds, the current level of social distancing policies could cut the surge peak by more than 50%.
Next week we’ll continue to track the rate of infected and hospitalized to better understand the impact of our policies and to continue to refine our forecasts of potential demand.
– Penn Predictive Healthcare Team