COVID-19 Modeling Pt. 4

In my previous post, I discussed models that can be used to forecast the future behavior of the pandemic. One of the limitations of these models is that they are complex and need to be recalibrated or updated frequently. What would be helpful is an analysis technique that would allow us to monitor various factors on a daily basis to know when a change in the situation is likely. For this purpose, we turn our attention to an approach known as Statistical Process Control (SPC) which was introduced to the industrial community by Walter Shewhart in the mid-1920s.

In SPC, the amount of variation that has transpired over time is used to derive upper and lower limits of activity expected. Unlike other statistical approaches, SPC accounts for the notion that processes happen over time, and in addition to the variability that is seen in the aggregate data, it is important to consider the amount of variability in the data over time. This is best understood by looking at an example. The following SPC chart depicts the daily count of COVID-19 deaths for the state of North Carolina.

As you can see, there are points that are beyond the upper control limit in Phase 3. These points are referred to as special cause points. These points are beyond the range of deaths which might be expected currently. For these points, it would be appropriate to question whether there was a specific precursor that caused these results. With there being so many factors that could lead to these spurts it may not be possible to identify a specific cause. However, that does not detract from our ability to understand the amount of variation which may be expected in the count of daily death, which could help policymakers from reacting to changes too quickly.

Another type of SPC chart that can be used to provide valuable information to policymakers during the time of a pandemic is one that is based on rates such as the example below.

This later chart can be useful when trying to compare multiple rates as it controls for the number of opportunities or sample size, and as such is a more equitable way of comparing multiple rates.

Over the past four posts, I have described a number of models that can be used to assess the trajectory and health impact of COVID-19. As is evident from these discussions, there is a great deal of variability in the data related to this pandemic and an assortment of factors to consider when interpreting this data. With this in mind, it is important to consider all of these factors before making a decision regarding the reopening of communities. In future posts, I will begin to look at some of the actions that need to be taken as we reopen.

2 thoughts on “COVID-19 Modeling Pt. 4”

  1. Edward Sammons

    It is surprising to me more accepted statistical methods are not being used to investigate this erroneously labeled “pandemic”. That said what I know of process analysis is that it is extremely dependent on data acquisition and investigation of change over time.
    Can you comment on how SPC can be used when the data related to infection and death is so catastrophically corrupted. Deaths are being artificially attributed to the virus using arbitrary rules handed down from government seeking to validate and justify draconian civil rights violations. Even worse, these rules change almost day to day making study of the resulting dataset all but impossible. Finally, how can a baseline for any SPC be established when bonafied principal viral caused deaths are unknown?
    Why not utilize a simple but effective run chart to plot study rates. Define each departure from a defined mean with an explanation, then use regression analysis to discover how much influence an identified specific data characteristic change had.

    1. I tend to think of control charts as process behavior charts. Instead of starting with an established mid-line and set of control limits, software today makes it easy to have both recalibrate (recalculate) with the addition of each data point. Who can say what the mid-line or control limits really are based on a sample? What if the sample were one data point more, or one data point less. Some suggest random sampling, however, Deming talked about judgment samples based on what one was trying to assess. You are correct, the number could be wrong, but with a process behavior chart, we can still see a change in the process. As it applies here, we may be counting non-covid patients into the mix but we still should see a change in the process if the number of covid-19 deaths starts to decrease.

      As for the number of cases, who knows? Until there is wide-spread testing, I don’t see how we can do much with the case data.

      Thanks for the comment! Hope you are well.

Leave a Comment

Your email address will not be published. Required fields are marked *