With data-driven models, every day of COVID-19 can tell us more about what happens next.
By: Vmasc Odu
Tags: Data Science, Modelling And Simulation, Virginia, Coronavirus, Data Driven Decisions

What can be good about a data-driven model?

A data-driven model like the one described can be useful when there is a lot of uncertainty about how the virus spreads. And with a new virus like COVID-19, there is definitely a lot of uncertainty in the air, especially early on.

Our simple data-driven model doesn’t have this requirement, it only needs to know the total number of current cases and how many cases occurred in the last day. This can be useful to estimate how quickly the disease might spread while scientists work to get more certain values for traditional disease spread models.

Also, if thorough data collection is taking place, data-driven models can be used at very fine-grained levels — counties, cities, and neighborhoods. This allows us to study how quickly the virus is spreading in different area that might otherwise appear to be similar. With this information, we might be able to tie the spread of a virus to other risk factors or comorbidities.

What can be bad about a data-driven model?

Data-driven models don’t have the same scientific insight as traditional models — they just crunch numbers. Because of this, data-driven models can easily make unrealistic predictions, especially early on in the spread of a disease.

Consider the following example:

‣ A county is has just reported its first case. The total cases is now equal to 1.

‣ The next day, the same county records a second case. The total cases is now equal to 2.

‣ With the total number of cases doubling from the first day to the second day, the growth factor is now 2.

‣ Using this growth factor, our model would predict the 3rd day to have twice as many cases as the second day, bringing the total predicted cases to 4.

From here, we could continue on, estimating 8, then 16, 32, 64, 128, and 256 cases — and so on! This sort of doubling (or exponential growth) is unsustainable, unrealistic, and not an accurate representation of how COVID-19 or most other diseases have played out. However, the beauty of the data-driven model is that it is self-correcting. Errors from one day will reduce the likelihood of error in following days (see the example below for Arlington County).

Table of forecasted and actual new COVID-19 cases in Arlington County, VA.

In this table, we see a mis-forecast on March 13th (2 cases vs. 5), which self-corrects over the following days. As time goes on, the scale of error in projections narrows and the model predicts actual cases across Arlington county much more accurately.

Additionally, a data-driven model is also at the mercy of data entry. For example, if the organization responsible for collecting and posting the data is not fully staffed on weekends (or not fully staffed at all) then the model that uses the data may show that the growth of the virus has stalled when in actuality there are simply less people available to gather the data the model uses as inputs.

Alternatively, results may be pushed into records all at once, indicating inaccurate, massive spikes in a specific county that appeared to be slowing — when in reality growth continued ahead of the bulk compiling of new figures in over-leveraged public health departments.

Again, the self-normalizing nature of this model can help to resolve this statistical aberrations and over time, the model will correct itself.