Model description is in-progress, to be updated soon!

This transmission model has been developed to assist in understanding and forecasting epidemic spread and hospital resource needs, and to explore and examine alternative scenarios (see scenarios tab). The model is calibrated based on Michigan data on COVID-19 testing and demographics, as well as Michigan and other literature data on disease progression, severity, mortality, etc.

Model description



Figure 1. Diagram for the basic transmission model used in this analysis. Figure 1. Diagram for the basic transmission model used in this analysis.

The spread of infectious disease in a population can be modeled as a dynamical system, where the number of infections changes over time accordingly to a host of complex and interrelated processes. The two fundamental disease processes are transmission and recovery, but sociobehavioral processes or other aspects of the natural history of infection can also be included to model a specific disease outbreak.

We have tested several different models based on more complex versions of what is known as an SIR model, or susceptible (S), infectious (I), recovered (R) model. This class of models tracks the fraction of the population in different disease stages.

A diagram of the simplest form of our model is shown in Figure 1. In this model of COVID19 disease, we track people who are susceptible (\(S\)), have mild disease but have not sought care (\(I_1\)), have mild disease and have sought care (\(I_{1,c}\)), have recovered (\(R\)), have severe disease (\(I_2\)), have been hospitalized (\(H\)), and have died (\(D\)). We have also tested similar models accounting for superspreading, alternative distributions of the latent and infectious period, and asymptomatic transmission, with similar results, so we opted for the simplest version of the model in this analysis. From the model, we estimated the number of people who need intensive care, ventilators, or oxygen support as a fraction of hospitalized patients.