Modeling and forecasting the spread of COVID-19 remains a challenge. This report details three regional-scale models for forecasting and assessing the course of the pandemic: 1) exponential growth, 2) self-exciting branching process, and 3) the susceptible–infected–resistant (SIR) compartment model.

In presenting these models, the authors demonstrate how they are connected and note that in different cases one model may fit better than another.

The illustrates several key points:

1. The reproduction number R is highly variable both over time and by location, and this variability is compounded by distancing measures. These variations can be calculated using a stochastic model, and lower R is critical to decreasing strains on health care systems and to creating time to develop effective vaccines and antiviral therapies.
2. Mortality data and confirmed case data have statistics that vary by location and by time depending on testing and on accurate accounting of deaths due to the disease. Differences in collection methods and in the accuracy of morbidity and mortality data can lead to different projected outcomes.
3. Nonpharmaceutical public health interventions (NPIs) such as social distancing and shelter in place orders offer an important means of reducing the virus’s reproduction number.
4. NPIs may not have a substantial impact on the total number of infections unless sustained over time. Policy makers should be cautious about scaling back distancing measures after early signs of effectiveness.

In sum, this report demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. The models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.