- These models are simple and they vary for each location. 
- They also have several areas of uncertainty due to the little data available when the paper was published. It was unknown what distancing measure would be taken at the time of writing the paper.
- The variability is difficult for policy makers who need to make the social and economic decisions and understand that lowering the interventions will cause an increase in the prevalence of the disease. 

University of Pittsburgh

- The results were effected when the data was either confirmed cases or mortality data.
- Many other aspects of disease spread were not considered (i.e. incubation time, and asymptomatic individuals) either because the data was not available or because they were not included for parsimony and accessibility


Considerations in Using the Models for the COVID-19 Pandemic

Bertozzi, A. L., Franco, E., Mohler, G., Short, M. B., & Sledge, D. (2020). The challenges of modeling and forecasting the spread of COVID-19. Proceedings of the National Academy of Sciences, 117(29), 16732-16738 https://doi.org/10.1073/pnas.2006520117. 

The challenges of modeling and forecasting the spread of COVID-19

The models were chosen for their simplicity, minimal parameters, and their ability to be specific to a region. The models had varying levels of effectiveness depending on the stage of the pandemic.
- Exponential Growth Model
- Self-Exciting Branching Process Model
- Susceptible-Infected-Resistance (SIR) Compartment Model


Three Macroscopic Models Presented to Forecast the Spread of COVID-19

- Non-pharmaceutical public health interventions, like social distancing, are important for decreasing the impact of the infections. It won't be impactful unless it is sustained over time.
- These models could be used for funding measures for the economic effects of social distancing and sheltering in place. 
- Only the point process models track the reproductive number over time.


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