Daron Acemoglu 

Optimal Targeted Lockdowns in a Multi-group SIR Model

Abstract:

We develop a multi-risk SIR model (MR-SIR) where infection, hospitalization and fatality rates vary between groups—in particular between the “young”, “the middle-aged” and the “old”. Our MR-SIR model enables a tractable quantitative analysis of optimal policy similar to those already developed in the context of the homogeneous-agent SIR models. For baseline parameter values for the COVID-19 pandemic applied to the US, we find that optimal policies differentially targeting risk/age groups significantly outperform optimal uniform policies and most of the gains can be realized by having stricter lockdown policies on the oldest group. Intuitively, a strict and long lockdown for the most vulnerable group both reduces infections and enables less strict lockdowns for the lower-risk groups. We also study the impacts of social distancing, the matching technology, the expected arrival time of a vaccine, and testing with or without tracing on optimal policies. Overall, targeted policies that are combined with measures that reduce interactions between groups and increase testing and isolation of the infected can minimize both economic losses and deaths in our model.

Charles Manski 

Coping with COVID Uncertainties

Abstract:

Treatment of COVID-19 and policy formation must cope with many uncertainties about the nature of the disease and the dynamics of the pandemic. These uncertainties have been well-recognized qualitatively, but they have not been well-characterized quantitatively. Credible measurement of COVID uncertainties is needed to make reasonable clinical and policy decisions. I will discuss research on several uncertainties relevant to decision making.