Working Paper: NBER ID: w27335
Authors: Andrew Atkeson; Karen Kopecky; Tao Zha
Abstract: This paper presents a procedure for estimating and forecasting disease scenarios for COVID-19 using a structural SIR model of the pandemic. Our procedure combines the flexibility of noteworthy reduced-form approaches for estimating the progression of the COVID-19 pandemic to date with the benefits of a simple SIR structural model for interpreting these estimates and constructing forecast and counterfactual scenarios. We present forecast scenarios for a devastating second wave of the pandemic as well as for a long and slow continuation of current levels of infections and daily deaths. In our counterfactual scenarios, we find that there is no clear answer to the question of whether earlier mitigation measures would have reduced the long run cumulative death toll from this disease. In some cases, we find that it would have, but in other cases, we find the opposite — earlier mitigation would have led to a higher long-run death toll.
Keywords: COVID-19; SIR model; forecasting; mitigation measures; counterfactual scenarios
JEL Codes: C01; C02; C11
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| mitigation timing (C41) | cumulative deaths (J17) |
| earlier mitigation measures (Y20) | long-run cumulative death toll (J17) |
| fraction of population actively infected when herd immunity is reached (J11) | long-run outcomes (P27) |
| effective reproduction number (C59) | normalized transmission rate (C22) |
| normalized transmission rate (C22) | effective reproduction number (C59) |
| relaxation of mitigation measures (H84) | second wave of infections (F44) |
| transmission rate returns to early April levels (C22) | severe second wave (E32) |