Working Paper: NBER ID: w27373
Authors: Glenn Ellison
Abstract: This paper provides a quick survey of results on the classic SIR model and variants allowing for heterogeneity in contact rates. It notes that calibrating the classic model to data generated by a heterogeneous model can lead to forecasts that are biased in several ways and to understatement of the forecast uncertainty. Among the biases are that we may underestimate how quickly herd immunity might be reached, underestimate differences across regions, and have biased estimates of the impact of endogenous and policy-driven social distancing.
Keywords: COVID-19; SIR model; heterogeneity; epidemiology; social distancing
JEL Codes: I18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Calibrating classic SIR model to heterogeneous data (C59) | Biased predictions about COVID-19 epidemic (C46) |
| Calibrating classic SIR model to heterogeneous data (C59) | Underestimate how quickly herd immunity could be achieved (C92) |
| Predictions based on homogeneous SIR models (C59) | Overstate number of infections needed for herd immunity (C92) |
| Targeted lockdown policies (R28) | More effective in heterogeneous populations (C92) |
| Dynamics of infections vary across different subpopulations (J11) | Complicate ability to make accurate forecasts about overall course of epidemic (C53) |