Working Paper: NBER ID: w27590
Authors: Alberto Bisin; Andrea Moro
Abstract: We simulate a spatial behavioral model of the diffusion of an infection to understand the role of geographic characteristics: the number and distribution of outbreaks, population size, density, and agents’ movements. We show that several invariance properties of the SIR model concerning these variables do not hold when agents interact with neighbors in a (two dimensional) geographical space. Indeed, the spatial model’s local interactions generate matching frictions and local herd immunity effects, which play a fundamental role in the infection dynamics. We also show that geographical factors affect how behavioral responses affect the epidemics. We derive relevant implications for estimating the effects of the epidemics and policy interventions that use panel data from several geographical units.
Keywords: epidemiology; spatial SIR model; behavioral responses; COVID-19; policy interventions
JEL Codes: D01; I12; R10
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| local interactions in the spatial SIR model (C21) | matching frictions (F12) |
| local interactions in the spatial SIR model (C21) | local herd immunity effects (C92) |
| geographic characteristics (R12) | rate of infection spread (J11) |
| higher population density (R23) | increased contact rates (J69) |
| increased contact rates (J69) | enhanced transmission of the disease (F42) |
| geographic factors (R12) | behavioral responses to the epidemic (E71) |
| behavioral responses to the epidemic (E71) | dynamics of the epidemic (C69) |
| geographic factors (R12) | dynamics of the epidemic (C69) |
| behavioral responses to the epidemic (E71) | herd immunity operation (C92) |
| geographic characteristics (R12) | pattern of infection spread (I14) |