Local containment policies and countrywide spread of COVID-19 in the United States: an epidemiologic analysis

ABSTRACT

We analyse the spatial diffusion of new COVID-19 cases and the countrywide impact of state-specific containment policies during the early months of the COVID-19 pandemic in the United States. We first use spatial econometric techniques to document direct and indirect spillovers of new infections across county and state lines, as well as the impact of individual states’ lockdown policies on infections in neighbouring states. We find consistent statistical evidence that new cases diffuse across county lines, holding county-level factors constant, and that the diffusion across counties was affected by the closure policies of adjacent states. We then develop a spatial version of the epidemiological susceptible–infected–recovered (SIR) model where new infections arise from interactions between infected people in one state and susceptible people in the same or in neighbouring states. We incorporate lockdown policies into our model and calibrate the model to match both the cumulative and the new infections across the 48 contiguous US states and DC. Our results suggest that had the states with the less restrictive social distancing measures tightened them by one level, the cumulative infections in other states would be about 5% smaller. In our spatial SIR model, the spatial containment policies such as border closures have a bigger impact on flattening the infection curve in the short-run than on the cumulative infections in the long-run.

KEYWORDS:

• diffusion
• spatial model
• COVID-19
• epidemics

JEL:

• H77
• I19
• R15

ACKNOWLEDGEMENTS

We thank the participants at the virtual meetings of the Urban Economic Association and the Econometric Society Delhi Winter School for helpful comments. This article supersedes the pre-print of the working paper version (Brady et al., 2020). The views expressed here are those of the authors and do not represent the views of the US Naval Academy, the Department of Defense or the federal government. All errors are our own.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. We discuss possible limitations arising due to the Lucas’ critique in section 4.3.

2. Of course, we do not suggest that giving the states such ability would be desirable. We are merely evaluating its potential impact on the spread of infections across the whole country.

3. Those include Eckardt et al. (2020), Dave et al. (2020), Renne et al. (2020), Giannone et al. (2020), Akovali and Yilmaz (2020) and Brinkman and Mangum (2020).

4. A non-exhaustive list of examples includes Atkeson et al. (2020), Holden and Thornton (2020), McAdams (2020), Favero (2020), Berger et al. (2020), Hornstein (2020) and Ellison (2020).

5. While masking guidance grew in prominence over the course of the pandemic, our dataset covers the early stages of the pandemic when such guidance was inconsistent at all levels (national, state and county). States and localities primarily took their cues from the national authorities with regard to masking requirements, which were insignificant until later in 2020. On the other hand, the most prominent containment measures, listed here, were imposed at the state level during the initial weeks of the pandemic.

6. Via Luis Sevillano on GitHub, but originally published in The New York Times and available here.

7. Here the outbreak appears to stem from both Harris County (Houston) and Galveston County, which is also a fairly densely populated area.

8. See Halleck Vega and Elhorst (2015) for a detailed discussion of the SLX model and the other spatial models employed in this paper.

9. There are various versions of spatial models with temporal dynamics. Elhorst (2012) refers to an SAR model augmented with temporal lag of the dependent variable and a temporal lag of the spatial lag as the time–space dynamic model. Pace et al. (1998) provide an example with their smooth transition autoregressive (STAR) model. Brady (2014) provides a brief overview of some of these models. See also Debarsy et al. (2012) for a discussion.

10. A table of results with time-fixed effects are available in Appendix B in the supplemental data online.

11. Allowing for $\rho \left(n^{\mathrm{\prime }},n\right)$ to have distinct value for each pair of states would yield 49 × 24 = 1176 parameters to be calibrated if we assume symmetric spillovers, and double that if we do not.

12. We cannot say anything about suboptimality of the lenient policies since we do not have a properly specified social welfare function that would take into account the economic costs.

13. Eckardt et al. (2020) show that border closures between European regions significantly slowed down the spread of the virus.

14. See Oates (1999) for a literature review of that topic. Rothert (2022) discusses a few examples of federal fiscal tools that could impact local policies.

Additional information

Funding

Jacek Rothert acknowledges financial support from the National Science Center, Poland [grant number 2019/35/B/HS4/00769].