Quantifying the spatial spillover effects of non-pharmaceutical interventions on pandemic risk

Table 3 The coefficients estimates of average direct effects from spatial econometric model

Variables	Phase A			Phase B
Variables	Coef.	S.D.	\(95\%\) CI	Coef.	S.D.	\(95\%\) CI
School closures	\(-\)0.0002	0.0002	[\(-\)0.0005, 0.0001]	0.0005	0.0001	[0.0003, 0.0007]
Workplace closures	0.0006	0.0001	[0.0004, 0.0008]	0.0002	0.00005	[0.0001, 0.0003]
Cancellation of public events	0.0004	0.0001	[0.0002, 0.0006]	0.0002	0.00004	[0.0001, 0.0003]
Restrictions on gatherings	− 0.0003	0.0001	[− 0.0005, − 0.0001]	− 0.0001	0.00004	[− 0.0003, − 0.0000]
Public transport closures	\(-\)0.0000	0.0001	[\(-\)0.0002, 0.0001]	− 0.0003	0.0001	[− 0.0004, − 0.0001]
Stay-at-home orders	0.0003	0.0001	[0.0001, 0.0005]	0.0002	0.0001	[0.0001, 0.0003]
Restrictions on internal movements	0.0002	0.0001	[0.0000, 0.0003]	\(-\)0.0001	0.0001	[\(-\)0.0001, 0.0000]

Our data includes 48 states (regions) and Washington D.C. Coef.: Posterior mean of coefficients. S.D.: Standard Deviation. We run a Markov chain of 50,000 iterations with a \(50\%\) burn-in ratio. We treat the posterior mean of parameters as their Bayesian point estimates. We also report the standard deviation of the posterior samples of parameters in parentheses. We rely on the Bayesian \(95\%\) CI to judge the significance of parameters. Bolded color indicates significance

ISSN: 1476-072X