Incidence of COVID-19 and Connections with Air Pollution Exposure It is well known that models with a high number of variables can over-fit data sets that contain only a modest number of observations. Model 2 estimates the same linear regression but uses step-wise variable selection following the AIC, Model 3 uses the selected variables in a spatial error model that controls for spatial dependence in the residuals ( _λ_ parameter), Model 4 estimates a spatial autoregressive model that allows for dependence on neighboring observations ( _ρ_ parameter), Model 5 allows for unique spatial autocorrelation and spatial residual correlation parameters. For compactness, table 1 only lists Model 1 estimates for variables selected by the AIC, even though all regressors are included. Finally, PM10 correlates (.95) strongly with PM2 _._ 5 and the AIC favored PM2 _._ 5. Replacing it with PM10 in the regressions below led to a small deterioration in measures of fit, indicating that PM2 _._ 5 is a statistically preferred predictor, although the main conclusions do not depend on this. For simplicity, the focus remains on the PM2 _._ 5 data.

Table 1: Dependent Variable: Confirmed COVID-19 cases <u>per</u> 100,000 inhabitants.

|**Variable**|**Model 1**|**Model 2**|**Model 3**|**Model 4**|**Model 5**|**Model 5b**|
|---|---|---|---|---|---|---|
|(Intercept)|-359.58|-402.51***|-202.58***|-200.63***|-207.50***|-185.43***|
||(218.20)|(80.61)|(76.06)|(64.11)|(74.59)|(71.75)|
|Population density|-6.28**|-6.54***|-0.03|-1.10|-0.48|-0.48|
||(2.58)|(2.38)|(2.05)|(1.87)|(2.06)|(1.94)|
|Share 25 to 44|3.55|2.62*|-0.80|0.47|-0.37|-.41|
||(2.17)|(1.34)|(1.05)|(1.05)|(1.07)|(1.02)|
|Share above 65|5.58*|4.22***|2.12**|2.14*|2.08**|1.07*|
||(2.26)|(1.27)|(1.03)|(1.00)|(1.05)|(1.00)|
|Share unmarried|4.94***|4.78***|4.01***|3.28***|3.83***|3.59***|
||(1.33)|(0.97)|(0.96)|(0.78)|(0.94)|(0.89)|
|Share single household|-4.02***|-2.17***|-1.62***|-1.50***|-1.59***|-1.70***|
||(1.47)|(0.57)|(0.49)|(0.45)|(0.49)|(0.46)|
|Share non-western immigrants|-1.32**|-1.23**|-0.57|-0.78**|-0.77*|-0.58|
||(0.57)|(0.48)|(0.43)|(0.38)|(0.42)|(0.40)|
|Share of water surface|17.58|16.11|11.30|13.56*|13.53|11.53*|
||(11.18)|(10.28)|(9.62)|(8.07)|(9.21)|(8.71)|
|Share with long-term illness|1.10|1.19|0.41|0.72|0.64|0.97|
||(1.00)|(0.76)|(0.92)|(0.60)|(0.79)|(0.74)|
|Case severity||||||-0.065***<br>(.01)|
|Mean PM2_._5|10.17**|10.84***|6.21***|3.52***|4.91**|4.47***|
||(4.66)|(1.48)|(2.82)|(1.22)|(2.44)|(2.31)|
|_λ_|||0.71***||0.42*|0.39|
||||(0.26)||(0.26)|(0.25)|
|_ρ_||||0.68***|0.43|0.50**|
|||||(0.25)|(0.25)|(0.21)|
|R2|0.24|0.22|0.52|0.51|0.50|0.55|
|AICc|3414.85|3391.40|3274.36|3274.12|3269.31|3237.91|

Standard Errors in parenthesis, significance levels as:<sup>***</sup> _p <_ 0 _._ 01,<sup>**</sup> _p <_ 0 _._ 05,<sup>*</sup> _p <_ 0 _._ 1 In the non-spatial regressions, the correlation with population density is negative and significant, suggesting that the case density is on average lower in densely populated areas. This could reflect mis-specified scaling. However, in the models that control for spatial clustering, population density is not significant. This suggests that, after controlling for spatial clustering, the spatial variation in case density is not related to population density patterns. Instead, the share of unmarried and the share of single households, which relate to the number of households in a given population and the type of social networks that they 7