Does climate help modeling COVID-19 risk and to what extent?

magnitude of their regression coefficients is comparable to the standard error (e.g., PM2.5 and PM10). Rainfall seems to have significance only at shorter moving averages (5 days). For the remaining meteorological factors, different moving averages (5, 7, 10, 12, and 14 days), which relate climatic variables to incubation periods of diverse duration, do not seem to influence the overall result of the econometric analysis. Testing for the added effect of intervention policies requires a more in-depth analysis. The enactment of restrictions and the pandemic peak tend to vary from country to country due to the diversity, severity, and enforcement ability of the containment policies implemented. For this reason, we limit our analysis to the OxCGRT stringency and containment & health indices which allow us to capture the general level of restriction without focusing on the intervention policies of each government. Moreover, most countries have applied distinct levels of restric- tions at a much deeper level of granularity than what the available data would allow us to test for (city, province, or regional level). Therefore, we only present an analysis of the north Amer- ican region including all the US states and Canadian territories, for which OxCGRT provides the highest level of detail. Table 5 provides the results of panel data analysis on the impact of stringency and contain- ment levels for COVID-19 and climatic factors. To evaluate the impact of the stringency factor, we created the two variables high_stringency and high_containment that take values equal to 1

when stringency and the containment & health are above 60% (median value) and 0 otherwise. We separately test the lagged effect of these two factors at 7 and 14 days as expressed by the numerical suffix associated with the two variables in Table 5. We find that high levels of clo- sure-type restrictions show significant effects on limiting COVID-19 spread only after about two weeks from their introduction (P < 0.01). Conversely, the containment & health index presents a strong negative regression coefficient at both 7 and 14 days from introduction (P < 0.01). This may be related to the added efficacy of combining different health and preven- tion policies (public info campaigns, PCR testing, contact tracing, and facial coverings) to Table 3. Results of the Fisher-type unit-root test analysis based on augmented Dickey-Fuller tests at 0 and 1 lag for each variable considered in the econometric study. I(0) 0 Lags I(1) 1 Lag Inverse chi- squared Inverse normal Inverse logit t Modified inv. chi- squared Inverse chi- squared Inverse normal Inverse logit t Modified inv. chi- squared P Z L� Pm P Z L� Pm Daily cases (log) 9101.3 -74.1 -106.8 171.2 5631.1 -53.5 -67.5 101.6 Temperature_7 1868.7 -14.4 -14.2 16.5 3157.1 -33.9 -35.7 47.0 Absolute Humidity_7 1912.3 -15.2 -14.8 17.4 3546.0 -36.7 -40.4 55.6 Pressure_7 3112.6 -29.9 -31.9 43.1 8835.9 -69.5 -106.1 172.4 Wind speed_7 4163.5 -40.5 -46.5 65.6 7312.3 -62.4 -87.8 138.8 Rainfall_7 4974.1 -43.9 -54.1 82.9 6797.6 -57.4 -80.1 127.4 Short-wave irradiation_7

2599.7 -26.5 -26.9 32.2 3156.0 -34.0 -36.0 47.0 PM2P5_7 3763.2 -36.7 -40.9 57.1 8004.9 -65.7 -96.0 154.0 PM10_7 3765.8 -36.2 -40.9 57.1 7778.1 -63.4 -92.9 149.0 UV_7 2516.5 -25.4 -25.7 30.4 3184.9 -33.6 -36.2 47.7 High_stringency_7 3333.0 -40.3 -50.0 73.7 2805.7 -36.5 -43.6 62.9 High_containment_7 4123.7 -45.5 -59.62 90.4 3302.7 -39.9 -50.2 74.4 For each test, we report inverse chi-squared, inverse normal, inverse logit t, and modified https://doi.org/10.1371/journal.pone.0273078.t003 PLOS ONE Does climate help modeling COVID-19 risk and to what extent? PLOS ONE | https://doi.org/10.1371/journal.pone.0273078 September 7, 2022 18 / 27