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

regression intercept, while βn represents the regression slope coefficient of each respective regressor. We include a vector of cross-sectional unit fixed effects ci to account for all time- invariant factors across a location that affect the local growth rate of infections, such as differ- ences in demographics, socioeconomic status, culture, and health systems. This is an impor- tant feature since it allows to partial out heterogeneous omitted factors that might be correlated with the dynamics of contagion and the daily cases. We also include a vector of (daily) time fixed effects λt to absorb the autoregressive component specific to the COVID-19 spread growth and to account for the presence of any potential seasonal bias. Since the effect of variables that behave as time-invariant factors for the period of focus (e.g., socioeconomic, environmental, and some global health indicator variables) would be absorbed in the intercept for collinearity due to the use of time fixed effect regression, these variables were omitted from the analysis. Finally, we cluster the standard errors ui,t at the entity-level to account for error correlation within each location. ln daily casesi;t ¼ b0 þ X N n¼1 bn Explanatory variablesn;i;t þ ci þ lt þ ui;t ð1Þ Fig 3. Examples of modeling performance for the optimized GBRT model. https://doi.org/10.1371/journal.pone.0273078.g003 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 11 / 27