Alternative to panel data regression for testing covariates

I have a balanced panel data set containing variable y = daily numbers of COVID-19 reported cases at the municipal level over a 3-month period. I want to know if another daily-measured variable (x) is related to the local COVID situation.

I also have several time-invariant variables (ie, sd = sociodemographic variables) and some variables that vary in time but are constant across areas (ie, cn = the number of reported cases in the whole country). In a classic situation I would perform an OLS linear regression such as

lm(formula = y ~ x + sd + cn, data = df)

However this is a panel dataset. I suppose I could perform a random effects model to assess the influence of local reported cases on my dependent variable, however I also would like to assess the role of the other variables. What would be an alternative to panel data regression?