Inference Modeling for COVID Data

I am trying to build a model to analyze the relationship between COVID-19 mortality rate in each U.S. state or county (y) and independent variables (x) including: Vaccination rate: 1st, 2nd, booster COVID-19 policies: mask mandate, vaccine mandate, social distancing, quarantine mandate Amount of healthcare resources: number of doctor / 1k population, % GDP spent on health Need to control for: Demographics: sex, race, income, age Major health conditions that increase COVID-19 mortality: cardio-pulmonary diseases, diabetes, obesity, age I am struggling to select the appropriate model. I don't know how to account for the time component of the pandemic, i.e. the 4 COVID waves, in an inference model. I have thought of some modeling ideas, but they all seem to have some issues: Linear model Pick one point in time, e.g. peak of the 2nd wave, do a linear model between the dependent and independent variables. Observational unit: state or county. Can also pick one period of time (e.g. the entire 2nd COVID wave) and aggregate the predictors for each state/county. Issue: Linear models assume independence between data points but COVID mortality at a given time point is likely related to that from its preceding time point(s) Time series model Issue: Time series models are made for predictions, but I would like to make inferences. Causal Impact analysis: would enable me to estimate the impact of an intervention Issues: (1) I have multiple interventions/mandates that I want to assess, and (…