I am working on an epidemiological model with disease incidence rate as dependent variable and environmental factors as independent variable. Disease data is available at county scale whereas environmental data is in raster format that can be re-sampled as needed. Scale plays a major role when we talk about modeling as a model may be good at one scale and not so good at the other. For this reason I think conducting sensitivity analysis may be the right option. My question here is how to approach based on the data available for study. Any ideas, links to articles or journals is much appreciated.
Since your disease data is aggregated at the county level, I'd start with exploratory analysis using this aggregation.
Take the polygon boundaries of your counties and create set of variables from zonal statistics tool (R, QGIS, ArcGIS and others can do that for you). Mean/median values would be probably a starting point but min/max values could also be useful.
In the next step, you would have to find a way to build a model linking disease incidence to explanatory variable. Simple descriptive stats and correlations and OLS are good way to start.
At next stage you might want to take into account spatial dependency in your data. Do some ESDA using GeoDa software, R, ArcGIS - smooth the rates for counties with small number of cases, look at global and local indicators of spatial autocorrelation.
If spatial dependency is an issue - next steps would require some serious thinking how to account for that. Geographically weighted regression, multilevel modelling or Bayesian methods (have a look at INLA package) will be your friends here.