[R-sig-Geo] Basic questions about Bayesian Spatio-temporal Analysis-INLA

Quiner, Claire CQuiner at bloodsystems.org
Tue Oct 10 01:35:30 CEST 2017

Hi Virgilio,
Thank you for your response and wealth of resources that you sent!
I had originally planned on using the raster files as covariates (went to great pains to get them!) but was swayed away from that approach at some point.

Here is what I have: a number raster stacks of various climatic and geophysical data across a country. Each stack is in the same resolution and each layer represents either the mean, min, max or median of that variable, for a given week. There is a layer for each week of the year, for each variable. As the outcome, I have weekly counts of an infectious disease, aggregated across each county (admin 2) of that country, in each week of the year (weeks that disease counts are aggregated into match the weeks that climatic data is assembled into).
I want my model to predict if there is an association of these climatic variables and the risk of the disease (disease count normalized by the number of residents in that county) and if this association various by different parts of the country...ie different climatic predictors for coastal vs inland. Further, I am interested in measuring if there is a time lag that is predictive of risk increase: i.e. x mm of precipitation predicts an increase in risk of disease 2 weeks later.  

Based on your resources, I believe that a space time geostatistical model would help me to answer these questions- although it is unclear to me if this would work since my outcome is aggregated counties and not points.
Any thoughts on this?
Thank you!

-----Original Message-----
From: VIRGILIO GOMEZ RUBIO [mailto:Virgilio.Gomez at uclm.es] 
Sent: Sunday, October 08, 2017 12:18 AM
To: Quiner, Claire
Cc: r-sig-geo at r-project.org
Subject: Re: [R-sig-Geo] Basic questions about Bayesian Spatio-temporal Analysis-INLA

Hi Claire,

Not sure what type of model or data you are trying to fit. If you have raster data, it would make sense to use them as covariates and not as priors. If you definitely want to fit a spatio-temporal model with INLA  you should check this book:


Also, please check these course materials that I prepared for the GEOSTAT 2017 summer school about spatial model fitting with INLA:


In a nutshell, the inla() function works similarly as the glm() or gam() functions: you define your model in a formula (which may include random effects) and use a data.frame to pass the data.

Hope this helps.


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