# Bayesian regression with spatio-temporal varying coefficients

## TL;DR

This paper introduces a Bayesian spatio-temporal regression model with a novel dependent process prior to analyze how climate variables affect disease morbidity across different regions and times in Mexico.

## Contribution

It proposes a new Bayesian spatio-temporal dependent process prior and demonstrates its ability to model varying climate effects on disease incidence.

## Key findings

- Climate effects vary across space and time.
- The model effectively captures spatio-temporal dependence.
- Results quantify changing climate impacts on diseases.

## Abstract

To study the impact of climate variables on morbidity of some diseases in Mexico, we propose a spatio-temporal varying coefficients regression model. For that we introduce a new spatio-temporal dependent process prior, in a Bayesian context, with identically distributed normal marginal distributions and joint multivariate normal distribution. We study its properties and characterise the dependence induced. Our results show that the effect of climate variables, on the incidence of specific diseases, is not constant across space and time and our proposed model is able to capture and quantify those changes.