Data visualization for time series in environmental epidemiology

 

Data visualization has become an integral part of statistical modelling. Exploratory graphical analysis allows insight into the underlying structure of observations in a data set, and graphical methods for diagnostic purposes after model fitting provide insight into the fitted model and its inadequacies. In this paper we present visualization methods for preliminary exploration of time series data and graphical diagnostic methods for modelling relationships between time series data in medicine. We will use exploratory graphical methods to better understand the relationship between a time series response and a number of potential covariates. Graphical methods will also be used to examine any remaining information in the residuals from these models. For illustrative purposes, we will apply exploratory graphical methods to a time series data set which consists of daily counts of hospital admissions for asthma and pollution and climatic variables. We provide an overview of the most recent and widely applicable data visualization methods for portraying and analyzing epidemiological time series.