In this post, I introduce the R package flowmapblue (http://flowmap.blue) to easily map mobility data. I show an example of a map of population flows in Spain derived from cellphone location data. These data have been obtained from the National Institute of Statistics of Spain. flowmapblue allows us to create an interactive mobility map by using just a few lines of code: Installation First, we need to install flowmapblue from GitHub as follows:
In this post I show how to use the R package epiflows for risk assessment of travel-related spread of disease. Infectious diseases can spread beyond national borders. Residents of an infectious location can travel abroad and infect people living there. Also, travelers can acquire a disease while staying in a foreign country, and then seed new outbreaks in their home country after their return.
In this post I present SpatialEpiApp, a Shiny web application for the analysis of spatial and spatio-temporal disease data. SpatialEpiApp integrates two of the most common approaches in public health surveillance: disease risk mapping and detection of clusters. The application allows to fit Bayesian disease models to obtain risk estimates and their uncertainty by using INLA, and to detect clusters by using the scan statistics implemented in SaTScan. SpatialEpiApp allows user interaction and creates interactive visualizations by using the R packages Leaflet for rendering maps, dygraphs for plotting time series, and DataTables for displaying data objects.