movement-package: Modelling and Analysing Movement Data for Epidemiology

In SEEG-Oxford/movement: Modelling and Analysing Human Movement Data

Modelling and Analysing Movement Data for Epidemiology

Description

Movement of humans and animals has a crucial role in the epidemiology of a number of diseases. Movement data is increasingly available to epidemiologists and its incorporation in models and maps of disease is increasingly popular. This package is a collaborative effort to improve our ability to analyses movement data and to build and apply epidemiological movement models.

Note

The most common use of the package is to parameterize a movement model based on observed population movements, and then use this model to predict _de novo_ population movements. Code to fit such a model might look like this:

m <- movement(observed_movement ~ location_data, model = radiationWithSelection())

where observed_movement is a movement_matrix object containing observations about movements between pairs of locations, location_data is a location_dataframe object containing the coordinates and populations of those locations, and radiationWithSelection() creates a flux object, representing the type of movement model to by fitted. Current supported movement models are: radiationWithSelection, originalRadiation, gravity, gravityWithDistance, interveningOpportunities and uniformSelection.

The movement model fits the parameters of the specified movement model, and returns a movement_model object. This object can be plotted (plot(m)), or used to predict to populations movements to new location_dataframe object (prediction <- predict(m, location_data)), or even a RasterLayer object giving populations in each cell (prediction <- predict(m, raster)).

Author(s)

Nick Golding, Andrew Schofield, Moritz Kraemer and Alex T. Perkins Maintainer: Nick Golding <nick.golding.research at gmail.com>

See Also

movement predict.flux, predict.movement_model plot.movement_predictions, getNetwork, kenya,

SEEG-Oxford/movement documentation built on April 17, 2023, 4:17 p.m.