Abstract
Modeling the movements of humans and animals is critical to understanding the transmission of infectious diseases in complex social and ecological systems. In this paper, we focus on the movements of pastoralists in the Far North Region of Cameroon, who follow an annual transhumance by moving between rainy and dry season pastures. Describing, summarizing, and modeling the transhumance movements in the region are important steps for understanding the role these movements may play in the transmission of infectious diseases affecting humans and animals. We collected data on this transhumance system for four years using a combination of surveys and GPS mapping. An analysis on the spatial and temporal characteristics of pastoral mobility suggests four transhumance modes, each with its own properties. Modes M1 and M2 represent the type of transhumance movements where pastoralists settle in a campsite for a relatively long period of time (≥20 days) and then move around the area without specific directions within a seasonal grazing area. Modes M3 and M4 on the other hand are the situations when pastoralists stay in a campsite for a relatively short period of time (<20 days) when moving between seasonal grazing areas. These four modes are used to develop a spatial-temporal mobility (STM) model that can be used to estimate the probability of a mobile pastoralist residing at a location at any time. We compare the STM model with two reference models and the experiments suggest that the STM model can effectively capture and predict the space-time dynamics of pastoral mobility in our study area.
Highlights
Humans and animals constantly move from one place to another
We describe movements in these two modes using the Brownian bridge motion model (BBMM), a continuous time movement model with the probability of an individual being at a location related to the two end points of the path during a specified period of time from t = 0 to T, where T is the total number of days in M3 or M4 [45, 52]
0.838 0.900 0.790 0.710 0.502 0.778 0.913 0.854 0.664 0.783 0.668 0.553 0.814 0.835 0.594 0.903 0.689 0.786 0.612 0.660 0.911 doi:10.1371/journal.pone.0131697.t005. Distribution of such length [58,59,60,61]. Using this type of models, regularity of human mobility has been found in different kinds of data sets such as mobile phone users, taxicabs on street networks, and social media check-ins [62,63,64]
Summary
Humans and animals constantly move from one place to another. Modeling such movements is critical for understanding the transmission of infectious diseases in complex social-ecological systems [1,2,3,4,5]. In this research we focus on a special human mobility system called.
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