Modelling the impact of mobility on spreading dynamics

The world population increased from 4 billion people in 1974 to 7.8 billion people in 2020 and continues to grow rapidly. Today, 55% of the world's population live in urban areas, a proportion that is expected to increase to 68% by 2050 according to the United Nations. The migration from rural to urban areas coupled with an increasingly mobile population forms a complex contact network that favours the rapid and large-scale spread of infectious diseases. The recent outbreak of coronavirus disease has demonstrated that physical human interactions and modern movement paradigms are the principle drivers for the rapid spatial spread of infectious diseases. Thus, modelling the impact of mobility is crucial to understand the underlying dynamics of the spreading process and consequently to develop effective containment and control strategies. Spreading processes have been widely studied in various different contexts. These processes mainly include the propagation of diseases, rumours and information across a given population network. However, the complexity of human mobility and the limitations on data sources recording movement create challenges for spread modellers. For instance, a main constraint is the use of data that doesn't accurately capture real physical contacts between individuals. Numerous studies considered grouping people based on one of their mobility aspects to identify influential behaviours responsible for spreading diseases through a network. A prominent example is the higher likelihood of individuals who visit new locations to drive the spread compared to those who visit the same locations. However, addressing this topic while considering a single mobility aspect is not sufficient. In fact, there is a great dependency of how far people move or how much they come in contact with others during their travels. So far, previous work has not incorporated multiple mobility aspects simultaneously. We propose a novel classification technique that divides a population into different mobility groups based on ...