With the help of mass media, people receive information concerning the status of an infectious disease to guide their mobility. Herein, we develop a theoretical framework to investigate the safety-information-driven human mobility with metapopulation epidemic dynamics. Individuals respond to the safety information of a city by taking safe moves (passing cities with a more number of healthy individuals) or unsafe moves (passing cities with a less number of healthy individuals). Our findings show that the critical threshold depends on mobility in such a way that personal execution of safe moves unexpectedly promotes the global spread of a disease, while unsafe moves counterintuitively cause a locally, relatively small outbreak size. Our analysis underlines the role of safety consideration in the spatial spread of an infectious disease with clear implications for the model of mobility driven by individuals' benefit. uring the past few years, reaction-diffusion processes have been successfully applied to modeling the spread of an epidemic with the considerations of individuals' mobility patterns . Since human travel is responsible for the spatial spread of human infectious diseases, understanding human movement patterns is crucial to the modeling of metapopulation epidemic spreading. Fortunately, data on human mobility collected at a global scale 22, 23 , such as the world-wide air-transportation network 22, 24 and at a local scale, such as bank-note circulation in the United States 25, 26 , US commuting network data 27 , and mobile-phone data 28, 29 have been broadly recorded. Consequently, mathematical modeling of the impacts of recurrent mobility patterns 22, 27, 30 and natural human mobility patterns 31-33 on the epidemic process has been greatly explored. In the event of an epidemic outbreak, individuals often collect information concerning the on-going status of an infectious disease and utilize it to guide their mobility patterns [34] [35] [36] . People might prefer to stay at home rather than travel if they are aware of the seriousness of the infection; if their travel plans cannot be canceled, they might prefer to bypass infectious areas and visit only those areas that appear relatively safe with a more number of healthy individuals (we call this ''safe moves''). This personal consideration of safe moves may have a profound effect on the spreading process, because the status of an epidemic in a city affects individuals' choice of mobility destination, and individuals' movement to a city further affects the epidemic spread there. Therefore, the interaction of the human mobility and the spreading process is a kind of coevolution dynamical process. Given the important relevance of safe moves to the spreading process, we investigate the impact of human mobility driven by safety information on the epidemic process. Resorting to mathematical modeling with the metapopulation approach, we consider two possible scenarios of human mobility that encompass most of real safety information situations: (i) safe moves, and (ii) unsafe moves (we will explain it later), quantified by a set of parameters. Scenario (ii) is an inverse virtual case of safe moves. Although this assumption is somehow opposite to our intuition because individuals usually take safe moves for their own benefits, it may help us comprehensively understand the impact of different kinds of human mobility on the epidemic behavior by comparing different situations. We also have to stress that the fitting of the model parameters to practical data is beyond the scope of the present work. We find that the invasion threshold (the critical population density r 0c in a nonlimited transmission pattern 4 ) alters with mobility parameters caused by safety consideration. Specifically, safe moves unexpectedly promote the global spread of a disease by lowering the critical mobility rate and increasing the outbreak size; while unsafe moves counterintuitively prevent the disease spread and reduce the outbreak size. The results underline the crucial role of human mobility in the spatial spread of a human disease.
The mobility of the human can be based on the status of the number of infected individuals in that location REF .
Safety-Information-Driven Human Mobility Patterns with Metapopulation Epidemic Dynamics
Scientific Reports
Scientific Reports
Medicine