How the individual human mobility spatio-temporally shapes the disease transmission dynamics

Abstract

Human mobility plays a crucial role in the temporal and spatial spreading of infectious diseases. During the past few decades, researchers have been extensively investigating how human mobility affects the propagation of diseases. However, the mechanism of human mobility shaping the spread of epidemics is still elusive. Here we examined the impact of human mobility on the infectious disease spread by developing the individual-based SEIR model that incorporates a model of human mobility. We considered the spread of human influenza in two contrasting countries, namely, Belgium and Martinique, as case studies, to assess the specific roles of human mobility on infection propagation. We found that our model can provide a geo-temporal spreading pattern of the epidemics that cannot be captured by a traditional homogenous epidemic model. The disease has a tendency to jump to high populated urban areas before spreading to more rural areas and then subsequently spread to all neighboring locations. This heterogeneous spread of the infection can be captured by the time of the first arrival of the infection $$(T_{fi} )$$(Tfi), which relates to the landscape of the human mobility characterized by the relative attractiveness. These findings can provide insights to better understand and forecast the disease spreading.

How do social media and individual behaviors affect epidemic transmission and control?

“…These results emphasize the importance of reducing agents' mobility (corresponding to travel restriction and quarantine) and decreasing their risk tolerance threshold (corresponding to public education to promote behavioral changes) to curb the spread of disease. This is evidenced by the recent study showing the benefit of reducing people's mobility and social distancing in COVID-19 control ( Aleta et al, 2020 ; Changruenngam et al, 2020 ; Gatto et al, 2020 ; Kraemer et al, 2020 ; Tian et al, 2020 ; Wells et al, 2020 ; Zhang et al, 2020 ). Similar analysis can be conducted for influencing agents' opinions on epidemic risk and adoption of preventive behaviors ( Fig.…”

Estimating the COVID-19 Spread Through Real-time Population Mobility Patterns: Surveillance in Low- and Middle-Income Countries

“…In addition, our regional stratified analysis showed consistent findings with the global one, except from the region of Africa. Taking into account that human mobility is a complicated concept (and at this point is analyzed collectively by using crowdsource data), we may hypothesize that individuals’ behavior (use of face mask) [ 32 ], people’s dynamic network [ 33 ] when visiting these places, and seasonality could be potential explaining mechanisms of virus spread [ 35 , 37 ]. The aforementioned findings could guide stakeholders on specific social distancing implementations and enforcement planning [ 38 ].…”

Competitive evolution of H1N1 and H3N2 influenza viruses in the United States: A mathematical modeling study

“…Moreover, individual heterogeneity in disease transmission can also result in a higher probability of stochastic extinction 77 . Finally, we did not consider human mobility in our model; incorporating these data into the model could improve a more realistic model 78,79 . It is worth stressing that the knowledge of the evolutionary change of the influenza virus is necessary to be investigated.…”