Mobility-based SIR model for complex networks: with case study Of COVID-19

Abstract

In the last decade, humanity has faced many different pandemics such as SARS, H1N1, and presently novel coronavirus . On one side, scientists have developed vaccinations, and on the other side, there is a need to propose models that can help in understanding the spread of these pandemics as it can help governmental and other concerned agencies to be well prepared, especially for pandemics, which spreads faster like COVID-19. The main reason for some epidemic turning into pandemics is the connectivity among different regions of the world, which makes it easier to affect a wider geographical area, often worldwide. Also, the population distribution and social coherence in the different regions of the world are non-uniform. Thus, once the epidemic enters a region, then the local population distribution plays an important role. Inspired by these ideas, we propose two versions of our mobility-based SIR model, (i) fully mixed and (ii) for complex networks, which especially takes into account real-life interactions. To the best of our knowledge, this model is the first of its kind, which takes into account the population distribution, connectivity of different geographic locations across the globe, and individuals' network connectivity information. In addition to presenting the mathematical proof of our models, we have performed extensive simulations using synthetic data to demonstrate the generalization capability of our models. Finally, to demonstrate the wider scope of our model, we applied our model to forecast the COVID-19 cases at county level (Estonia) and regional level (Rhône-Alpes region in France).

On the Coevolution Between Social Network Structure and Diffusion of the Coronavirus (COVID-19) in Spatial Compartmental Epidemic Models

“…A last, possibly interesting, extension of the model concerns the introduction of mobility. Following insights from recent literature blending epidemic models on networks with agent mobility (Feng et al, 2020;Goel et al, 2021;Huang and Chen, 2022), one might consider separating geographical proximity and social ties. In the foregoing model, those two dimensions are intertwined and individuals always interact with a set of neighbors that may change over time due to quarantines but is initially predetermined.…”

Mitigating biological epidemic on heterogeneous social networks

“…A solution for the spread of STDs on bipartite graph of men and women is also presented. The work in [31] presents a mobility based SIR model for complex network that is used to forecast ongoing Covid-19 pandemic cases at country and regional level.…”

Mitigating virus spread through dynamic control of community-based social interactions for infection rate and cost

“…Describing a Bayesian heterogeneity learning approach, the SIR model is formulated into a hierarchical structure in Hu and Geng (2020). In Goel et al (2021), a mobility-based variant of the SIR model is introduced to take population distribution and the relationship between different geographical locations into account. The authors in Giordano et al (2020) consider various stages of infections; they take into account both diagnosed and non-diagnosed infected individuals to account for the role of asymptomatic infection in epidemic spreading.…”