Epidemic spreading on modular networks: The fear to declare a pandemic
Abstract
In the past few decades, the frequency of pandemics has been increased due to the growth of urbanization and mobility among countries. Since a disease spreading in one country could become a pandemic with a potential worldwide humanitarian and economic impact, it is important to develop models to estimate the probability of a worldwide pandemic. In this paper, we propose a model of disease spreading in a structural modular complex network (having communities) and study how the number of bridge nodes n that connect communities affects disease spread. We find that our model can be described at a global scale as an infectious transmission process between communities with global infectious and recovery time distributions that depend on the internal structure of each community and n . We find that near the critical point as n increases, the disease reaches most of the communities, but each community has only a small fraction of recovered nodes. In addition, we obtain that in the limit n →∞ , the probability of a pandemic increases abruptly at the critical point. This scenario could make the decision on whether to launch a pandemic alert or not more difficult. Finally, we show that link percolation theory can be used at a global scale to estimate the probability of a pandemic since the global transmissibility between communities has a weak dependence on the global recovery time.
 Publication:

Physical Review E
 Pub Date:
 March 2020
 DOI:
 10.1103/PhysRevE.101.032309
 arXiv:
 arXiv:1909.09695
 Bibcode:
 2020PhRvE.101c2309V
 Keywords:

 Physics  Physics and Society;
 Quantitative Biology  Populations and Evolution
 EPrint:
 Phys. Rev. E 101, 032309 (2020)