Stochasticity of disease spreading derived from the microscopic simulation approach for various physical contact networks

Yuichi Tatsukawa, Md Rajib Arefin, Shinobu Utsumi, Kazuki Kuga, Jun Tanimoto

Research output: Contribution to journalArticlepeer-review

Abstract

COVID-19 has emphasized that a precise prediction of a disease spreading is one of the most pressing and crucial issues from a social standpoint. Although an ordinary differential equation (ODE) approach has been well established, stochastic spreading features might be hard to capture accurately. Perhaps, the most important factors adding such stochasticity are the effect of the underlying networks indicating physical contacts among individuals. The multi-agent simulation (MAS) approach works effectively to quantify the stochasticity. We systematically investigate the stochastic features of epidemic spreading on homogeneous and heterogeneous networks. The study quantitatively elucidates that a strong microscopic locality observed in one- and two-dimensional regular graphs, such as ring and lattice, leads to wide stochastic deviations in the final epidemic size (FES). The ensemble average of FES observed in this case shows substantial discrepancies with the results of ODE based mean-field approach. Unlike the regular graphs, results on heterogeneous networks, such as Erdős–Rényi random or scale-free, show less stochastic variations in FES. Also, the ensemble average of FES in heterogeneous networks seems closer to that of the mean-field result. Although the use of spatial structure is common in epidemic modeling, such fundamental results have not been well-recognized in literature. The stochastic outcomes brought by our MAS approach may lead to some implications when the authority designs social provisions to mitigate a pandemic of un-experienced infectious disease like COVID-19.

Original languageEnglish
Article number127328
JournalApplied Mathematics and Computation
Volume431
DOIs
Publication statusPublished - Oct 15 2022

All Science Journal Classification (ASJC) codes

  • Computational Mathematics
  • Applied Mathematics

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