2020/08/31 by R. Arazi, Alexander Feigel, A. Feigel
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · Physics and Astronomy · Psychology · #COVID-19 epidemiological studies #Complex Network Analysis Techniques #Computer science #Context (archaeology) #Coronavirus disease 2019 (COVID-19) #Demography #Economics #Epidemic model #Geography #Globe #Mathematical and Theoretical Epidemiology and Ecology Models #Medicine #Pandemic #Population #Positive economics #Psychology #Social distance #Sociology #Soundness #physics.soc-ph #q-bio.PE
paper · pdf · doi:10.1016/j.physa.2020.125632
version 2, 3 figures added, introduction/model explanation are extended, no change of results
arxiv created 2020/11/25 · openalex publication_date 2020/12/09 · arxiv updated 2021/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
To describe the dynamics of social distancing during pandemics, we follow previous efforts to combine basic epidemiology models (e.g. SIR - Susceptible, Infected, and Recovered) with game and economy theory tools. We present an extension of the SIR model that predicts a series of discontinuous transitions in social distancing. Each transition resembles a phase transition of the second-order (Ginzburg-Landau instability) and, therefore, potentially a general phenomenon. The first wave of COVID-19 led to social distancing around the globe: severe lockdowns to stop the pandemic were followed by a series of lockdown lifts. Data analysis of the first wave in Austria, Israel, and Germany corroborates the soundness of the model. Furthermore, this work presents analytical tools to analyze pandemic waves, which may be extended to calculate derivatives of giant components in network percolation transitions and may also be of interest in the context of crisis formation theories.