TY - JOUR
T1 - Discontinuous transitions of social distancing in the SIR model
AU - Arazi, R.
AU - Feigel, A.
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/3/15
Y1 - 2021/3/15
N2 - 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 economic 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.
AB - 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 economic 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.
UR - http://www.scopus.com/inward/record.url?scp=85098452219&partnerID=8YFLogxK
U2 - 10.1016/j.physa.2020.125632
DO - 10.1016/j.physa.2020.125632
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AN - SCOPUS:85098452219
SN - 0378-4371
VL - 566
JO - Physica A: Statistical Mechanics and its Applications
JF - Physica A: Statistical Mechanics and its Applications
M1 - 125632
ER -