The 1st wave of COVID-19 changed social distancing around the globe: severe lockdowns to stop pandemics at the cost of state economies preceded a series of lockdown lifts. To understand social distancing dynamics it is important to combine basic epidemiology models for viral unfold (like SIR ) with game theory tools, such as a utility function that quantifies individual or government forecast for epidemic damage and economy cost as the functions of social distancing. Here we present a model that predicts a series of discontinuous transitions in social distancing after pandemics climax . Each transition resembles a phase transition and, so, maybe a general phenomenon.  Data analysis of the first wave in Austria, Israel, and Germany corroborates the soundness of the model. Besides , this work presents analytical tools to analyze pandemic waves  .
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 .
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The 1st wave of COVID-19 changed social distancing around the globe: severe lockdowns to stop pandemics at the cost of state economies preceded a series of lockdown lifts. To understand social distancing dynamics it is important
To describe the dynamics of social distancing during pandemics, we follow previous efforts
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for viral unfold (like SIR
(e.g. SIR - Susceptible, Infected, and Recovered
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theory tools, such as a utility function that quantifies individual or government forecast for epidemic damage and economy cost as the functions of social distancing. Here we present a
and economy theory tools. We present an extension of the SIR
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after pandemics climax
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and, so, maybe
of the second-order (Ginzburg-Landau instability) and, therefore, potentially
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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.
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Besides
Furthermore
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, 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