Starting with the power law for the total number of infections during the middle stages of epidemics , we propose differential equations describing the process of momentum epidemic management , which is a set of measures aimed at reducing the epidemic spread via timely response to the dynamic of the number of infections. In the most aggressive mode, the saturation of the number of infections can be achieved sufficiently quickly. Our 2-phase formula matches very well the  total numbers of infections for Covid-19  in many countries; modeling the first phase is based on Bessel functions.  We discuss Austria,  Germany, Japan,  Israel, Italy, the Netherlands, Sweden, Switzerland, UK, and the USA  .
Starting with the power law for the total number of detected infections , we propose differential equations describing the effect of momentum epidemic management . Our 2-phase formula matches very well the curves of the total numbers of the Covid-19 infection in many countries; the first phase is described by Bessel functions. It provides projections for the saturation, assuming that the management is steady. We discuss Austria, Brazil, Germany, Japan, India, Israel, Italy, the Netherlands, Sweden, Switzerland, UK, and the USA , including some analysis of the second waves .
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infections during the middle stages of epidemics
detected infections
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process
effect
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, which is a set of measures aimed at reducing the epidemic spread via timely response to the dynamic of the number of infections. In the most aggressive mode, the saturation of the number of infections can be achieved sufficiently quickly.
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curves of the
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infections for
the
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infection
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modeling
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based on
described by
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It provides projections for the saturation, assuming that the management is steady.
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Brazil,
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India,
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, including some analysis of the second waves