Starting with the power law for the 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 of any nature aimed at reducing the epidemic spread via timely response to the dynamic of the number of infections. One of the finding is that the aggressive imposing the most significant measures followed by equally aggressive reducing them when the spread diminishes will effectively reduce the number of new infections to zero . We use the asymptotic periodicity of Bessel functions , which readily provides the required; but the epidemic may resume . Practically: the hesitation before enabling some protection measures must be at least doubled before disabling them. Also, the square root of the intensity of hard measures is  inversely proportional to the epidemic duration , which works surprisingly well practically; a couple of examples is given .
Starting with the power law for the 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 of any nature aimed at reducing the epidemic spread via timely response to the dynamic of the number of infections. One of the finding is that the aggressive imposing the most significant measures followed by equally aggressive reducing them when the spread diminishes will effectively reduce the number of new infections to zero ; we use the theory of Bessel functions . Epidemics may resume after such saturation . Practically: the hesitation before enabling some protection measures must be at least doubled before disabling them. Also, the square root of the intensity of hard measures is qualitatively inversely proportional to the epidemic duration . At least for Covid-19, Bessel functions appeared surprisingly efficient for modeling the whole period of intensive spread; we discuss the USA, and other places .
R
. We use the asymptotic periodicity
; we use the theory
R
, which readily provides the required; but the epidemic may resume
. Epidemics may resume after such saturation
A
qualitatively
R
, which works surprisingly well practically; a couple of examples is given
. At least for Covid-19, Bessel functions appeared surprisingly efficient for modeling the whole period of intensive spread; we discuss the USA, and other places