Papers
Topics
Authors
Recent
Search
2000 character limit reached

How to reduce epidemic peaks keeping under control the time-span of the epidemic

Published 5 Apr 2020 in q-bio.PE | (2004.02189v1)

Abstract: One of the main challenges of the measures against the COVID-19 epidemic is to reduce the amplitude of the epidemic peak without increasing without control its timescale. We investigate this problem using the SIR model for the epidemic dynamics, for which reduction of the epidemic peak $I_P$ can be achieved only at the price of increasing the time $t_P$ of its occurrence and its entire time-span $t_E$. By means of a time reparametrization we linearize the equations for the SIR dynamics. This allows us to solve exactly the dynamics in the time domain and to derive the scaling behaviour of the size, the timescale and the speed of the epidemics, by reducing the infection rate $\alpha$ and by increasing the removal rate $\beta$ by a factor of $\lambda$. We show that for a given value of the size ($I_P$, the total, $I_E$ and average $\hat I_P$ number of infected), its occurrence time $t_P$ and entire time-span $t_E$ can be reduced by a factor $1/\lambda$ if the reduction of $I$ is achieved by increasing the removal rate instead of reducing the infection rate. Thus, epidemic containment measures based on tracing, early detection followed by prompt isolation of infected individuals are more efficient than those based on social distancing. We apply our results to the COVID-19 epidemic in Northern Italy. We show that the peak time $t_P$ and the entire time span $t_E$ could have been reduced by a factor $0.9 \le 1/\lambda\le 0.34$ with containment measures focused on increasing $\beta$ instead of reducing $\alpha$.

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

Collections

Sign up for free to add this paper to one or more collections.