Leonhard Horstmeyer will present an online talk within the seminar “Analysis of Complex Systems” on May 14, 2021 from 3pm – 4 pm (CET).
If you would like to join the talk, please send an email to firstname.lastname@example.org
Title: “Balancing endogenous and exogenous mitigation measures in SIR-type epidemics”
During the current pandemic we have seen a multitude of non-pharmaceutical mitigation measures, which may largely be grouped into endogenous self-distancing/social avoidance and exogenous isolation measures.
Here we are studying a minimal compartmental model that aims to understand the relative effect of self-distancing and quarantining. We introduce a simple adaptive extension of the SIR model with a quarantine compartment. The latter is known as the SIRX model, but to our knowledge the adaptive extensions are novel. First, we compare computationally expensive, adaptive network simulations with their corresponding computationally ODE equivalents and find excellent agreement. Second, we discover that there exists a relatively simple critical curve in parameter space for the epidemic threshold, which strongly suggests a mutual compensation effect between the two mitigation strategies: as long as social distancing and quarantine measures are both sufficiently strong, large outbreaks are prevented. Third, we study the total number of infected and the maximum peak during large outbreaks using a combination of analytical estimates and numerical simulations. Also for large outbreaks we find a similar compensation effect as for the epidemic threshold.
This suggests that if there is little incentive for social distancing within a population, drastic quarantining is required, and vice versa. Both pure scenarios are unrealistic in practice. Our models show that only a combination of measures is likely to succeed to control epidemic spreading. Fourth, we analytically compute an upper bound for the total number of infected on adaptive networks, using integral estimates in combination with the moment-closure approximation on the level of an observable.