Data-Driven Distributed Mitigation Strategies and Analysis of Mutating Epidemic Processes

• In this paper we study a discrete-time SIS (susceptible-infected-susceptible) model, where the infection and healing parameters and the underlying network may change over time. We provide conditions for the model to be well-defined and study its stability. For systems with homogeneous infection rates over symmetric graphs, we provide a sufficient condition for global exponential stability (GES) of the healthy state, that is, where the virus is eradicated. For systems with heterogeneous virus spread over directed graphs, provided that the variation is not too fast, a sufficient condition for GES of the healthy state is established. Appealing to the first stability result, we present two data-driven mitigation strategies that set the healing parameters in a centralized and a distributed manner, respectively, in order to drive the system to the healthy state.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Infektionsmedicin (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Infectious Medicine (hsv//eng)

Keyword

Digital storage

Directed graphs

Viruses

Discrete time

Epidemic process

Global exponential stability

Infection rates

Mitigation strategy

Stability results

Susceptible-infected-susceptible

Underlying networks

System stability

Publication and Content Type

ref (subject category)

kon (subject category)