Abstract
A flow-regulated infection rate is defined for a Susceptible-Infected-Susceptible (SIS) model revealing the network structural properties that influence the spread of infections. The infection rate is linked to the flow between compartments in the associated positive compartmental system, providing a self-regulatory effect on the spreading dynamics. This translates to infection carriers preferring to visit healthier sites over more infected ones. A flow-independent epidemic threshold is defined that sets the conditions on the graph’s structure and the aggregate infection rate for a disease to either spread or die out. An individual-based mean-field approach returns results comparable to models with constant infection rates. This approach lends itself well to model the spread of infectious diseases as well as threats in IT networks, pharmacokinetics and the spread of disruptions on infrastructure networks.
Highlights
Epidemiological models have been proven valuable beyond the understanding of infectious diseases
Epidemic spreading is modelled considering that infected individuals infect the susceptible ones with whom they are in contact
A contagion spreads successfully depending on a threshold, indicated by a basic reproduction number R0, which depends on the infection and recovery rates, and on the network of contacts, when this is considered (Nowzari, Preciado, & Pappas, 2016)
Summary
Epidemiological models have been proven valuable beyond the understanding of infectious diseases They have been associated, for example, to the study of terrorist networks (Gutfraind, 2010), the spreading of rumors (Daley & Kendall, 1965) and computer viruses (Garetto, Gong, & Towsley, 2003). A contagion spreads successfully depending on a threshold, indicated by a basic reproduction number R0, which depends on the infection and recovery rates, and on the network of contacts, when this is considered (Nowzari, Preciado, & Pappas, 2016). When the drug binds to the targets, the availability of these decreases, i.e. the compartment gets healthier, and the drug accumulation reduces as a consequence (Mager & Jusko, 2001) This eventually provides a self-regulatory mechanism of the infection (or drug, as in the latter example) spreading. There, the change in the spreading rate is associated to a regulation effect, linked to a negative feedback on the infection rate, leading to a stable dynamics approaching asymptotic conditions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
