Bistability and Resurgent Epidemics in Reinfection Models

Spreading processes that propagate through local interactions have been studied in multiple fields (e.g., epidemiology, complex networks, social sciences) using the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) frameworks. SIR assumes individuals acquire full immunity to the infection after recovery, while SIS assumes individuals acquire no immunity after recovery. However, in many spreading processes individuals may acquire only partial immunity to the infection or may become more susceptible to reinfection after recovery. We study a model for reinfection called Susceptible-Infected-Recovered-Infected (SIRI). The SIRI model generalizes the SIS and SIR models and allows for study of systems in which the susceptibility of agents changes irreversibly after first exposure to the infection. We show that when the rate of reinfection is higher than the rate of primary infection, the SIRI model exhibits bistability with a small difference in the initial fraction of infected individuals determining whether the infection dies out or spreads through the population. We find this critical value and show that when the infection does not die out there is a resurgent epidemic in which the number of infected individuals decays initially and remains at a low level for an arbitrarily long period of time before rapidly increasing toward an endemic equilibrium in which the fraction of infected individuals is non-zero.

[1]  Nico Stollenwerk,et al.  The phase transition lines in pair approximation for the basic reinfection model SIRI , 2007 .

[2]  Robert A. Van Gorder,et al.  Understanding viral video dynamics through an epidemic modelling approach , 2018 .

[3]  Tamer Basar,et al.  On the analysis of a continuous-time bi-virus model , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[4]  B. Murphy,et al.  Serum and nasal wash antibodies associated with resistance to experimental challenge with influenza A wild-type virus , 1986, Journal of clinical microbiology.

[5]  Angelia Nedić,et al.  Epidemic Processes Over Time-Varying Networks , 2016, IEEE Transactions on Control of Network Systems.

[6]  George J. Pappas,et al.  Optimal Resource Allocation for Competitive Spreading Processes on Bilayer Networks , 2015, IEEE Transactions on Control of Network Systems.

[7]  Graham F Medley,et al.  Infection, reinfection, and vaccination under suboptimal immune protection: epidemiological perspectives. , 2004, Journal of theoretical biology.

[8]  George J. Pappas,et al.  Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks , 2015, IEEE Control Systems.

[9]  Herbert W. Hethcote,et al.  The Mathematics of Infectious Diseases , 2000, SIAM Rev..

[10]  Joy Kuri,et al.  How to run a campaign: Optimal control of SIS and SIR information epidemics , 2014, Appl. Math. Comput..

[11]  Bahman Gharesifard,et al.  Stability of epidemic models over directed graphs: A positive systems approach , 2014, Autom..

[12]  Jesús Gómez-Gardeñes,et al.  Abrupt transitions from reinfections in social contagions , 2015 .

[13]  Christian Becker,et al.  An epidemic model for information diffusion in MANETs , 2002, MSWiM '02.

[14]  Chinwendu Enyioha,et al.  Optimal Resource Allocation for Network Protection Against Spreading Processes , 2013, IEEE Transactions on Control of Network Systems.

[15]  Francesco Bullo,et al.  On the dynamics of deterministic epidemic propagation over networks , 2017, Annu. Rev. Control..

[16]  Donald A Enarson,et al.  Rate of reinfection tuberculosis after successful treatment is higher than rate of new tuberculosis. , 2005, American journal of respiratory and critical care medicine.