Effects of information-dependent vaccination behavior on coronavirus outbreak: insights from a SIRI model

Abstract

A mathematical model is proposed to assess the effects of a vaccine on the time evolution of a coronavirus outbreak. The model has the basic structure of SIRI compartments (susceptible-infectious-recovered-infectious) and is implemented by taking into account of the behavioral changes of individuals in response to the available information on the status of the disease in the community. We found that the cumulative incidence may be significantly reduced when the information coverage is high enough and/or the information delay is short, especially when the reinfection rate is high enough to sustain the presence of the disease in the community. This analysis is inspired by the ongoing outbreak of a respiratory illness caused by the novel coronavirus COVID-19.

The trade-off between mobility and vaccination for COVID-19 control: a metapopulation modeling approach

“…Note that in our model, vaccination not only may prevent SARS-CoV-2 infection but also it may prevent the symptomatic disease COVID-19. Moreover, unlike previous COVID-19 epidemic models (see, for example, [7,16,21,22,28,32]) system (2) allows the possibility of reinfections. This is essential since it is not yet known how long natural immunity will last and there have been already confirmed cases of coronavirus reinfection [15].…”

Hamiltonian structure of compartmental epidemiological models

“…Systems that model vaccinated populations are specially interesting, since they allow to design optimal vaccination strategies (see for example [24] , [25] ). Setting and in the generalized SIR (17) we have the system If is a strictly positive function, this model describes a population that is receiving a vaccination during the epidemic outbreak.…”

Analysis of the mitigation strategies for COVID-19: From mathematical modelling perspective

“…Mathematical models that analyse the spread of COVID-19 have began to appear in few published papers and online resources [7][8][9][10][11][12][13] . However, there are several challenges to the use of mathematical models in providing nearly accurate predictions at an early stage of the outbreak, particularly in real time as it is difficult to determine many of the pathogen-based parameters through mathematical models.…”