Linking the disease transmission to information dissemination dynamics: An insight from a multi-scale model study

Spatiotemporal pattern recognition and dynamical analysis of COVID-19 in Shanghai, China

“…An age-structured epidemic model was constructed by Duan et al (2022) to find that shortening the diagnosis period can result in an enormous selective pressure on the evolution of SARS-CoV-2. A multi-scale model study was given by Li and Xiao (2021) to investigate the interaction of SARS-CoV-2 transmission and information dissemination dynamics during the outbreak of emerging infectious diseases. A probability model was proposed to retrospectively quantify the confidence of giving the end-of-outbreak declaration during the COVID-19 epidemic in Wuhan ( Yuan et al, 2022 ).…”

Effect of disease prevalence and growth stage on symptoms severity in theTurnip mosaic virus - Arabidopsis thalianapathosystem

“…However, the potential epidemiological implications of this communication networks have not been considered yet. In recent years, theoretical epidemiologists have been examining the relationship between the propagation of epidemic awareness and the actual epidemic infection in spatially-structured populations of susceptible hosts (Granell et al 2013;Wang et al 2015;Scatà et al 2016;Li and Xiao 2021;Gao et al 2022). In such models, the spread of the epidemics is not anymore properly described by classic models (e.g, susceptible -infected -recovered, SIR) but more complex and richer dynamics emerge, in which the onset of the epidemics has a critical value defined by the awareness dynamics.…”

FOMO (fate of online media only) in infectious disease modeling: a review of compartmental models

“…Aware people make behavioral changes such as mask wearing, hand-washing and avoidance of crowded places, thus reducing their risk of infection. The effects of these changes have been incorporated in models by either a reduction of the transmission term in the form of an exponential/saturated Holling type-II function [ 18 , 20 – 22 , 25 , 26 ] or by the introduction of extra compartments representing awareness of the disease with [ 23 , 26 – 29 ] and without a media compartment [ 30 – 32 ].…”

“…For ease of comparison, the following notation is adopted— for information transmission rate terms, S for susceptibles, E for exposed Individuals, I for infected/infectious Individuals, A for asymptomatics, R for recovered individuals and M for the media compartment Reference Basic Model Effect of online media Transmission term [ 28 ] SEIR Creation of an aware class Susceptibles interact with the media at a rate to become aware, where represents the daily normalized number of tweets about the infectious disease at any given time [ 34 ] SEIR Modification of the transmission term Media-induced quarantine The transmission rate between susceptible and infected individuals is reduced by a factor due to awareness driven by media reports where represents the weight of the media effect Terms / represent the quarantine rates due to media reports [ 29 ] SIR Creation of aware classes Unaware individuals interact with the media at a rate to become aware [ 35 ] SEI Modification of the transmission term The transmission rate between susceptible and infected individuals is reduced by a factor due to awareness driven by media reports, where α is a measure of the media effect [ 36 ] SEIR Creation of aware classes Modification of transmission terms Susceptibles interact with the media at a rate to become aware Asymptomatics interact with the media at a rate to go into quarantine The transmission rate between susceptible and infected individuals is reduced by where ρ is a half saturation constant [ 37 ] …”