In this project we develop epidemiological models on complex networks to explore how behavioral adaption to epidemic can alter the epidemic itself. This project was developed for the course Life data and epidemiology at uniPD.
- SIS with risk perception
We assume the perception of the risk of being infected depends on the fraction of neighbors that are ill and study this behaviour in the mean-field approximation and by numerical simulations for regular, random and scale-free networks. For more details see the notebook SIS_fear_Bagnoli
and the reference Bagnoli et al. https://doi.org/10.1103/PhysRevE.76.061904
2. Two layer model: SEIRD for the epidemic in the physical net and spreading of Fear in the social net
We separate physical layer of contact with social layer by modelling two different dynamics. The first layer on which a classic SEIRD epidemic can spread and a second layer where we link fear with the number of diseased/death neighbors of the node simulating the spreading of the fear in an individual. In this way the infectivity is not more a global parameter but depends on the node itself and its fear level. Check the implementation in the SEIRD_fear
module and the plots in SIR_SEIRD_fear_plots
notebook.
Stochastic simulation of the dynamics, the fear level increases as the fraction of infected/deaths neighbours increases thus lowering the infectivity.
3. SIS on adaptive network
We study the epidemic dynamics on an adaptive network, where the susceptibles are able to avoid contact with infected by rewiring their network connections. This dynamic leads to oscillations and hysteresis. For more details see the notebook Adaptive_SIS
.
4. SIR on adaptive network
We further study the effect of the rewiring on SIR_SEIRD_fear_plots
and the module SIR_adaptive
.