Epidemics on networks: Reducing disease transmission using health emergency declarations and peer communication

Azizi, Asma; Montalvo, Cesar; Espinoza, Baltazar; Kang, Yun; Castillo-Chavez, Carlos

Now, we define assumptions and algorithm of our model Table 2 . Person i at any given day t has one of the states: S u ;S a ;S i ; I q ; I f , or R. The fraction of infected people at dat t is PðtÞ, and P à is a positive fraction 2½0; 1. We define t à ¼ min t fPðtÞ ¼ P à g which is the first time that PðtÞ reaches P à . We assume Education level for each person k, x k does not change by time. Also prior to time t à every susceptible person is at state S u , and finally the time scale for each update is day, in which each person can have at most one update. The following algorithms briefly shows some key part of the model. Algorithm 1. The probability of contact per day between two neighbors: infected k and susceptible j. Set of recovered nodes at time t G:NðkÞ Set of neighbors of node k in Network G IP(k)