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

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

In this Section, we perform our analysis and simulations on three different networks: Erd} os-R enyi random network G E , Small-world network G W , and Scale-free network G S . These networks are defined in the appendix. To generate the networks, Fig. 2 . Plot of the awareness probability function g bðs; t Ã Þ for t à ¼ 10: Before day t à ¼ 10 there is no awareness spread,b ¼ 0. At day t à ¼ 10 initially aware individual start informing its neighbors about infection. Through time the desire to spread information wanes (Funk et al., 2009). we use Networkx 1 and Erd} os-R enyi -graph algorithm to generate G E with probability of connection p ¼ 0:001. Using the same package, the Watts-Strogatz algorithm is used to generate Small-world network G W with probability of rewiring p ¼ 0:5. We also use BarabasieAlbert's preferential attachment model in Networkx to generate a Scale-free network G S with degree distribution PðkÞek À2:11 (Barab asi & Albert, 1999). The size of all networks is 10000 and the average number of neighbors for all networks is equal to 10. However, the networks differ in terms of degree distributions and other properties due to their different structures. Simulations start by infecting the most connected node (index case) to reach a balanced initial condition (Azizi, Dewar, & Mac Hyman, 2018, p. 233239) . They came out with the model baseline parameters in Table. 1, unless stated otherwise.