On the impact of epidemic severity on network immunization algorithms

Abstract

There has been much recent interest in the prevention and mitigation of epidemics spreading through contact networks of host populations. Here, we investigate how the severity of epidemics, measured by its infection rate, influences the efficiency of well-known vaccination strategies. In order to assess the impact of severity, we simulate the SIR model at different infection rates on various real and model immunized networks. An extensive analysis of our simulation results reveals that immunization algorithms, which efficiently reduce the nodes' average degree, are more effective in the mitigation of weak and slow epidemics, whereas vaccination strategies that fragment networks to small components, are more successful in suppressing severe epidemics.

Keywords: Complex networks; Epidemic model; Graph; Immunization algorithms; Infection rate.

Fig. 1(a)

(a) HEP network.

Fig. 1(b)

(b) AS network.

Fig. 1(c)

(c) Facebook-like network.

Fig. 1(d)

(d) Erdös–Renyi network.

Fig. 1(e)

(e) Scale-free network.

Fig. 1(f)

(f) Small-world network.

Fig. 2(a)

(a) HEP network.

Fig. 2(b)

(b) AS network.

Fig. 2(c)

(c) Facebook-like network.

Fig. 2(d)

(d) Erdös–Renyi network.

Fig. 2(e)

(e) Scale-free network.

Fig. 2(f)

(f) Small-world network.

Fig. 3(a)

(a) HEP network.

Fig. 3(b)

(b) AS network.

Fig. 3(c)

(c) Facebook-like network.

Fig. 3(d)

(d) Erdös–Renyi network.

Fig. 3(e)

(e) Scale-free network.

Fig. 3(f)

(f) Small-world network.

Fig. 4(a)

(a) HEP network.

Fig. 4(b)

(b) AS network.

Fig. 4(c)

(c) Facebook-like network.

Fig. 4(d)

(d) Erdös–Renyi network.

Fig. 4(e)

(e) Scale-free network.

Fig. 4(f)

(f) Small-world network.

Fig. 5(a)

(a) HEP network.

Fig. 5(b)

(b) AS network.

Fig. 5(c)

(c) Facebook-like network.

Fig. 5(d)

(d) Erdös–Renyi network.

Fig. 5(e)

(e) Scale-free network.

Fig. 5(f)

(f) Small-world network.