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.

On the effectiveness of random walks for modeling epidemics on networks

“…However, as the size of the matrix increases exponentially, the number of nonzero entries would increase exponentially since the graphs we consider are connected. Hence, SpTrSV would also eventually become infeasible for the computation of mean infection covering times as in (10).…”

“…In the literature, two main categories of random-walk models have been proposed to analyse epidemic networks. First, models based on a single random walker have been used, often derived from the expanding field of centrality measures [ 8 ]; they have been shown to provide interesting insight of the population networks; see, e.g., [ 6 , 9 , 10 ]. These models are particularly attractive because the relevant quantities are simple to compute also for relatively large networks.…”

Optimal test-kit based intervention strategy of epidemic spreading in heterogeneous complex networks

“…In these set-ups, the severity of the disease is determined by one of the key parameters of the system: the basic reproduction number (R 0 ), defined as the average number of susceptible individuals infected by a single infected individual during their infectious period [8]. These models can efficiently capture the optimal vaccination strategy [9,10], effective awareness program [11][12][13][14], efficient contact tracing [15,16] technique and suitable social distancing [17][18][19] plan to delay or eradicate the spread of the disease.…”

“…Note that, this meta-population network with migration is analogous to the reaction-diffusion dynamics [25,[30][31][32] i.e., particles (here fraction of population) diffuse and interact. It is well known that the suitable intervention strategy by partially controlling the network can reduce the prevalence of the entire system [9,[12][13][14]. For instance, the infection spreads rapidly through the hubs [27].…”

Optimizing targeted vaccination across cyber–physical networks: an empirically based mathematical simulation study

“…While full knowledge of the outbreak state (in the case of monitoring) or full population protection (in the case of vaccination) are preferable, neither is practically feasible [10,11]. For this reason, targeted strategies that involve interventions focused on a small, carefully selected subpopulation are of major interest [7,10,[12][13][14][15][16][17]. It has been shown that densely connected populations, such as schools [18,19], universities [10] or hospitals [20], play a significant role in large outbreaks [21], offering numerous paths for diseases to propagate.…”