Modeling the Consequences of Social Distancing Over Epidemics Spreading in Complex Social Networks: From Link Removal Analysis to SARS-CoV-2 Prevention

Abstract

In this perspective, we describe how the link removal (LR) analysis in social complex networks may be a promising tool to model non-pharmaceutical interventions (NPIs) and social distancing to prevent epidemics spreading. First, we show how the extent of the epidemic spreading and NPIs effectiveness over complex social networks may be evaluated with a static indicator, that is, the classic largest connected component (LCC). Then we explain how coupling the LR analysis and type SIR epidemiological models (EM) provide further information by including the temporal dynamics of the epidemic spreading. This is a promising approach to investigate important aspects of the recent NPIs applied by government to contain SARS-CoV-2, such as modeling the effect of the social distancing severity and timing over different network topologies. Further, implementing different link removal strategies to halt epidemics spreading provides information to individuate more effective NPIs, representing an important tool to offer a rationale sustaining policies to prevent SARS-CoV-2 and similar epidemics.

Network structure indexes to forecast epidemic spreading in real-world complex networks

“…Then, we assessed the pace of the epidemic spreading by the total number of individuals that have been infected ( TI ) at the end of the simulation, i.e. when there are no more infected nodes (5,12) and by the maximum value of infected nodes in a given day (  ) (12). The TI indicator corresponds to the cumulative sum of new cases, which is equivalent to the number of recovered nodes at the end of the dynamics, when, by model construction, no more nodes can be infected.…”

“…The TI indicator provides an estimate of the spread of the disease within a population and it is likely to correlate with the number of severe, and possible fatal, cases. The  indicator, besides the evaluation of the spreading pace, it provides an estimate of the pressure over the sanitary system which might collapse, thus causing higher mortality probabilities of infected individuals, when a critical threshold is exceeded (12). Since in epidemiology, "prevalence" is the fraction of a population currently infected (26),  can be defined as the maximum prevalence occurring during the epidemic simulations.…”

“…The fundamental role of networks in epidemiology has been recognized in the last years (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12). The disease spreading can be modeled as a network where nodes (vertices) represent the individuals (i.e.…”

A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks

“…Network science plays a fundamental role in epidemiology, and many recent pieces of research modeled disease spreading using network models [4][5][6][7][8][9][10] . A spreading disease can be described as a network where nodes represent the individuals and links (edges) represent the social contacts between them 9,[11][12][13] . The two classes of interventions that can be implemented to reduce the size of an outbreak can be divided into pharmacological interventions, PI, such as vaccinations, and nonpharmacological interventions, NPI, such as social distancing, washing hands, and lockdowns.…”

“…Fine-grained models should encompass further aspects of the epidemics, such as the temporal dynamics of the disease spreading. This can be done by solving the classical susceptible-infectedrecovered (SIR) model 23 over a network 10,12,24 . Coupling SIR epidemiological models and network structure provides further information about the disease spread, such as evaluating the effect of node vaccination/removal strategies (NVS) to reduce the pace of the epidemics, the peak of infected individuals, or the total number of infected at the end of the epidemic 12 .…”