Modeling and Evaluating Epidemic Control Strategies With High-Order Temporal Networks

Non-Pharmaceutical Interventions (NPIs) are essential measures that reduce and control a severe outbreak or a pandemic, especially in the absence of drug treatments. However, estimating and evaluating their impact on society remains challenging, considering the numerous and closely tied aspects to examine. This article proposes a fine-grain modeling methodology for NPIs, based on high-order relationships between people and environments, mimicking direct and indirect contagion pathways over time. After assessing the ability of each intervention in controlling an epidemic propagation, we devise a multi-objective optimization framework, which, based on the epidemiological data, calculates the NPI combination that should be implemented to minimize the spread of an epidemic as well as the damage due to the intervention. Each intervention is thus evaluated through an agent-based simulation, considering not only the reduction in the fraction of infected but also to what extent its application damages the daily life of the population. We run experiments on three data sets, and the results illustrate how the application of NPIs should be tailored to the specific epidemic situation. They further highlight the critical importance of correctly implementing personal protective (e.g., using face masks) and sanitization measures to slow down a pathogen spreading, especially in crowded places

Characterizing Twitter Users: What do Samantha Cristoforetti, Barack Obama and Britney Spears Have in Common?

The exponential growth in the use of digital devices and the ubiquitous online access produce a huge amount of structured and unstructured data that can be mined and analyzed to gather insights into several domains. In particular, since the advent of Web 2.0, Online Social Networks (OSNs) represent a rich opportunity for researchers to collect real user data and to explore OSNs users behavior. This study represents a first attempt to characterize and classify OSNs users according to their level of activity through the use of user profile attributes. We analyzed four case studies from the Twitter platform for a final total of around 721 thousand users, divided into four sub-datasets and examined over a period of at least six months in 2017. Following a data-driven methodology, we found that static, profile-based information - based on the entire lifetime of the users - can help to recognize users influence in Twitter online communities. On the other hand, these profile attributes are not enough to characterize user activity on the microblogging platform

On evaluating graph partitioning algorithms for distributed agent based models on networks

Graph Partitioning is a key challenge problem with application in many scientific and technological fields. The problem is very well studied with a rich literature and is known to be NP-hard. Several heuristic solutions, which follow diverse approaches, have been proposed, they are based on different initial assumptions that make them difficult to compare. An analytical comparison was performed based on an Implementation Challenge [3], however being a multi-objective problem (two opposing goals are for instance load balancing and edge-cut size), the results are difficult to compare and it is hard to foresee what can be the impact of one solution, instead of another, in a real scenario. In this paper we analyze the problem in a real context: the development of a distributed agent-based simulation model on a network field (which for instance can model social interactions). We present an extensive evaluation of the most efficient and effective solutions for the balanced k-way partitioning problem. We evaluate several strategies both analytically and on real distributed simulation settings (D-Mason). Results show that, a good partitioning strategy strongly influences the performances of the distributed simulation environment. Moreover, we show that there is a strong correlation between the edge-cut size and the real performances. Analyzing the results in details we were also able to discover the parameters that need to be optimized for best performances on networks in ABMs

Social Influence Maximization in Hypergraphs

This work deals with a generalization of the minimum Target Set Selection (TSS) problem, a key algorithmic question in information diffusion research due to its potential commercial value. Firstly proposed by Kempe et al., the TSS problem is based on a linear threshold diffusion model defined on an input graph with node thresholds, quantifying the hardness to influence each node. The goal is to find the smaller set of items that can influence the whole network according to the diffusion model defined. This study generalizes the TSS problem on networks characterized by many-to-many relationships modeled via hypergraphs. Specifically, we introduce a linear threshold diffusion process on such structures, which evolves as follows. Let H = (V,E) be a hypergraph. At the beginning of the process, the nodes in a given set S subset of V are influenced. Then, at each iteration, (i) the influenced hyperedges set is augmented by all edges having a sufficiently large number of influenced nodes; (ii) consequently, the set of influenced nodes is enlarged by all the nodes having a sufficiently large number of already influenced hyperedges. The process ends when no new nodes can be influenced. Exploiting this diffusion model, we define the minimum Target Set Selection problem on hypergraphs (TSSH). Being the problem NP-hard (as it generalizes the TSS problem), we introduce four heuristics and provide an extensive evaluation on real-world networks

Experimenting with Agent-Based Model Simulation Tools

Agent-based models (ABMs) are one of the most effective and successful methods for analyzing real-world complex systems by investigating how modeling interactions on the individual level (i.e., micro-level) leads to the understanding of emergent phenomena on the system level (i.e., macro-level). ABMs represent an interdisciplinary approach to examining complex systems, and the heterogeneous background of ABM users demands comprehensive, easy-to-use, and efficient environments to develop ABM simulations. Currently, many tools, frameworks, and libraries exist, each with its characteristics and objectives. This article aims to guide newcomers in the jungle of ABM tools toward choosing the right tool for their skills and needs. This work proposes a thorough overview of open-source general-purpose ABM tools and offers a comparison from a two-fold perspective. We first describe an off-the-shelf evaluation by considering each ABM tool’s features, ease of use, and efficiency according to its authors. Then, we provide a hands-on evaluation of some ABM tools by judging the effort required in developing and running four ABM models and the obtained performance

Defect of the endogenous inhibitory pain system in idiopathic restless legs syndrome: a laser evoked potentials study

Background: Restless legs syndrome (RLS) is a complex sensorimotor disorder. Symptoms worsen toward evening and at rest and are temporarily relieved by movement. Symptoms are perceived as painful in up to 45% of cases, and nociception system may be involved. Objectives: To assess the descending diffuse noxious inhibitory control in RLS patients. Methods: Twenty-one RLS patients and twenty age and sex-matched healthy controls (HC) underwent a conditioned pain modulation protocol. Cutaneous heat stimuli were delivered via laser evoked potentials (LEPs) on the dorsum of the right hand (UL) and foot (LL). N2 and P2 latencies, N2/P2 amplitude and pain ratings (NRS) were recorded before (baseline), during, and after a heterotopic noxious conditioning stimulation (HNCS) application. The baseline/HNCS ratio was calculated for both UL and LL. Results: N2 and P2 latencies did not vary between groups at each condition and limbs. Both groups showed a physiological N2/P2 amplitude and NRS reduction during the HNCS condition in UL and LL in comparison to baseline and post conditions (all, P < 0.003). Between-groups comparisons revealed a significant lower amplitude reduction in RLS at the N2/P2 amplitude during the HNCS condition only for LL (RLS, 13.6 μV; HC, 10.1 μV; P = 0.004). Such result was confirmed by the significant difference at the ratio (RLS, 69%, HC, 52.5%; P = 0.038). Conclusions: The lower physiological reduction during the HNCS condition at LL in RLS patients suggests a defect in the endogenous inhibitory pain system. Further studies should clarify the causal link of this finding, also investigating the circadian modulation of this paradigm. © 2023 International Parkinson and Movement Disorder Society

Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl

Real-world complex networks are usually being modeled as graphs. The concept of graphs assumes that the relations within the network are binary (for instance, between pairs of nodes); however, this is not always true for many real-life scenarios, such as peer-to-peer communication schemes, paper co-authorship, or social network interactions. For such scenarios, it is often the case that the underlying network is better and more naturally modeled by hypergraphs. A hypergraph is a generalization of a graph in which a single (hyper)edge can connect any number of vertices. Hypergraphs allow modelers to have a complete representation of multi-relational (many-to-many) networks; hence, they are extremely suitable for analyzing and discovering more subtle dependencies in such data structures. Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (such as searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms (such as community detection). In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation. We also present various approaches for hypergraph visualization integrated into our tool. In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series. The results are promising and they confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.Comment: 32 pages, 10 figures, 7 tables, submitted to Internet Mathematics Journa