Elma Dervić, Katharina Ledebur, Stefan Thurner & Peter Klimek
Scientific Data volume 12, Article number: 215 (2025)

Comorbidity networks have become a valuable tool to support data-driven biomedical research. Yet, studies often are severely hindered by the availability of the necessary comprehensive data, often due to the sensitivity of health care information. This study presents a population-wide comorbidity network dataset derived from 45 million hospital stays of 8.9 million patients over 17 years in Austria. We present co-occurrence networks of hospital diagnoses, stratified by age, sex, and observation period in a total of 96 different subgroups. For each of these groups we report a range of association measures (e.g., count data, and odds ratios) for all pairs of diagnoses. The dataset provides the possibility to researchers to create their own, tailor-made comorbidity networks from real patient data that can be used as a starting point in quantitative and machine learning methods. This data platform is intended to lead to deeper insights into a wide range of epidemiological, public health, and biomedical research questions.

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Guillaume St-Onge, Jessica T. Davis, Laurent Hébert-Dufresne, Antoine Allard, Alessandra Urbinati, Samuel V. Scarpino, Matteo Chinazzi & Alessandro Vespignani 
Nature Medicine (2025)

Aircraft wastewater surveillance has been proposed as a new approach to monitor the global spread of pathogens. Here we develop a computational framework providing actionable information for the design and estimation of the effectiveness of global aircraft-based wastewater surveillance networks (WWSNs). We study respiratory diseases of varying transmission potential and find that networks of 10–20 strategically placed wastewater sentinel sites can provide timely situational awareness and function effectively as an early warning system. The model identifies potential blind spots and suggests optimization strategies to increase WWSN effectiveness while minimizing resource use. Our findings indicate that increasing the number of sentinel sites beyond a critical threshold does not proportionately improve WWSN capabilities, emphasizing the importance of resource optimization. We show, through retrospective analyses, that WWSNs can notably shorten detection time for emerging pathogens. The approach presented offers a realistic analytic framework for the analysis of WWSNs at airports.

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Waldemar Karwowski, et al.

International Journal of Production Research

Contemporary society faces a growing set of complex issues representing significant socioeconomic, health and well-being, environmental, and sustainability challenges. The discipline of industrial and systems engineering (ISE) can play an important role in addressing these issues. This paper identifies and discusses eight grand challenges for ISE. These grand challenges are (1) Artificial Intelligence (AI) For Business and Personal Use: Decision-Making and System Design and Operations, (2) Cybersecurity and Resilience, (3) Sustainability: Environment, Energy and Infrastructure, (4) Health Issues, (5) Social Issues, (6) Logistics and Supply Chain, (7) System Integration and Operations: Humans, Automation, and AI, and (8) Industrial and Systems Engineering Education. The discussed grand challenges were derived by accomplished ISE professionals who are the authors of this paper. The implications of the ISE grand challenges for education, training, research, and implementation of ISE principles and methodologies for the benefit of global society are discussed.

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Minze Wu, Tongfeng Weng, Zhuoming Ren, Xiaolu Chen and Chunzi Li

EPLA

Self-similarity of complex networks has been exhaustively explored but only concentrating on pairwise interactions between nodes. We restudy self-similar characteristics of networks from algebraic topological perspective. By virtue of a box covering technique, we generate consecutive renormalized networks with respect to different length scales. Interestingly, we find that the number of a specific order of clique in the renormalized networks presents a clearly scaling behavior. Moreover, we show that the growth pattern of cliques is likely to follow a universal principle for seemingly different kinds of real networks. Our work, for the first time, reveals the role of higher-order interactions in shaping self-similarity of complex networks.

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Jonah E. Friederich, Everton S. Medeiros, Sabine H. L. Klapp, Anna Zakharova

In networked systems, stochastic resonance occurs as a collective phenomenon where the entire stochastic network resonates with a weak applied periodic signal. Beyond the interplay among the network coupling, the amplitude of the external periodic signal, and the intensity of stochastic fluctuations, the maintenance of stochastic resonance also crucially depends on the resonance capacity of each oscillator composing the network. This scenario raises the question: Can local defects in the ability of oscillators to resonate break down the stochastic resonance phenomenon in the entire network? Here, we investigate this possibility in complex networks of prototypical bistable oscillators in a double-well potential. We test the sustainability of stochastic resonance by considering a fraction of network oscillators with nonresonant potential landscapes. We find that the number of nonresonant oscillators depends nonlinearly on their dissimilarity from the rest of the network oscillators. In addition, we unravel the role of the network topology and coupling strength in maintaining, or suppressing, the stochastic resonance for different noise levels and number of nonresonant oscillators. Finally, we obtain a low-dimensional deterministic model confirming the results observed for the networks.

Read the full article at: arxiv.org