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Hosseinzadeh MM, Cannataro M, Guzzi PH, Dondi R. Temporal networks in biology and medicine: a survey on models, algorithms, and tools. NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2022; 12:10. [PMID: 36618274 PMCID: PMC9803903 DOI: 10.1007/s13721-022-00406-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/16/2022] [Accepted: 12/17/2022] [Indexed: 01/01/2023]
Abstract
The use of static graphs for modelling and analysis of biological and biomedical data plays a key role in biomedical research. However, many real-world scenarios present dynamic behaviours resulting in both node and edges modification as well as feature evolution. Consequently, ad-hoc models for capturing these evolutions along the time have been introduced, also referred to as dynamic, temporal, time-varying graphs. Here, we focus on temporal graphs, i.e., graphs whose evolution is represented by a sequence of time-ordered snapshots. Each snapshot represents a graph active in a particular timestamp. We survey temporal graph models and related algorithms, presenting fundamentals aspects and the recent advances. We formally define temporal graphs, focusing on the problem setting and we present their main applications in biology and medicine. We also present temporal graph embedding and the application to recent problems such as epidemic modelling. Finally, we further state some promising research directions in the area. Main results of this study include a systematic review of fundamental temporal network problems and their algorithmic solutions considered in the literature, in particular those having application in computational biology and medicine. We also include the main software developed in this context.
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Affiliation(s)
| | - Mario Cannataro
- Department of Surgical and Medical Sciences and Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences and Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Riccardo Dondi
- Department of Literature, Philosophy, Communication Studies, University of Bergamo, Bergamo, Italy
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Wan J, Ichinose G, Small M, Sayama H, Moreno Y, Cheng C. Multilayer networks with higher-order interaction reveal the impact of collective behavior on epidemic dynamics. CHAOS, SOLITONS, AND FRACTALS 2022; 164:112735. [PMID: 36275139 PMCID: PMC9560911 DOI: 10.1016/j.chaos.2022.112735] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 09/20/2022] [Indexed: 06/12/2023]
Abstract
The ongoing COVID-19 pandemic has inflicted tremendous economic and societal losses. In the absence of pharmaceutical interventions, the population behavioral response, including situational awareness and adherence to non-pharmaceutical intervention policies, has a significant impact on contagion dynamics. Game-theoretic models have been used to reproduce the concurrent evolution of behavioral responses and disease contagion, and social networks are critical platforms on which behavior imitation between social contacts, even dispersed in distant communities, takes place. Such joint contagion dynamics has not been sufficiently explored, which poses a challenge for policies aimed at containing the infection. In this study, we present a multi-layer network model to study contagion dynamics and behavioral adaptation. It comprises two physical layers that mimic the two solitary communities, and one social layer that encapsulates the social influence of agents from these two communities. Moreover, we adopt high-order interactions in the form of simplicial complexes on the social influence layer to delineate the behavior imitation of individual agents. This model offers a novel platform to articulate the interaction between physically isolated communities and the ensuing coevolution of behavioral change and spreading dynamics. The analytical insights harnessed therefrom provide compelling guidelines on coordinated policy design to enhance the preparedness for future pandemics.
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Affiliation(s)
- Jinming Wan
- Department of Systems Science and Industrial Engineering, State University of New York, Binghamton, NY 13902, United States of America
| | - Genki Ichinose
- Department of Mathematical and Systems Engineering, Shizuoka University, 3-5-1 Johoku, Naka-ku, Hamamatsu 432-8561, Japan
| | - Michael Small
- Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Crawley, WA 6009, Australia
- Mineral Resources, CSIRO, Kensington, WA 6151, Australia
| | - Hiroki Sayama
- Department of Systems Science and Industrial Engineering, State University of New York, Binghamton, NY 13902, United States of America
| | - Yamir Moreno
- Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, 50018 Zaragoza, Spain
- Department of Theoretical Physics, University of Zaragoza, 50009 Zaragoza, Spain
- CENTAI Institute, Torino, 10138, Italy
| | - Changqing Cheng
- Department of Systems Science and Industrial Engineering, State University of New York, Binghamton, NY 13902, United States of America
- ISI Foundation, Torino, 10126, Italy
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O’Reilly KR, Yeh PT, Fonner VA, Sweat MD, Kennedy CE. Sexual partner concurrency: is it a useful concept for HIV prevention? A systematic review of the evidence for intervention effectiveness in low- and middle-income countries. AIDS Care 2022; 34:392-396. [PMID: 34702093 PMCID: PMC8976708 DOI: 10.1080/09540121.2021.1991881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
If sexual partner concurrency drives HIV transmission dynamics, shouldn't HIV prevention efforts be addressing this behavior? We systematically reviewed studies evaluating interventions to reduce sexual partner concurrency in low- and middle-income countries using pre/post or multi-arm designs. Only two studies met our inclusion criteria; neither found significant differences by intervention exposure on self-reported concurrency. Overall, very few interventions have specifically targeted concurrency, and those that did have not been rigorously evaluated. In practice, concurrency may be difficult to separate from multiple partnerships more generally.
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Affiliation(s)
- Kevin R. O’Reilly
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Ping Teresa Yeh
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Virginia A. Fonner
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Michael D. Sweat
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Caitlin E. Kennedy
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA,corresponding author
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