The Impact of Community Structure of Social Contact Network on Epidemic Outbreak and Effectiveness of Non-pharmaceutical Interventions

Wang, Youzhong; Zeng, Daniel; Cao, Zhidong; Wang, Yong; Song, Hongbin; Zheng, Xiaolong

doi:10.1007/978-3-642-22039-5_12

Cited by 5 publications

(3 citation statements)

References 29 publications

(30 reference statements)

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“…The potential for the transmission of a pathogen through the hospital and across specialities is confirmed by the analysis of the community structure of the whole patient population and its coverage by the contact population with the presence of peripheral wards in most communities, see Table 2. Community structure, or clustering, is a common feature of complex systems 33 that usually constrains epidemic and diffusive dynamics [34][35][36] that in the case of patient movement and hospital wards highlight functional clusters comprising wards that commonly share patients. The communities found in the three hospitals indeed are thematically homogeneous, see Figure SI 1.…”

Section: Summary Of Resultsmentioning

confidence: 99%

Electronic health record enabled track and trace in an urban hospital network: implications for infection prevention and control

Expert

Clarke

et al. 2021

Preprint

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Healthcare-associated infections represent one of the most significant challenges for modern medicine as they can significantly impact patients' lives. Carbapenemase-producing Enterobacteriaceae (CPE) pose the greatest clinical threat, given the high levels of resistance to carbapenems, which are considered as agents of 'last resort' against life-threatening infections. Understanding patterns of CPE infection spreading in hospitals is paramount to design effective infection control protocols to mitigate the presence of CPE in hospitals. We used patient electronic health records from three urban hospitals to: i) track microbiologically confirmed carbapenemase producing Escherichia coli (CP-Ec) carriers and ii) trace the patients they shared place and time with until their identification. We show that yearly contact networks in each hospital consistently exhibit a core-periphery structure, highlighting the presence of a core set of wards where most carrier-contact interactions occured before being distributed to peripheral wards. We also identified functional communities of wards from the general patient movement network. The contact networks projected onto the general patient movement community structure showed a comprehensive coverage of the hospital. Our findings highlight that infections such as CP-Ec infections can reach virtually all parts of hospitals through first-level contacts.

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Section: Summary Of Resultsmentioning

confidence: 99%

Electronic health record enabled track and trace in an urban hospital network: implications for infection prevention and control

Expert

Clarke

et al. 2021

Preprint

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“…In addition, researchers also studied vaccination strategies in complex networks [57,58], as well as the bifurcation and oscillation phenomena of epidemic dynamics in complex networks [59,60]. Recently, many researchers who focus on the impact that network topologies have on epidemic dynamics have transferred their attention to epidemic spread in hierarchy and modular networks with community structure [61][62][63][64][65].…”

Section: The Impact Of Network Topologies On Epidemic Dynamicsmentioning

confidence: 99%

Mathematical and computational approaches to epidemic modeling: a comprehensive review

Duan

Fan

Zhang

et al. 2015

Front. Comput. Sci.

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Mathematical and computational approaches are important tools for understanding epidemic spread patterns and evaluating policies of disease control. In recent years, epidemiology has become increasingly integrated with mathematics, sociology, management science, complexity science, and computer science. The cross of multiple disciplines has caused rapid development of mathematical and computational approaches to epidemic modeling. In this article, we carry out a comprehensive review of epidemic models to provide an insight into the literature of epidemic modeling and simulation.We introduce major epidemic models in three directions, including mathematical models, complex network models, and agent-based models. We discuss the principles, applications, advantages, and limitations of these models. Meanwhile, we also propose some future research directions in epidemic modeling. Table 1 Classification of epidemic models Types Names Methods Mathematical models Compartmental models, Reed-Frost models Differential equations, stochastic process, Monte Carlo, Markov chains Complex network models System dynamics models in complex networks, numerical simulations of epidemics in complex networks, metapopulation models, weighted networks, adaptive networks Differential equations, stochastic processes Agent-based models

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“…However, these approaches lack the effectiveness due to either the time-delayed data collection (e.g, using medical survey or medical records) or impractical statistic assumptions about the dynamics of social contact networks. As a type of social networks, social contact network [2] focuses more on physical social contact than online social relationships. To address the effectiveness issue, we proposed a WBAN-smartphone based data collection framework to collect people’s health data and social contact information simultaneously in our previous work [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Cluster-Based Epidemic Control through Smartphone-Based Body Area Networks

Zhang

Wang

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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Increasing population density, closer social contact and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be quarantined to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphone-based body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective cluster-based quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks.

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The Impact of Community Structure of Social Contact Network on Epidemic Outbreak and Effectiveness of Non-pharmaceutical Interventions

Cited by 5 publications

References 29 publications

Electronic health record enabled track and trace in an urban hospital network: implications for infection prevention and control

Electronic health record enabled track and trace in an urban hospital network: implications for infection prevention and control

Mathematical and computational approaches to epidemic modeling: a comprehensive review

Cluster-Based Epidemic Control through Smartphone-Based Body Area Networks

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