1
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Wang L, Zhang K, Xu L, Wang J. Understanding underlying physical mechanism reveals early warning indicators and key elements for adaptive infections disease networks. PNAS NEXUS 2024; 3:pgae237. [PMID: 39035039 PMCID: PMC11259140 DOI: 10.1093/pnasnexus/pgae237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 06/03/2024] [Indexed: 07/23/2024]
Abstract
The study of infectious diseases holds significant scientific and societal importance, yet current research on the mechanisms of disease emergence and prediction methods still face challenging issues. This research uses the landscape and flux theoretical framework to reveal the non-equilibrium dynamics of adaptive infectious diseases and uncover its underlying physical mechanism. This allows the quantification of dynamics, characterizing the system with two basins of attraction determined by gradient and rotational flux forces. Quantification of entropy production rates provides insights into the system deviating from equilibrium and associated dissipative costs. The study identifies early warning indicators for the critical transition, emphasizing the advantage of observing time irreversibility from time series over theoretical entropy production and flux. The presence of rotational flux leads to an irreversible pathway between disease states. Through global sensitivity analysis, we identified the key factors influencing infectious diseases. In summary, this research offers valuable insights into infectious disease dynamics and presents a practical approach for predicting the onset of critical transition, addressing existing research gaps.
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Affiliation(s)
- Linqi Wang
- Center of Theoretical Physics, College of Physics, Jilin University, Changchun, Jilin, 130012, China
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, 130022, China
| | - Kun Zhang
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, 130022, China
| | - Li Xu
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin, 130022, China
| | - Jin Wang
- Department of Chemistry, Physics and Astronomy, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
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2
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Young MJ, Silk MJ, Pritchard AJ, Fefferman NH. The interplay of social constraints and individual variation in risk tolerance in the emergence of superspreaders. J R Soc Interface 2023; 20:20230077. [PMID: 37528679 PMCID: PMC10394411 DOI: 10.1098/rsif.2023.0077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 07/11/2023] [Indexed: 08/03/2023] Open
Abstract
Individual host behaviours can drastically impact the spread of infection through a population. Differences in the value individuals place on both socializing with others and avoiding infection have been shown to yield emergent homophily in social networks and thereby shape epidemic outcomes. We build on this understanding to explore how individuals who do not conform to their social surroundings contribute to the propagation of infection during outbreaks. We show how non-conforming individuals, even if they do not directly expose a disproportionate number of other individuals themselves, can become functional superspreaders through an emergent social structure that positions them as the functional links by which infection jumps between otherwise separate communities. Our results can help estimate the potential success of real-world interventions that may be compromised by a small number of non-conformists if their impact is not anticipated, and plan for how best to mitigate their effects on intervention success.
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Affiliation(s)
- Matthew J. Young
- Department of Mathematics, The University of Tennessee Knoxville, Knoxville 37996-4519 TN, USA
| | - Matthew J. Silk
- Department of NIMBioS, The University of Tennessee Knoxville, Knoxville 37996-4519 TN, USA
| | - Alexander J. Pritchard
- Department of NIMBioS, The University of Tennessee Knoxville, Knoxville 37996-4519 TN, USA
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3
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Chen L, Wang J. Bifurcation analysis in an epidemic model on adaptive networks. CHAOS (WOODBURY, N.Y.) 2023; 33:033135. [PMID: 37003833 DOI: 10.1063/5.0130068] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
In this paper, we study a delayed adaptive network epidemic model in which the local spatial connections of susceptible and susceptible individuals have time-delay effects on the rate of demographic change of local spatial connections of susceptible and susceptible individuals. We prove that the Hopf bifurcation occurs at the critical value τ0 with delay τ as the bifurcation parameter. Then, by using the normal form method and the central manifold theory, the criteria for the bifurcation direction and stability are derived. Finally, numerical simulations are presented to show the feasibility of our results.
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Affiliation(s)
- Liang Chen
- Department of Mathematics, Guizhou University, Guiyang, Guizhou 550025, China
| | - JinRong Wang
- Department of Mathematics, Guizhou University, Guiyang, Guizhou 550025, China
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4
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Wilber MQ, Yang A, Boughton R, Manlove KR, Miller RS, Pepin KM, Wittemyer G. A model for leveraging animal movement to understand spatio-temporal disease dynamics. Ecol Lett 2022; 25:1290-1304. [PMID: 35257466 DOI: 10.1111/ele.13986] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/27/2021] [Accepted: 02/04/2022] [Indexed: 12/19/2022]
Abstract
The ongoing explosion of fine-resolution movement data in animal systems provides a unique opportunity to empirically quantify spatial, temporal and individual variation in transmission risk and improve our ability to forecast disease outbreaks. However, we lack a generalizable model that can leverage movement data to quantify transmission risk and how it affects pathogen invasion and persistence on heterogeneous landscapes. We developed a flexible model 'Movement-driven modelling of spatio-temporal infection risk' (MoveSTIR) that leverages diverse data on animal movement to derive metrics of direct and indirect contact by decomposing transmission into constituent processes of contact formation and duration and pathogen deposition and acquisition. We use MoveSTIR to demonstrate that ignoring fine-scale animal movements on actual landscapes can mis-characterize transmission risk and epidemiological dynamics. MoveSTIR unifies previous work on epidemiological contact networks and can address applied and theoretical questions at the nexus of movement and disease ecology.
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Affiliation(s)
- Mark Q Wilber
- Forestry, Wildlife, and Fisheries, Institute of Agriculture, University of Tennessee, Knoxville, Tennessee, USA
| | - Anni Yang
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, Colorado, USA.,Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA.,Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, Oklahoma, USA
| | - Raoul Boughton
- Archbold Biological Station, Buck Island Ranch, Lake Placid, Florida, USA
| | - Kezia R Manlove
- Department of Wildland Resources and Ecology Center, Utah State University, Logan, Utah, USA
| | - Ryan S Miller
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Service, Center for Epidemiology and Animal Health, Fort Collins, Colorado, USA
| | - Kim M Pepin
- United States Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, Fort Collins, Colorado, USA
| | - George Wittemyer
- Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
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5
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Lemanski N, Silk M, Fefferman N, Udiani O. How territoriality reduces disease transmission among social insect colonies. Behav Ecol Sociobiol 2021; 75:164. [PMID: 34866761 PMCID: PMC8630993 DOI: 10.1007/s00265-021-03095-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 10/03/2021] [Accepted: 10/11/2021] [Indexed: 12/22/2022]
Abstract
Abstract
Social behavior can have a major impact on the dynamics of infectious disease outbreaks. For animals that live in dense social groups, such as the eusocial insects, pathogens pose an especially large risk because frequent contacts among individuals can allow rapid spread within colonies. While there has been a large body of work examining adaptations to mitigate the spread of infectious disease within social insect colonies, there has been less work on strategies to prevent the introduction of pathogens into colonies in the first place. We develop an agent-based model to examine the effect of territorial behavior on the transmission of infectious diseases between social insect colonies. We find that by preventing the introduction of infected foreign workers into a colony, territoriality can flatten the curve of an epidemic, delaying the introduction of an infectious disease and reducing its maximum prevalence, but only for diseases with moderate to low transmissibility. Our results have implications for understanding how pathogen risk influences the evolution of territorial behavior in social insects and other highly social animals. Significance statement Infectious disease outbreaks can impose a large fitness cost to animals that live in social groups. The frequency and pattern of contacts both within and among groups can have a large impact on the speed and extent of an epidemic. Using an individual-based model, we examined how the exclusion of foreign workers from a territory around the nest influences disease transmission between social insect colonies. We find that territoriality can protect colonies from outbreaks of low to moderately contagious pathogens by delaying the spillover from other colonies and reducing the maximum number of workers who are infected. These results suggest that the relative threat posed by infectious diseases may have played an important role in shaping the diversity of territorial behaviors seen in different social insect species. Supplementary Information The online version contains supplementary material available at 10.1007/s00265-021-03095-0.
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Affiliation(s)
- Natalie Lemanski
- Department of Ecology, Evolution, & Natural Resources, Rutgers University, New Brunswick, NJ USA
| | - Matthew Silk
- Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville, TN USA
| | - Nina Fefferman
- Department of Ecology & Evolutionary Biology, University of Tennessee, Knoxville, TN USA
| | - Oyita Udiani
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA USA
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6
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Rivas AL, van Regenmortel MHV. COVID-19 related interdisciplinary methods: Preventing errors and detecting research opportunities. Methods 2021; 195:3-14. [PMID: 34029715 PMCID: PMC8545872 DOI: 10.1016/j.ymeth.2021.05.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 05/18/2021] [Accepted: 05/19/2021] [Indexed: 12/12/2022] Open
Abstract
More than 130,000 peer-reviewed studies have been published within one year after COVID-19 emerged in many countries. This large and rapidly growing field may overwhelm the synthesizing abilities of both researchers and policy-makers. To provide a sinopsis, prevent errors, and detect cognitive gaps that may require interdisciplinary research methods, the literature on COVID-19 is summarized, twice. The overall purpose of this study is to generate a dialogue meant to explain the genesis of and/or find remedies for omissions and contradictions. The first review starts in Biology and ends in Policy. Policy is chosen as a destination because it is the setting where cognitive integration must occur. The second review follows the opposite path: it begins with stated policies on COVID-19 and then their assumptions and disciplinary relationships are identified. The purpose of this interdisciplinary method on methods is to yield a relational and explanatory view of the field -one strategy likely to be incomplete but usable when large bodies of literature need to be rapidly summarized. These reviews identify nine inter-related problems, research needs, or omissions, namely: (1) nation-wide, geo-referenced, epidemiological data collection systems (open to and monitored by the public); (2) metrics meant to detect non-symptomatic cases -e.g., test positivity-; (3) cost-benefit oriented methods, which should demonstrate they detect silent viral spreaders even with limited testing; (4) new personalized tests that inform on biological functions and disease correlates, such as cell-mediated immunity, co-morbidities, and immuno-suppression; (5) factors that influence vaccine effectiveness; (6) economic predictions that consider the long-term consequences likely to follow epidemics that growth exponentially; (7) the errors induced by self-limiting and/or implausible paradigms, such as binary and reductionist approaches; (8) new governance models that emphasize problem-solving skills, social participation, and the use of scientific knowledge; and (9) new educational programs that utilize visual aids and audience-specific communication strategies. The analysis indicates that, to optimally address these problems, disciplinary and social integration is needed. By asking what is/are the potential cause(s) and consequence(s) of each issue, this methodology generates visualizations that reveal possible relationships as well as omissions and contradictions. While inherently limited in scope and likely to become obsolete, these shortcomings are avoided when this 'method on methods' is frequently practiced. Open-ended, inter-/trans-disciplinary perspectives and broad social participation may help researchers and citizens to construct, de-construct, and re-construct COVID-19 related research.
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Affiliation(s)
- Ariel L Rivas
- Center for Global Health, School of Medicine, University of New Mexico, Albuquerque, NM, United States.
| | - Marc H V van Regenmortel
- University of Vienna, Austria; and Higher School of Biotechnology, University of Strasbourg, and French National Research Center, France
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7
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Masuda N, Miller JC, Holme P. Concurrency measures in the era of temporal network epidemiology: a review. J R Soc Interface 2021; 18:20210019. [PMID: 34062106 PMCID: PMC8169215 DOI: 10.1098/rsif.2021.0019] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 05/11/2021] [Indexed: 01/19/2023] Open
Abstract
Diseases spread over temporal networks of interaction events between individuals. Structures of these temporal networks hold the keys to understanding epidemic propagation. One early concept of the literature to aid in discussing these structures is concurrency-quantifying individuals' tendency to form time-overlapping 'partnerships'. Although conflicting evaluations and an overabundance of operational definitions have marred the history of concurrency, it remains important, especially in the area of sexually transmitted infections. Today, much of theoretical epidemiology uses more direct models of contact patterns, and there is an emerging body of literature trying to connect methods to the concurrency literature. In this review, we will cover the development of the concept of concurrency and these new approaches.
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Affiliation(s)
- Naoki Masuda
- Department of Mathematics, State University of New York at Buffalo, New York, NY, USA
- Computational and Data-Enabled Science and Engineering Program, State University of New York at Buffalo, New York, NY, USA
| | - Joel C. Miller
- School of Engineering and Mathematical Sciences, La Trobe University, Bundoora, Australia
| | - Petter Holme
- Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Yokohama 226-8503, Japan
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8
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The role of social structure and dynamics in the maintenance of endemic disease. Behav Ecol Sociobiol 2021; 75:122. [PMID: 34421183 PMCID: PMC8370858 DOI: 10.1007/s00265-021-03055-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 07/09/2021] [Accepted: 07/13/2021] [Indexed: 02/07/2023]
Abstract
Social interactions are required for the direct transmission of infectious diseases. Consequently, the social network structure of populations plays a key role in shaping infectious disease dynamics. A huge research effort has examined how specific social network structures make populations more (or less) vulnerable to damaging epidemics. However, it can be just as important to understand how social networks can contribute to endemic disease dynamics, in which pathogens are maintained at stable levels for prolonged periods of time. Hosts that can maintain endemic disease may serve as keystone hosts for multi-host pathogens within an ecological community, and also have greater potential to act as key wildlife reservoirs of agricultural and zoonotic diseases. Here, we examine combinations of social and demographic processes that can foster endemic disease in hosts. We synthesise theoretical and empirical work to demonstrate the importance of both social structure and social dynamics in maintaining endemic disease. We also highlight the importance of distinguishing between the local and global persistence of infection and reveal how different social processes drive variation in the scale at which infectious diseases appear endemic. Our synthesis provides a framework by which to understand how sociality contributes to the long-term maintenance of infectious disease in wildlife hosts and provides a set of tools to unpick the social and demographic mechanisms involved in any given host-pathogen system. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s00265-021-03055-8.
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9
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Abstract
Animal populations are occasionally shocked by epidemics of contagious diseases. The ability of social systems to withstand epidemic shocks and mitigate disruptions could shape the evolution of complex animal societies. We present a mathematical model to explore the potential impact of disease on the evolutionary fitness of different organizational strategies for populations of social species whose survival depends on collaborative efficiency. We show that infectious diseases select for a specific feature in the organization of collaborative roles-cohort stability-and that this feature is costly, and therefore unlikely to be maintained in environments where infection risks are absent. Our study provides evidence for an often-stated (but rarely supported) claim that pathogens have been the dominant force shaping the complexity of division of labour in eusocial societies of honeybees and termites and establishes a general theoretical approach for assessing evolutionary constraints on social organization from disease risk in other collaborative taxa.
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Affiliation(s)
- Oyita Udiani
- National Institute for Mathematical & Biological Synthesis, Knoxville, TN, USA.,Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA, USA
| | - Nina H Fefferman
- National Institute for Mathematical & Biological Synthesis, Knoxville, TN, USA.,Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA.,Department of Mathematics, University of Tennessee, Knoxville, TN, USA
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10
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Ellner SP, Ng WH, Myers CR. Individual Specialization and Multihost Epidemics: Disease Spread in Plant-Pollinator Networks. Am Nat 2020; 195:E118-E131. [PMID: 32364778 DOI: 10.1086/708272] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Many parasites infect multiple species and persist through a combination of within- and between-species transmission. Multispecies transmission networks are typically constructed at the species level, linking two species if any individuals of those species interact. However, generalist species often consist of specialized individuals that prefer different subsets of available resources, so individual- and species-level contact networks can differ systematically. To explore the epidemiological impacts of host specialization, we build and study a model for pollinator pathogens on plant-pollinator networks, in which individual pollinators have dynamic preferences for different flower species. We find that modeling and analysis that ignore individual host specialization can predict die-off of a disease that is actually strongly persistent and can badly over- or underpredict steady-state disease prevalence. Effects of individual preferences remain substantial whenever mean preference duration exceeds half of the mean time from infection to recovery or death. Similar results hold in a model where hosts foraging in different habitats have different frequencies of contact with an environmental reservoir for the pathogen. Thus, even if all hosts have the same long-run average behavior, dynamic individual differences can profoundly affect disease persistence and prevalence.
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11
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Wilson SN, Sindi SS, Brooks HZ, Hohn ME, Price CR, Radunskaya AE, Williams ND, Fefferman NH. How Emergent Social Patterns in Allogrooming Combat Parasitic Infections. Front Ecol Evol 2020. [DOI: 10.3389/fevo.2020.00054] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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12
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Description of social contacts among student cases of pandemic influenza during the containment phase, Melbourne, Australia, 2009. Western Pac Surveill Response J 2018; 9:27-34. [PMID: 31832251 PMCID: PMC6902646 DOI: 10.5365/wpsar.2018.9.5.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Introduction Students comprised the majority of early cases of influenza A(H1N1)pdm09 in Melbourne, Australia. Students and school settings were targeted for public health interventions following the emergence of pH1N1. This study was conducted to describe changes in social contacts among the earliest confirmed student cases of pH1N1 in Melbourne, Australia, to inform future pandemic control policy and explore transmission model assumptions. Methods A retrospective cross-sectional behavioural study of student cases with laboratory-confirmed pH1N1 between 28 April and 3 June 2009 was conducted in 2009. Demographics, symptom onset dates and detailed information on regular and additional extracurricular activities were collected. Summary measures for activities were calculated, including median group size and median number of close contacts and attendance during the students’ exposure and infectious periods or during school closures. A multivariable model was used to assess associations between rates of participation in extracurricular activities and both school closures and students’ infectious periods. Results Among 162 eligible cases, 99 students participated. Students reported social contact in both curricular and extra-curricular activities. Group size and total number of close contacts varied. While participation in activities decreased during the students’ infectious periods and during school closures, social contact was common during periods when isolation was advised and during school closures. Discussion This study demonstrates the potential central role of young people in pandemic disease transmission given the level of non-adherence to prevention and control measures. These finding have public health implications for both informing modelling estimates of future pandemics and targeting prevention and control strategies to young people.
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13
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Valdano E, Fiorentin MR, Poletto C, Colizza V. Epidemic Threshold in Continuous-Time Evolving Networks. PHYSICAL REVIEW LETTERS 2018; 120:068302. [PMID: 29481258 PMCID: PMC7219439 DOI: 10.1103/physrevlett.120.068302] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 11/20/2017] [Indexed: 05/11/2023]
Abstract
Current understanding of the critical outbreak condition on temporal networks relies on approximations (time scale separation, discretization) that may bias the results. We propose a theoretical framework to compute the epidemic threshold in continuous time through the infection propagator approach. We introduce the weak commutation condition allowing the interpretation of annealed networks, activity-driven networks, and time scale separation into one formalism. Our work provides a coherent connection between discrete and continuous time representations applicable to realistic scenarios.
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Affiliation(s)
- Eugenio Valdano
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012 Paris, France
| | - Michele Re Fiorentin
- Center for Sustainable Future Technologies, CSFT@PoliTo, Istituto Italiano di Tecnologia, corso Trento 21, 10129 Torino, Italy
| | - Chiara Poletto
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012 Paris, France
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Epidémiologie et de Santé Publique IPLESP, F75012 Paris, France
- ISI Foundation, 10126 Torino, Italy
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14
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Leu ST, Godfrey SS. Advances from the nexus of animal behaviour and pathogen transmission: new directions and opportunities using contact networks. BEHAVIOUR 2018. [DOI: 10.1163/1568539x-00003507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Abstract
Contact network models have enabled significant advances in understanding the influence of behaviour on parasite and pathogen transmission. They are an important tool that links variation in individual behaviour, to epidemiological consequences at the population level. Here, in our introduction to this special issue, we highlight the importance of applying network approaches to disease ecological and epidemiological questions, and how this has provided a much deeper understanding of these research areas. Recent advances in tracking host behaviour (bio-logging: e.g., GPS tracking, barcoding) and tracking pathogens (high-resolution sequencing), as well as methodological advances (multi-layer networks, computational techniques) started producing exciting new insights into disease transmission through contact networks. We discuss some of the exciting directions that the field is taking, some of the challenges, and importantly the opportunities that lie ahead. For instance, we suggest to integrate multiple transmission pathways, multiple pathogens, and in some systems, multiple host species, into the next generation of network models. Corresponding opportunities exist in utilising molecular techniques, such as high-resolution sequencing, to establish causality in network connectivity and disease outcomes. Such novel developments and the continued integration of network tools offers a more complete understanding of pathogen transmission processes, their underlying mechanisms and their evolutionary consequences.
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Affiliation(s)
- Stephan T. Leu
- aDepartment of Biological Sciences, Macquarie University, Sydney, Australia. E-mail:
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15
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White LA, Forester JD, Craft ME. Covariation between the physiological and behavioral components of pathogen transmission: host heterogeneity determines epidemic outcomes. OIKOS 2017. [DOI: 10.1111/oik.04527] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Lauren A. White
- Dept of Ecology, Evolution and Behavior; Univ. of Minnesota, 140 Gortner Laboratory; 1479 Gortner Avenue St. Paul MN 55108 USA
| | - James D. Forester
- Dept of Fisheries, Wildlife and Conservation Biology; Univ. of Minnesota; St. Paul MN USA
| | - Meggan E. Craft
- Veterinary Population Medicine, Univ. of Minnesota; St. Paul MN USA
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16
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Rocha LEC, Masuda N, Holme P. Sampling of temporal networks: Methods and biases. Phys Rev E 2017; 96:052302. [PMID: 29347767 DOI: 10.1103/physreve.96.052302] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Indexed: 11/07/2022]
Abstract
Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data.
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Affiliation(s)
- Luis E C Rocha
- Department of Public Health Sciences, Karolinska Institutet, 17177 Stockholm, Sweden and Department of Mathematics, Université de Namur, 5000 Namur, Belgium
| | - Naoki Masuda
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, United Kingdom
| | - Petter Holme
- Institute of Innovative Research, Tokyo Institute of Technology, Tokyo 152-8550, Japan
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17
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Greenbaum G, Fefferman NH. Application of network methods for understanding evolutionary dynamics in discrete habitats. Mol Ecol 2017; 26:2850-2863. [DOI: 10.1111/mec.14059] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Revised: 02/03/2017] [Accepted: 02/06/2017] [Indexed: 02/02/2023]
Affiliation(s)
- Gili Greenbaum
- Department of Solar Energy and Environmental Physics and Mitrani Department of Desert Ecology; The Jacob Blaustein Institutes for Desert Research; Ben-Gurion University of the Negev; Midreshet Ben-Gurion 84990 Israel
| | - Nina H. Fefferman
- Department of Ecology and Evolutionary Biology; University of Tennessee; Knoxville 37996 TN USA
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18
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Wu B, Mao S, Wang J, Zhou D. Control of epidemics via social partnership adjustment. Phys Rev E 2017; 94:062314. [PMID: 28085324 PMCID: PMC7217516 DOI: 10.1103/physreve.94.062314] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Indexed: 11/07/2022]
Abstract
Epidemic control is of great importance for human society. Adjusting interacting partners is an effective individualized control strategy. Intuitively, it is done either by shortening the interaction time between susceptible and infected individuals or by increasing the opportunities for contact between susceptible individuals. Here, we provide a comparative study on these two control strategies by establishing an epidemic model with nonuniform stochastic interactions. It seems that the two strategies should be similar, since shortening the interaction time between susceptible and infected individuals somehow increases the chances for contact between susceptible individuals. However, analytical results indicate that the effectiveness of the former strategy sensitively depends on the infectious intensity and the combinations of different interaction rates, whereas the latter one is quite robust and efficient. Simulations are shown to verify our analytical predictions. Our work may shed light on the strategic choice of disease control.
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Affiliation(s)
- Bin Wu
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, People's Republic of China
| | - Shanjun Mao
- School of Mathematical Sciences, Xiamen University, Xiamen 361005, People's Republic of China
| | - Jiazeng Wang
- Department of Mathematics, Beijing Technology and Business University, Beijing 100048, People's Republic of China
| | - Da Zhou
- School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computation, Xiamen University, Xiamen 361005, People's Republic of China
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19
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Widgren S, Engblom S, Bauer P, Frössling J, Emanuelson U, Lindberg A. Data-driven network modelling of disease transmission using complete population movement data: spread of VTEC O157 in Swedish cattle. Vet Res 2016; 47:81. [PMID: 27515697 PMCID: PMC4982012 DOI: 10.1186/s13567-016-0366-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Accepted: 07/18/2016] [Indexed: 11/10/2022] Open
Abstract
European Union legislation requires member states to keep national databases of all bovine animals. This allows for disease spread models that includes the time-varying contact network and population demographic. However, performing data-driven simulations with a high degree of detail are computationally challenging. We have developed an efficient and flexible discrete-event simulator SimInf for stochastic disease spread modelling that divides work among multiple processors to accelerate the computations. The model integrates disease dynamics as continuous-time Markov chains and livestock data as events. In this study, all Swedish livestock data (births, movements and slaughter) from July 1st 2005 to December 31st 2013 were included in the simulations. Verotoxigenic Escherichia coli O157:H7 (VTEC O157) are capable of causing serious illness in humans. Cattle are considered to be the main reservoir of the bacteria. A better understanding of the epidemiology in the cattle population is necessary to be able to design and deploy targeted measures to reduce the VTEC O157 prevalence and, subsequently, human exposure. To explore the spread of VTEC O157 in the entire Swedish cattle population during the period under study, a within- and between-herd disease spread model was used. Real livestock data was incorporated to model demographics of the population. Cattle were moved between herds according to real movement data. The results showed that the spatial pattern in prevalence may be due to regional differences in livestock movements. However, the movements, births and slaughter of cattle could not explain the temporal pattern of VTEC O157 prevalence in cattle, despite their inherently distinct seasonality.
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Affiliation(s)
- Stefan Widgren
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
- Department of Disease Control and Epidemiology, National Veterinary Institute, 751 89 Uppsala, Sweden
| | - Stefan Engblom
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden
| | - Pavol Bauer
- Division of Scientific Computing, Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden
| | - Jenny Frössling
- Department of Disease Control and Epidemiology, National Veterinary Institute, 751 89 Uppsala, Sweden
- Department of Animal Environment and Health, Swedish University of Agricultural Sciences, Box 234, 532 23 Skara, Sweden
| | - Ulf Emanuelson
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden
| | - Ann Lindberg
- Department of Disease Control and Epidemiology, National Veterinary Institute, 751 89 Uppsala, Sweden
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20
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Pinter-Wollman N, Keiser CN, Wollman R, Pruitt JN. The Effect of Keystone Individuals on Collective Outcomes Can Be Mediated through Interactions or Behavioral Persistence. Am Nat 2016; 188:240-52. [PMID: 27420788 PMCID: PMC5475371 DOI: 10.1086/687235] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Collective behavior emerges from interactions among group members who often vary in their behavior. The presence of just one or a few keystone individuals, such as leaders or tutors, may have a large effect on collective outcomes. These individuals can catalyze behavioral changes in other group members, thus altering group composition and collective behavior. The influence of keystone individuals on group function may lead to trade-offs between ecological situations, because the behavioral composition they facilitate may be suitable in one situation but not another. We use computer simulations to examine various mechanisms that allow keystone individuals to exert their influence on group members. We further discuss a trade-off between two potentially conflicting collective outcomes, cooperative prey attack and disease dynamics. Our simulations match empirical data from a social spider system and produce testable predictions for the causes and consequences of the influence of keystone individuals on group composition and collective outcomes. We find that a group's behavioral composition can be impacted by the keystone individual through changes to interaction patterns or behavioral persistence over time. Group behavioral composition and the mechanisms that drive the distribution of phenotypes influence collective outcomes and lead to trade-offs between disease dynamics and cooperative prey attack.
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Affiliation(s)
- Noa Pinter-Wollman
- BioCircuits Institute, University of California, San Diego, La Jolla, California 92093
- San Diego Center for Systems Biology, University of California, San Diego, La Jolla, California 92093
| | - Carl N. Keiser
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Roy Wollman
- San Diego Center for Systems Biology, University of California, San Diego, La Jolla, California 92093
- Department of Chemistry and Biochemistry and Section for Cellular and Developmental Biology, University of California, San Diego, La Jolla, California 92093
| | - Jonathan N. Pruitt
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, California 93106
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21
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Keiser CN, Pinter-Wollman N, Augustine DA, Ziemba MJ, Hao L, Lawrence JG, Pruitt JN. Individual differences in boldness influence patterns of social interactions and the transmission of cuticular bacteria among group-mates. Proc Biol Sci 2016; 283:20160457. [PMID: 27097926 PMCID: PMC4855390 DOI: 10.1098/rspb.2016.0457] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 03/30/2016] [Indexed: 12/15/2022] Open
Abstract
Despite the importance of host attributes for the likelihood of associated microbial transmission, individual variation is seldom considered in studies of wildlife disease. Here, we test the influence of host phenotypes on social network structure and the likelihood of cuticular bacterial transmission from exposed individuals to susceptible group-mates using female social spiders (Stegodyphus dumicola). Based on the interactions of resting individuals of known behavioural types, we assessed whether individuals assorted according to their behavioural traits. We found that individuals preferentially interacted with individuals of unlike behavioural phenotypes. We next applied a green fluorescent protein-transformed cuticular bacterium,Pantoeasp., to individuals and allowed them to interact with an unexposed colony-mate for 24 h. We found evidence for transmission of bacteria in 55% of cases. The likelihood of transmission was influenced jointly by the behavioural phenotypes of both the exposed and susceptible individuals: transmission was more likely when exposed spiders exhibited higher 'boldness' relative to their colony-mate, and when unexposed individuals were in better body condition. Indirect transmission via shared silk took place in only 15% of cases. Thus, bodily contact appears key to transmission in this system. These data represent a fundamental step towards understanding how individual traits influence larger-scale social and epidemiological dynamics.
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Affiliation(s)
- Carl N Keiser
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Noa Pinter-Wollman
- BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093, USA
| | - David A Augustine
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Michael J Ziemba
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Lingran Hao
- BioCircuits Institute, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jeffrey G Lawrence
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Jonathan N Pruitt
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260, USA Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, CA 93106, USA
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22
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Porphyre T, Boden LA, Correia-Gomes C, Auty HK, Gunn GJ, Woolhouse MEJ. Using national movement databases to help inform responses to swine disease outbreaks in Scotland: the impact of uncertainty around incursion time. Sci Rep 2016; 6:20258. [PMID: 26833241 PMCID: PMC4735280 DOI: 10.1038/srep20258] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2015] [Accepted: 12/30/2015] [Indexed: 11/09/2022] Open
Abstract
Modelling is an important component of contingency planning and control of disease outbreaks. Dynamic network models are considered more useful than static models because they capture important dynamic patterns of farm behaviour as evidenced through animal movements. This study evaluates the usefulness of a dynamic network model of swine fever to predict pre-detection spread via movements of pigs, when there may be considerable uncertainty surrounding the time of incursion of infection. It explores the utility and limitations of animal movement data to inform such models and as such, provides some insight into the impact of improving traceability through real-time animal movement reporting and the use of electronic animal movement databases. The study concludes that the type of premises and uncertainty of the time of disease incursion will affect model accuracy and highlights the need for improvements in these areas.
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Affiliation(s)
- Thibaud Porphyre
- Centre for Immunity, Infection and Evolution, University of Edinburgh, King's Buildings, Edinburgh, UK
| | - Lisa A Boden
- School of Veterinary Medicine, Boyd Orr Centre for Population and Ecosystem Health, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Carla Correia-Gomes
- Epidemiology Research Unit, SRUC, Drummondhill, Stratherrick Road, Inverness, UK
| | - Harriet K Auty
- Epidemiology Research Unit, SRUC, Drummondhill, Stratherrick Road, Inverness, UK
| | - George J Gunn
- Epidemiology Research Unit, SRUC, Drummondhill, Stratherrick Road, Inverness, UK
| | - Mark E J Woolhouse
- Centre for Immunity, Infection and Evolution, University of Edinburgh, King's Buildings, Edinburgh, UK
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23
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White LA, Forester JD, Craft ME. Using contact networks to explore mechanisms of parasite transmission in wildlife. Biol Rev Camb Philos Soc 2015; 92:389-409. [DOI: 10.1111/brv.12236] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 10/08/2015] [Accepted: 10/12/2015] [Indexed: 12/21/2022]
Affiliation(s)
- Lauren A. White
- Department of Ecology, Evolution and Behaviour University of Minnesota 140 Gortner Laboratory, 1479 Gortner Avenue St. Paul MN 55108 U.S.A
| | - James D. Forester
- Department of Fisheries, Wildlife and Conservation Biology University of Minnesota 135 Skok Hall, 2003 Upper Buford Circle St. Paul MN 55108 U.S.A
| | - Meggan E. Craft
- Department of Veterinary Population Medicine University of Minnesota 225 Veterinary Medical Center, 1365 Gortner Avenue St. Paul MN 55108 U.S.A
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24
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Experimental resource pulses influence social-network dynamics and the potential for information flow in tool-using crows. Nat Commun 2015; 6:7197. [PMID: 26529116 PMCID: PMC4659832 DOI: 10.1038/ncomms8197] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Accepted: 04/16/2015] [Indexed: 11/09/2022] Open
Abstract
Social-network dynamics have profound consequences for biological processes such as information flow, but are notoriously difficult to measure in the wild. We used novel transceiver technology to chart association patterns across 19 days in a wild population of the New Caledonian crow--a tool-using species that may socially learn, and culturally accumulate, tool-related information. To examine the causes and consequences of changing network topology, we manipulated the environmental availability of the crows' preferred tool-extracted prey, and simulated, in silico, the diffusion of information across field-recorded time-ordered networks. Here we show that network structure responds quickly to environmental change and that novel information can potentially spread rapidly within multi-family communities, especially when tool-use opportunities are plentiful. At the same time, we report surprisingly limited social contact between neighbouring crow communities. Such scale dependence in information-flow dynamics is likely to influence the evolution and maintenance of material cultures.
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25
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Herrera M, Armelini G, Salvaj E. Understanding Social Contagion in Adoption Processes Using Dynamic Social Networks. PLoS One 2015; 10:e0140891. [PMID: 26505473 PMCID: PMC4624715 DOI: 10.1371/journal.pone.0140891] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 10/01/2015] [Indexed: 11/19/2022] Open
Abstract
There are many studies in the marketing and diffusion literature of the conditions in which social contagion affects adoption processes. Yet most of these studies assume that social interactions do not change over time, even though actors in social networks exhibit different likelihoods of being influenced across the diffusion period. Rooted in physics and epidemiology theories, this study proposes a Susceptible Infectious Susceptible (SIS) model to assess the role of social contagion in adoption processes, which takes changes in social dynamics over time into account. To study the adoption over a span of ten years, the authors used detailed data sets from a community of consumers and determined the importance of social contagion, as well as how the interplay of social and non-social influences from outside the community drives adoption processes. Although social contagion matters for diffusion, it is less relevant in shaping adoption when the study also includes social dynamics among members of the community. This finding is relevant for managers and entrepreneurs who trust in word-of-mouth marketing campaigns whose effect may be overestimated if marketers fail to acknowledge variations in social interactions.
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Affiliation(s)
- Mauricio Herrera
- Facultad de Ingeniería, Universidad del Desarrollo, Santiago, Chile
| | | | - Erica Salvaj
- Facultad de Economía y Negocios, Universidad del Desarrollo, Santiago, Chile
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26
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Holme P. Information content of contact-pattern representations and predictability of epidemic outbreaks. Sci Rep 2015; 5:14462. [PMID: 26403504 PMCID: PMC4585889 DOI: 10.1038/srep14462] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Accepted: 08/27/2015] [Indexed: 11/29/2022] Open
Abstract
To understand the contact patterns of a population--who is in contact with whom, and when the contacts happen--is crucial for modeling outbreaks of infectious disease. Traditional theoretical epidemiology assumes that any individual can meet any with equal probability. A more modern approach, network epidemiology, assumes people are connected into a static network over which the disease spreads. Newer yet, temporal network epidemiology, includes the time in the contact representations. In this paper, we investigate the effect of these successive inclusions of more information. Using empirical proximity data, we study both outbreak sizes from unknown sources, and from known states of ongoing outbreaks. In the first case, there are large differences going from a fully mixed simulation to a network, and from a network to a temporal network. In the second case, differences are smaller. We interpret these observations in terms of the temporal network structure of the data sets. For example, a fast overturn of nodes and links seem to make the temporal information more important.
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Affiliation(s)
- Petter Holme
- Department of Energy Science, Sungkyunkwan University, Suwon 440-746, Korea
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27
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Gallos LK, Fefferman NH. The Effect of Disease-Induced Mortality on Structural Network Properties. PLoS One 2015; 10:e0136704. [PMID: 26313926 PMCID: PMC4552173 DOI: 10.1371/journal.pone.0136704] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Accepted: 08/06/2015] [Indexed: 12/17/2022] Open
Abstract
As the understanding of the importance of social contact networks in the spread of infectious diseases has increased, so has the interest in understanding the feedback process of the disease altering the social network. While many studies have explored the influence of individual epidemiological parameters and/or underlying network topologies on the resulting disease dynamics, we here provide a systematic overview of the interactions between these two influences on population-level disease outcomes. We show that the sensitivity of the population-level disease outcomes to the combination of epidemiological parameters that describe the disease are critically dependent on the topological structure of the population's contact network. We introduce a new metric for assessing disease-driven structural damage to a network as a population-level outcome. Lastly, we discuss how the expected individual-level disease burden is influenced by the complete suite of epidemiological characteristics for the circulating disease and the ongoing process of network compromise. Our results have broad implications for prediction and mitigation of outbreaks in both natural and human populations.
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Affiliation(s)
- Lazaros K. Gallos
- Department of Ecology, Evolution, and Natural Resources, Rutgers University - New Brunswick, NJ 08901, United States of America
- DIMACS, Rutgers University - Piscataway, NJ 08854, United States of America
| | - Nina H. Fefferman
- Department of Ecology, Evolution, and Natural Resources, Rutgers University - New Brunswick, NJ 08901, United States of America
- DIMACS, Rutgers University - Piscataway, NJ 08854, United States of America
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28
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Chen S, Ilany A, White BJ, Sanderson MW, Lanzas C. Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals? PLoS One 2015; 10:e0129253. [PMID: 26107251 PMCID: PMC4479463 DOI: 10.1371/journal.pone.0129253] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2014] [Accepted: 05/06/2015] [Indexed: 11/22/2022] Open
Abstract
Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways.
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Affiliation(s)
- Shi Chen
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, 27607, United States of America
- * E-mail:
| | - Amiyaal Ilany
- Department of Biology, University of Pennsylvania, Philadelphia, PA, 19104, United States of America
| | - Brad J. White
- College of Veterinary Medicine, Kansas State University, Manhattan, KS, 66506, United States of America
| | - Michael W. Sanderson
- College of Veterinary Medicine, Kansas State University, Manhattan, KS, 66506, United States of America
| | - Cristina Lanzas
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, 27607, United States of America
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29
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Enns EA, Brandeau ML. Link removal for the control of stochastically evolving epidemics over networks: a comparison of approaches. J Theor Biol 2015; 371:154-65. [PMID: 25698229 DOI: 10.1016/j.jtbi.2015.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 11/12/2014] [Accepted: 02/04/2015] [Indexed: 10/24/2022]
Abstract
For many communicable diseases, knowledge of the underlying contact network through which the disease spreads is essential to determining appropriate control measures. When behavior change is the primary intervention for disease prevention, it is important to understand how to best modify network connectivity using the limited resources available to control disease spread. We describe and compare four algorithms for selecting a limited number of links to remove from a network: two "preventive" approaches (edge centrality, R0 minimization), where the decision of which links to remove is made prior to any disease outbreak and depends only on the network structure; and two "reactive" approaches (S-I edge centrality, optimal quarantining), where information about the initial disease states of the nodes is incorporated into the decision of which links to remove. We evaluate the performance of these algorithms in minimizing the total number of infections that occur over the course of an acute outbreak of disease. We consider different network structures, including both static and dynamic Erdös-Rényi random networks with varying levels of connectivity, a real-world network of residential hotels connected through injection drug use, and a network exhibiting community structure. We show that reactive approaches outperform preventive approaches in averting infections. Among reactive approaches, removing links in order of S-I edge centrality is favored when the link removal budget is small, while optimal quarantining performs best when the link removal budget is sufficiently large. The budget threshold above which optimal quarantining outperforms the S-I edge centrality algorithm is a function of both network structure (higher for unstructured Erdös-Rényi random networks compared to networks with community structure or the real-world network) and disease infectiousness (lower for highly infectious diseases). We conduct a value-of-information analysis of knowing which nodes are initially infected by comparing the performance improvement achieved by reactive over preventive strategies. We find that such information is most valuable for moderate budget levels, with increasing value as disease spread becomes more likely (due to either increased connectedness of the network or increased infectiousness of the disease).
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Affiliation(s)
- Eva A Enns
- Division of Health Policy and Management, University of Minnesota School of Public Health, 420 Delaware St. SE, MMC 729, Minneapolis, MN 55455, USA.
| | - Margaret L Brandeau
- Department of Management Science and Engineering, Stanford University, 475 Via Ortega, Stanford, CA 94305, USA.
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30
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Birth and death of links control disease spreading in empirical contact networks. Sci Rep 2014; 4:4999. [PMID: 24851942 PMCID: PMC4031628 DOI: 10.1038/srep04999] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Accepted: 04/08/2014] [Indexed: 12/03/2022] Open
Abstract
We investigate what structural aspects of a collection of twelve empirical temporal networks of human contacts are important to disease spreading. We scan the entire parameter spaces of the two canonical models of infectious disease epidemiology—the Susceptible-Infectious-Susceptible (SIS) and Susceptible-Infectious-Removed (SIR) models. The results from these simulations are compared to reference data where we eliminate structures in the interevent intervals, the time to the first contact in the data, or the time from the last contact to the end of the sampling. The picture we find is that the birth and death of links, and the total number of contacts over a link, are essential to predict outbreaks. On the other hand, the exact times of contacts between the beginning and end, or the interevent interval distribution, do not matter much. In other words, a simplified picture of these empirical data sets that suffices for epidemiological purposes is that links are born, is active with some intensity, and die.
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31
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Pautasso M, Jeger MJ. Network epidemiology and plant trade networks. AOB PLANTS 2014; 6:plu007. [PMID: 24790128 PMCID: PMC4038442 DOI: 10.1093/aobpla/plu007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2013] [Accepted: 02/11/2014] [Indexed: 05/29/2023]
Abstract
Models of epidemics in complex networks are improving our predictive understanding of infectious disease outbreaks. Nonetheless, applying network theory to plant pathology is still a challenge. This overview summarizes some key developments in network epidemiology that are likely to facilitate its application in the study and management of plant diseases. Recent surveys have provided much-needed datasets on contact patterns and human mobility in social networks, but plant trade networks are still understudied. Human (and plant) mobility levels across the planet are unprecedented-there is thus much potential in the use of network theory by plant health authorities and researchers. Given the directed and hierarchical nature of plant trade networks, there is a need for plant epidemiologists to further develop models based on undirected and homogeneous networks. More realistic plant health scenarios would also be obtained by developing epidemic models in dynamic, rather than static, networks. For plant diseases spread by the horticultural and ornamental trade, there is the challenge of developing spatio-temporal epidemic simulations integrating network data. The use of network theory in plant epidemiology is a promising avenue and could contribute to anticipating and preventing plant health emergencies such as European ash dieback.
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Affiliation(s)
- Marco Pautasso
- Forest Pathology and Dendrology, Institute of Integrative Biology, ETHZ, Zurich, Switzerland
| | - Mike J. Jeger
- Division of Ecology and Evolution & Centre for Environmental Policy, Imperial College London, London, UK
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32
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Parikh N, Youssef M, Swarup S, Eubank S. Modeling the effect of transient populations on epidemics in Washington DC. Sci Rep 2013; 3:3152. [PMID: 24193263 PMCID: PMC3818653 DOI: 10.1038/srep03152] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Accepted: 09/23/2013] [Indexed: 11/09/2022] Open
Abstract
Large numbers of transients visit big cities, where they come into contact with many people at crowded areas. However, epidemiological studies have not paid much attention to the role of this subpopulation in disease spread. We evaluate the effect of transients on epidemics by extending a synthetic population model for the Washington DC metro area to include leisure and business travelers. A synthetic population is obtained by combining multiple data sources to build a detailed minute-by-minute simulation of population interaction resulting in a contact network. We simulate an influenza-like illness over the contact network to evaluate the effects of transients on the number of infected residents. We find that there are significantly more infections when transients are considered. Since much population mixing happens at major tourism locations, we evaluate two targeted interventions: closing museums and promoting healthy behavior (such as the use of hand sanitizers, covering coughs, etc.) at museums. Surprisingly, closing museums has no beneficial effect. However, promoting healthy behavior at the museums can both reduce and delay the epidemic peak. We analytically derive the reproductive number and perform stability analysis using an ODE-based model.
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Affiliation(s)
- Nidhi Parikh
- Networks Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, USA
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33
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Holme P. Epidemiologically optimal static networks from temporal network data. PLoS Comput Biol 2013; 9:e1003142. [PMID: 23874184 PMCID: PMC3715509 DOI: 10.1371/journal.pcbi.1003142] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2013] [Accepted: 06/02/2013] [Indexed: 11/24/2022] Open
Abstract
One of network epidemiology's central assumptions is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We discuss these observations in the context of the temporal and topological structure of the data sets. To understand how diseases spread in a population, it is important to study the network of people in contact. Many methods to model epidemic outbreaks make the assumption that one can treat this network as static. In reality, we know that contact patterns between people change in time, and old contacts are soon irrelevant—it does not matter that we know Marie Antoinette's lovers to understand the HIV epidemic. This paper investigates methods for constructing networks of people that are as relevant as possible for disease spreading. The most promising method we call exponential-threshold network works by letting contacts contribute less, the further from the beginning of an outbreak they take place. We investigate the methods both on artificial models of the contact patterns and empirical data. Except searching for the optimal network representation, we also investigate how the structure of the original data set affects the performance of the representations.
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Affiliation(s)
- Petter Holme
- Department of Energy Science, Sungkyunkwan University, Suwon, Korea.
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34
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Masuda N, Holme P. Predicting and controlling infectious disease epidemics using temporal networks. F1000PRIME REPORTS 2013; 5:6. [PMID: 23513178 PMCID: PMC3590785 DOI: 10.12703/p5-6] [Citation(s) in RCA: 122] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments.
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Affiliation(s)
- Naoki Masuda
- Department of Mathematical Informatics, The University of Tokyo7-3-1 Hongo Bunkyo, Tokyo 113-8656Japan
| | - Petter Holme
- Department of Energy Science, Sungkyunkwan UniversitySuwon 440-746Korea
- IceLab, Department of Physics, Umeå University901 87 UmeåSweden
- Department of Sociology, Stockholm University106 91 StockholmSweden
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Hock K, Fefferman NH. Social organization patterns can lower disease risk without associated disease avoidance or immunity. ECOLOGICAL COMPLEXITY 2012. [DOI: 10.1016/j.ecocom.2012.09.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Zhou J, Chung NN, Chew LY, Lai CH. Epidemic spreading induced by diversity of agents' mobility. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2012; 86:026115. [PMID: 23005833 DOI: 10.1103/physreve.86.026115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2012] [Revised: 07/29/2012] [Indexed: 06/01/2023]
Abstract
In this paper, we study the impact of the preference of an individual for public transport on the spread of infectious disease, through a quantity known as the public mobility. Our theoretical and numerical results based on a constructed model reveal that if the average public mobility of the agents is fixed, an increase in the diversity of the agents' public mobility reduces the epidemic threshold, beyond which an enhancement in the rate of infection is observed. Our findings provide an approach to improve the resistance of a society against infectious disease, while preserving the utilization rate of the public transportation system.
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Affiliation(s)
- Jie Zhou
- Temasek Laboratories, National University of Singapore, Singapore
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The dynamics of sexual contact networks: effects on disease spread and control. Theor Popul Biol 2012; 81:89-96. [PMID: 22248701 DOI: 10.1016/j.tpb.2011.12.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2011] [Revised: 12/19/2011] [Accepted: 12/20/2011] [Indexed: 11/21/2022]
Abstract
Sexually transmitted pathogens persist in populations despite the availability of biomedical interventions and knowledge of behavioural changes that would reduce individual-level risk. While behavioural risk factors are shared between many sexually transmitted infections, the prevalence of these diseases across different risk groups varies. Understanding this heterogeneity and identifying better control strategies depends on an improved understanding of the complex social contact networks over which pathogens spread. To date, most efforts to study the impact of sexual network structure on disease dynamics have focused on static networks. However, the interaction between the dynamics of partnership formation and dissolution and the dynamics of transmission plays a role, both in restricting the effective network accessible to the pathogen, and in modulating the transmission dynamics. We present a simple method to simulate dynamical networks of sexual partnerships. We inform the model using survey data on sexual attitudes and lifestyles, and investigate how the duration of infectiousness changes the effective contact network over which disease may spread. We then simulate several control strategies: screening, vaccination and behavioural interventions. Previous theory and research has advanced the importance of core groups for spread and control of STD. Our work is consistent with the importance of core groups, but extends this idea to consider how the duration of infectiousness associated with a particular pathogen interacts with host behaviours to define these high risk subpopulations. Characteristics of the parts of the network accessible to the pathogen, which represent the network structure of sexual contacts from the "point of view" of the pathogen, are substantially different from those of the network as a whole. The pathogen itself plays an important role in determining this effective network structure; specifically, we find that if the pathogen's duration of infectiousness is short, infection is more concentrated in high-activity, high-concurrency individuals even when all other factors are held constant. Widespread screening programmes would be enhanced by follow-up interventions targeting higher-risk individuals, because screening shortens the expected duration of infectiousness and causes a greater relative decrease in prevalence among lower-activity than in higher-activity individuals. Even for pathogens with longer durations of infectiousness, our findings suggest that targeting vaccination and behavioural interventions towards high-activity individuals provides comparable benefits to population-wide interventions.
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Barmak DH, Dorso CO, Otero M, Solari HG. Dengue epidemics and human mobility. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2011; 84:011901. [PMID: 21867207 DOI: 10.1103/physreve.84.011901] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2011] [Revised: 05/02/2011] [Indexed: 05/31/2023]
Abstract
In this work we explore the effects of human mobility on the dispersion of a vector borne disease. We combine an already presented stochastic model for dengue with a simple representation of the daily motion of humans on a schematic city of 20 × 20 blocks with 100 inhabitants in each block. The pattern of motion of the individuals is described in terms of complex networks in which links connect different blocks and the link length distribution is in accordance with recent findings on human mobility. It is shown that human mobility can turn out to be the main driving force of the disease dispersal.
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Affiliation(s)
- D H Barmak
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and IFIBA, CONICET, Pabellón I, Ciudad Universitaria, Nuñez, 1428 Buenos Aires, Argentina
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Chui KKH, Cohen SA, Naumova EN. Snowbirds and infection--new phenomena in pneumonia and influenza hospitalizations from winter migration of older adults: a spatiotemporal analysis. BMC Public Health 2011; 11:444. [PMID: 21649919 PMCID: PMC3128025 DOI: 10.1186/1471-2458-11-444] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2011] [Accepted: 06/07/2011] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Despite advances in surveillance and prevention, pneumonia and influenza (P&I) remain among the leading causes of mortality in the United States. Elderly adults experience the most severe morbidity from influenza-associated diseases, and have the highest rates of seasonal migration within the U.S. compared to other subpopulations. The objective of this study is to assess spatiotemporal patterns in influenza-associated hospitalizations in the elderly, by time, geography, and intensity of P&I. Given the high seasonal migration of individuals to Florida, this state was examined more closely using harmonic regression to assess spatial and temporal patterns of P&I hospitalizations by state of residence. METHODS Data containing all Medicare-eligible hospitalizations in the United States for 1991-2006 with P&I (ICD-9-CM codes 480-487) were abstracted for the 65+ population. Hospitalizations were classified by state of residence, provider state, and date of admissions, specifically comparing those admitted between October and March to those admitted between April and September. We then compared the hospitalization profile data of Florida residents with that of out-of-state residents by state of primary residence and time of year (in-season or out-of-season). RESULTS We observed distinct seasonal patterns of nonresident P&I hospitalizations, especially comparing typical winter destination states, such as California, Arizona, Texas, and Florida, to other states. Although most other states generally experienced a higher proportion of non-resident P&I during the summer months (April-September), these states had higher nonresident P&I during the traditional peak influenza season (October-March). CONCLUSIONS This study is among the first to quantify spatiotemporal P&I hospitalization patterns in the elderly, focusing on the change of patterns that are possibly due to seasonal population migration. Understanding migration and influenza-associated disease patterns in this vulnerable population is critical to prepare for and potentially prevent influenza outbreaks in this vulnerable population.
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Affiliation(s)
- Kenneth KH Chui
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, Massachusetts, USA
| | - Steven A Cohen
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, Massachusetts, USA
| | - Elena N Naumova
- Department of Civil and Environmental Engineering, Tufts University School of Engineering, Medford, Massachusetts, USA
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Bansal S, Read J, Pourbohloul B, Meyers LA. The dynamic nature of contact networks in infectious disease epidemiology. JOURNAL OF BIOLOGICAL DYNAMICS 2010; 4:478-89. [PMID: 22877143 DOI: 10.1080/17513758.2010.503376] [Citation(s) in RCA: 88] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Although contact network models have yielded important insights into infectious disease transmission and control throughout the last decade, researchers have just begun to explore the dynamic nature of contact patterns and their epidemiological significance. Most network models have assumed that contacts are static through time. Developing more realistic models of the social interactions that underlie the spread of infectious diseases thus remains an important challenge for both data gatherers and modelers. In this article, we review some recent data-driven and process-driven approaches that capture the dynamics of human contact, and discuss future challenges for the field.
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Affiliation(s)
- Shweta Bansal
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA, USA.
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Schwarzkopf Y, Rákos A, Mukamel D. Epidemic spreading in evolving networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2010; 82:036112. [PMID: 21230144 DOI: 10.1103/physreve.82.036112] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2010] [Revised: 08/24/2010] [Indexed: 05/30/2023]
Abstract
A model for epidemic spreading on rewiring networks is introduced and analyzed for the case of scale free steady state networks. It is found that contrary to what one would have naively expected, the rewiring process typically tends to suppress epidemic spreading. In particular it is found, that as in static networks under a mean-field approximation, rewiring networks with degree distribution exponent γ>3 exhibit a threshold in the infection rate below which epidemics die out in the steady state. However the threshold is higher in the rewiring case. For 2<γ≤3 no such threshold exists, but for small infection rate the steady state density of infected nodes (prevalence) is smaller for rewiring networks.
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Affiliation(s)
- Yonathan Schwarzkopf
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel
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Marceau V, Noël PA, Hébert-Dufresne L, Allard A, Dubé LJ. Adaptive networks: Coevolution of disease and topology. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2010; 82:036116. [PMID: 21230148 DOI: 10.1103/physreve.82.036116] [Citation(s) in RCA: 76] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2010] [Indexed: 05/05/2023]
Abstract
Adaptive networks have been recently introduced in the context of disease propagation on complex networks. They account for the mutual interaction between the network topology and the states of the nodes. Until now, existing models have been analyzed using low complexity analytical formalisms, revealing nevertheless some novel dynamical features. However, current methods have failed to reproduce with accuracy the simultaneous time evolution of the disease and the underlying network topology. In the framework of the adaptive susceptible-infectious-susceptible (SIS) model of Gross [Phys. Rev. Lett. 96, 208701 (2006)]10.1103/PhysRevLett.96.208701, we introduce an improved compartmental formalism able to handle this coevolutionary task successfully. With this approach, we analyze the interplay and outcomes of both dynamical elements, process and structure, on adaptive networks featuring different degree distributions at the initial stage.
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Affiliation(s)
- Vincent Marceau
- Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec, Québec, Canada G1V 0A6
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Abstract
The idea behind adaptive behavioral epidemiology is that groups and individuals respond to the knowledge of a disease threat by changing their habits to avoid interactions with those who are contagious. Network-based models take this adaptive behavior into account by allowing the network to "rewire" its connections.
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Affiliation(s)
- Ira B Schwartz
- Naval Research Laboratory, Nonlinear Systems Dynamics Section, Code 6792, Washington, DC 20375, USA
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Eames KTD, Read JM, Edmunds WJ. Epidemic prediction and control in weighted networks. Epidemics 2008; 1:70-6. [PMID: 21352752 DOI: 10.1016/j.epidem.2008.12.001] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2008] [Revised: 11/25/2008] [Accepted: 12/10/2008] [Indexed: 11/25/2022] Open
Abstract
Contact networks are often used in epidemiological studies to describe the patterns of interactions within a population. Often, such networks merely indicate which individuals interact, without giving any indication of the strength or intensity of interactions. Here, we use weighted networks, in which every connection has an associated weight, to explore the influence of heterogeneous contact strengths on the effectiveness of control measures. We show that, by using contact weights to evaluate an individual's influence on an epidemic, individual infection risk can be estimated and targeted interventions such as preventative vaccination can be applied effectively. We use a diary study of social mixing behaviour to indicate the patterns of contact weights displayed by a real population in a range of different contexts, including physical interactions; we use these data to show that considerations of link weight can in some cases lead to improved interventions in the case of infections that spread through close contact interactions. However, we also see that simpler measures, such as an individual's total number of social contacts or even just their number of contacts during a single day, can lead to great improvements on random vaccination. We therefore conclude that, for many infections, enhanced social contact data can be simply used to improve disease control but that it is not necessary to have full social mixing information in order to enhance interventions.
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Affiliation(s)
- Ken T D Eames
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, UK.
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Shaw LB, Schwartz IB. Fluctuating epidemics on adaptive networks. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2008; 77:066101. [PMID: 18643330 DOI: 10.1103/physreve.77.066101] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2008] [Indexed: 05/04/2023]
Abstract
A model for epidemics on an adaptive network is considered. Nodes follow a susceptible-infective-recovered-susceptible pattern. Connections are rewired to break links from noninfected nodes to infected nodes and are reformed to connect to other noninfected nodes, as the nodes that are not infected try to avoid the infection. Monte Carlo simulation and numerical solution of a mean field model are employed. The introduction of rewiring affects both the network structure and the epidemic dynamics. Degree distributions are altered, and the average distance from a node to the nearest infective increases. The rewiring leads to regions of bistability where either an endemic or a disease-free steady state can exist. Fluctuations around the endemic state and the lifetime of the endemic state are considered. The fluctuations are found to exhibit power law behavior.
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Affiliation(s)
- Leah B Shaw
- Department of Applied Science, College of William and Mary, Williamsburg, Virginia 23187, USA
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