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Valdano E, Colombi D, Poletto C, Colizza V. Epidemic graph diagrams as analytics for epidemic control in the data-rich era. Nat Commun 2023; 14:8472. [PMID: 38123580 PMCID: PMC10733371 DOI: 10.1038/s41467-023-43856-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
COVID-19 highlighted modeling as a cornerstone of pandemic response. But it also revealed that current models may not fully exploit the high-resolution data on disease progression, epidemic surveillance and host behavior, now available. Take the epidemic threshold, which quantifies the spreading risk throughout epidemic emergence, mitigation, and control. Its use requires oversimplifying either disease or host contact dynamics. We introduce the epidemic graph diagrams to overcome this by computing the epidemic threshold directly from arbitrarily complex data on contacts, disease and interventions. A grammar of diagram operations allows to decompose, compare, simplify models with computational efficiency, extracting theoretical understanding. We use the diagrams to explain the emergence of resistant influenza variants in the 2007-2008 season, and demonstrate that neglecting non-infectious prodromic stages of sexually transmitted infections biases the predicted epidemic risk, compromising control. The diagrams are general, and improve our capacity to respond to present and future public health challenges.
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Affiliation(s)
- Eugenio Valdano
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012, Paris, France
| | | | - Chiara Poletto
- Department of Molecular Medicine, University of Padova, 35121, Padova, Italy
| | - Vittoria Colizza
- Sorbonne Université, INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, F75012, Paris, France.
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2
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Shridhar SV, Alexander M, Christakis NA. Characterizing super-spreaders using population-level weighted social networks in rural communities. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2022; 380:20210123. [PMID: 34802276 DOI: 10.1098/rsta.2021.0123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 07/07/2021] [Indexed: 05/22/2023]
Abstract
Sociocentric network maps of entire populations, when combined with data on the nature of constituent dyadic relationships, offer the dual promise of advancing understanding of the relevance of networks for disease transmission and of improving epidemic forecasts. Here, using detailed sociocentric data collected over 4 years in a population of 24 702 people in 176 villages in Honduras, along with diarrhoeal and respiratory disease prevalence, we create a social-network-powered transmission model and identify super-spreading nodes as well as the nodes most vulnerable to infection, using agent-based Monte Carlo network simulations. We predict the extent of outbreaks for communicable diseases based on detailed social interaction patterns. Evidence from three waves of population-level surveys of diarrhoeal and respiratory illness indicates a meaningful positive correlation with the computed super-spreading capability and relative vulnerability of individual nodes. Previous research has identified super-spreaders through retrospective contact tracing or simulated networks. By contrast, our simulations predict that a node's super-spreading capability and its vulnerability in real communities are significantly affected by their connections, the nature of the interaction across these connections, individual characteristics (e.g. age and sex) that affect a person's ability to disperse a pathogen, and also the intrinsic characteristics of the pathogen (e.g. infectious period and latency). This article is part of the theme issue 'Data science approach to infectious disease surveillance'.
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Affiliation(s)
- Shivkumar Vishnempet Shridhar
- School of Engineering and Applied Science, Yale University, 17 Hillhouse Ave, New Haven, CT 06520, USA
- Yale Institute for Network Science, Yale University, 17 Hillhouse Ave, New Haven, CT 06520, USA
| | - Marcus Alexander
- Yale Institute for Network Science, Yale University, 17 Hillhouse Ave, New Haven, CT 06520, USA
| | - Nicholas A Christakis
- Yale Institute for Network Science, Yale University, 17 Hillhouse Ave, New Haven, CT 06520, USA
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3
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Colman E, Colizza V, Hanks EM, Hughes DP, Bansal S. Social fluidity mobilizes contagion in human and animal populations. eLife 2021; 10:62177. [PMID: 34328080 PMCID: PMC8324292 DOI: 10.7554/elife.62177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 06/25/2021] [Indexed: 11/13/2022] Open
Abstract
Humans and other group-living animals tend to distribute their social effort disproportionately. Individuals predominantly interact with a small number of close companions while maintaining weaker social bonds with less familiar group members. By incorporating this behavior into a mathematical model, we find that a single parameter, which we refer to as social fluidity, controls the rate of social mixing within the group. Large values of social fluidity correspond to gregarious behavior, whereas small values signify the existence of persistent bonds between individuals. We compare the social fluidity of 13 species by applying the model to empirical human and animal social interaction data. To investigate how social behavior influences the likelihood of an epidemic outbreak, we derive an analytical expression of the relationship between social fluidity and the basic reproductive number of an infectious disease. For species that form more stable social bonds, the model describes frequency-dependent transmission that is sensitive to changes in social fluidity. As social fluidity increases, animal-disease systems become increasingly density-dependent. Finally, we demonstrate that social fluidity is a stronger predictor of disease outcomes than both group size and connectivity, and it provides an integrated framework for both density-dependent and frequency-dependent transmission.
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Affiliation(s)
- Ewan Colman
- Department of Biology, Georgetown University, Washington, United States.,Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Vittoria Colizza
- INSERM, Sorbonne Université, Institut Pierre Louis d'Épidémiologie et de Santé Publique (IPLESP UMRS 1136), F75012, Paris, France
| | - Ephraim M Hanks
- Department of Statistics, Eberly College of Science, Penn State University, State College, United States
| | - David P Hughes
- Department of Entomology, College of Agricultural Sciences, Penn State University, State College, United States
| | - Shweta Bansal
- Department of Biology, Georgetown University, Washington, United States
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Brommesson P, Sellman S, Beck-Johnson L, Hallman C, Murrieta D, Webb CT, Miller RS, Portacci K, Lindström T. Assessing intrastate shipments from interstate data and expert opinion. ROYAL SOCIETY OPEN SCIENCE 2021; 8:192042. [PMID: 33959304 PMCID: PMC8074939 DOI: 10.1098/rsos.192042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 02/10/2021] [Indexed: 06/12/2023]
Abstract
Live animal shipments are a potential route for transmitting animal diseases between holdings and are crucial when modelling spread of infectious diseases. Yet, complete contact networks are not available in all countries, including the USA. Here, we considered a 10% sample of Interstate Certificate of Veterinary Inspections from 1 year (2009). We focused on distance dependence in contacts and investigated how different functional forms affect estimates of unobserved intrastate shipments. To further enhance our predictions, we included responses from an expert elicitation survey about the proportion of shipments moving intrastate. We used hierarchical Bayesian modelling to estimate parameters describing the kernel and effects of expert data. We considered three functional forms of spatial kernels and the inclusion or exclusion of expert data. The resulting six models were ranked by widely applicable information criterion (WAIC) and deviance information criterion (DIC) and evaluated through within- and out-of-sample validation. We showed that predictions of intrastate shipments were mildly influenced by the functional form of the spatial kernel but kernel shapes that permitted a fat tail at large distances while maintaining a plateau-shaped behaviour at short distances better were preferred. Furthermore, our study showed that expert data may not guarantee enhanced predictions when expert estimates are disparate.
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Affiliation(s)
- Peter Brommesson
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 58183 Linköping, Sweden
| | - Stefan Sellman
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 58183 Linköping, Sweden
| | | | - Clayton Hallman
- Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Deedra Murrieta
- Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Colleen T. Webb
- Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Ryan S. Miller
- Center for Epidemiology and Animal Health, United States Department of Agriculture-Veterinary Services, Fort Collins, CO 80526, USA
| | - Katie Portacci
- Center for Epidemiology and Animal Health, United States Department of Agriculture-Veterinary Services, Fort Collins, CO 80526, USA
| | - Tom Lindström
- Department of Physics, Chemistry and Biology, Division of Theoretical Biology, Linköping University, 58183 Linköping, Sweden
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Holme P. Fast and principled simulations of the SIR model on temporal networks. PLoS One 2021; 16:e0246961. [PMID: 33577564 PMCID: PMC7880429 DOI: 10.1371/journal.pone.0246961] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 01/28/2021] [Indexed: 01/16/2023] Open
Abstract
The Susceptible-Infectious-Recovered (SIR) model is the canonical model of epidemics of infections that make people immune upon recovery. Many of the open questions in computational epidemiology concern the underlying contact structure's impact on models like the SIR model. Temporal networks constitute a theoretical framework capable of encoding structures both in the networks of who could infect whom and when these contacts happen. In this article, we discuss the detailed assumptions behind such simulations-how to make them comparable with analytically tractable formulations of the SIR model, and at the same time, as realistic as possible. We also present a highly optimized, open-source code for this purpose and discuss all steps needed to make the program as fast as possible.
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Affiliation(s)
- Petter Holme
- Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
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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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Stockmaier S, Bolnick DI, Page RA, Carter GG. Sickness effects on social interactions depend on the type of behaviour and relationship. J Anim Ecol 2020; 89:1387-1394. [DOI: 10.1111/1365-2656.13193] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 01/07/2020] [Indexed: 12/17/2022]
Affiliation(s)
- Sebastian Stockmaier
- Department of Integrative Biology University of Texas at Austin Austin TX USA
- Smithsonian Tropical Research Institute Balboa Panama
| | - Daniel I. Bolnick
- Department of Integrative Biology University of Texas at Austin Austin TX USA
- Department of Ecology and Evolutionary Biology University of Connecticut Storrs CT USA
| | | | - Gerald G. Carter
- Smithsonian Tropical Research Institute Balboa Panama
- Department of Evolution, Ecology and Organismal Biology The Ohio State University Columbus OH USA
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Silk MJ, Hodgson DJ, Rozins C, Croft DP, Delahay RJ, Boots M, McDonald RA. Integrating social behaviour, demography and disease dynamics in network models: applications to disease management in declining wildlife populations. Philos Trans R Soc Lond B Biol Sci 2019; 374:20180211. [PMID: 31352885 PMCID: PMC6710568 DOI: 10.1098/rstb.2018.0211] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2019] [Indexed: 02/03/2023] Open
Abstract
The emergence and spread of infections can contribute to the decline and extinction of populations, particularly in conjunction with anthropogenic environmental change. The importance of heterogeneity in processes of transmission, resistance and tolerance is increasingly well understood in theory, but empirical studies that consider both the demographic and behavioural implications of infection are scarce. Non-random mixing of host individuals can impact the demographic thresholds that determine the amplification or attenuation of disease prevalence. Risk assessment and management of disease in threatened wildlife populations must therefore consider not just host density, but also the social structure of host populations. Here we integrate the most recent developments in epidemiological research from a demographic and social network perspective, and synthesize the latest developments in social network modelling for wildlife disease, to explore their applications to disease management in populations in decline and at risk of extinction. We use simulated examples to support our key points and reveal how disease-management strategies can and should exploit both behavioural and demographic information to prevent or control the spread of disease. Our synthesis highlights the importance of considering the combined impacts of demographic and behavioural processes in epidemics to successful disease management in a conservation context. This article is part of the theme issue 'Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation'.
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Affiliation(s)
- Matthew J. Silk
- Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Penryn, UK
- Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn, UK
| | - David J. Hodgson
- Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Penryn, UK
| | - Carly Rozins
- Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Penryn, UK
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Darren P. Croft
- Centre for Research in Animal Behaviour, University of Exeter, Exeter, UK
| | - Richard J. Delahay
- National Wildlife Management Centre, Animal and Plant Health Agency, Nympsfield, UK
| | - Mike Boots
- Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Penryn, UK
- Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA
| | - Robbie A. McDonald
- Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn, UK
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Abstract
Assume one has the capability of determining whether a node in a network is infectious or not by probing it. Then problem of optimizing sentinel surveillance in networks is to identify the nodes to probe such that an emerging disease outbreak can be discovered early or reliably. Whether the emphasis should be on early or reliable detection depends on the scenario in question. We investigate three objective measures from the literature quantifying the performance of nodes in sentinel surveillance: the time to detection or extinction, the time to detection, and the frequency of detection. As a basis for the comparison, we use the susceptible-infectious-recovered model on static and temporal networks of human contacts. We show that, for some regions of parameter space, the three objective measures can rank the nodes very differently. This means sentinel surveillance is a class of problems, and solutions need to chose an objective measure for the particular scenario in question. As opposed to other problems in network epidemiology, we draw similar conclusions from the static and temporal networks. Furthermore, we do not find one type of network structure that predicts the objective measures, i.e., that depends both on the data set and the SIR parameter values.
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Affiliation(s)
- Petter Holme
- Institute of Innovative Research, Tokyo Institute of Technology, Nagatsuta-cho 4259, Midori-ku, Yokohama, Kanagawa 226-8503, Japan
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