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Pandey A, Wojan C, Feuka A, Craft ME, Manlove K, Pepin KM. The influence of social and spatial processes on the epidemiology of environmentally transmitted pathogens in wildlife: implications for management. Philos Trans R Soc Lond B Biol Sci 2024; 379:20220532. [PMID: 39230447 DOI: 10.1098/rstb.2022.0532] [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: 02/09/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 09/05/2024] Open
Abstract
Social and spatial structures of host populations play important roles in pathogen transmission. For environmentally transmitted pathogens, the host space use interacts with both the host social structure and the pathogen's environmental persistence (which determines the time-lag across which two hosts can transmit). Together, these factors shape the epidemiological dynamics of environmentally transmitted pathogens. While the importance of both social and spatial structures and environmental pathogen persistence has long been recognized in epidemiology, they are often considered separately. A better understanding of how these factors interact to determine disease dynamics is required for developing robust surveillance and management strategies. Here, we use a simple agent-based model where we vary host mobility (spatial), host gregariousness (social) and pathogen decay (environmental persistence), each from low to high levels to uncover how they affect epidemiological dynamics. By comparing epidemic peak, time to epidemic peak and final epidemic size, we show that longer infectious periods, higher group mobility, larger group size and longer pathogen persistence lead to larger, faster growing outbreaks, and explore how these processes interact to determine epidemiological outcomes such as the epidemic peak and the final epidemic size. We identify general principles that can be used for planning surveillance and control for wildlife host-pathogen systems with environmental transmission across a range of spatial behaviour, social structure and pathogen decay rates. This article is part of the theme issue 'The spatial-social interface: a theoretical and empirical integration'.
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Affiliation(s)
- Aakash Pandey
- Department of Fisheries and Wildlife, Michigan State University , East Lansing, MI 48824, USA
| | - Chris Wojan
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul , MN 55108, USA
| | - Abigail Feuka
- National Wildlife Research Center, USDA-APHIS, Fort Collins, CO 80521, USA
| | - Meggan E Craft
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul , MN 55108, USA
| | - Kezia Manlove
- Department of Wildland Resources and Ecology Center, Utah State University, 5200 Old Main Hill , Logan, UT 84322, USA
| | - Kim M Pepin
- National Wildlife Research Center, USDA-APHIS, Fort Collins, CO 80521, USA
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2
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Barrett TM, Titcomb GC, Janko MM, Pender M, Kauffman K, Solis A, Randriamoria MT, Young HS, Mucha PJ, Moody J, Kramer RA, Soarimalala V, Nunn CL. Disentangling social, environmental, and zoonotic transmission pathways of a gastrointestinal protozoan (Blastocystis spp.) in northeast Madagascar. AMERICAN JOURNAL OF BIOLOGICAL ANTHROPOLOGY 2024:e25030. [PMID: 39287986 DOI: 10.1002/ajpa.25030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 08/22/2024] [Accepted: 09/05/2024] [Indexed: 09/19/2024]
Abstract
OBJECTIVES Understanding disease transmission is a fundamental challenge in ecology. We used transmission potential networks to investigate whether a gastrointestinal protozoan (Blastocystis spp.) is spread through social, environmental, and/or zoonotic pathways in rural northeast Madagascar. MATERIALS AND METHODS We obtained survey data, household GPS coordinates, and fecal samples from 804 participants. Surveys inquired about social contacts, agricultural activity, and sociodemographic characteristics. Fecal samples were screened for Blastocystis using DNA metabarcoding. We also tested 133 domesticated animals for Blastocystis. We used network autocorrelation models and permutation tests (network k-test) to determine whether networks reflecting different transmission pathways predicted infection. RESULTS We identified six distinct Blastocystis subtypes among study participants and their domesticated animals. Among the 804 human participants, 74% (n = 598) were positive for at least one Blastocystis subtype. Close proximity to infected households was the most informative predictor of infection with any subtype (model averaged OR [95% CI]: 1.56 [1.33-1.82]), and spending free time with infected participants was not an informative predictor of infection (model averaged OR [95% CI]: 0.95 [0.82-1.10]). No human participant was infected with the same subtype as the domesticated animals they owned. DISCUSSION Our findings suggest that Blastocystis is most likely spread through environmental pathways within villages, rather than through social or animal contact. The most likely mechanisms involve fecal contamination of the environment by infected individuals or shared food and water sources. These findings shed new light on human-pathogen ecology and mechanisms for reducing disease transmission in rural, low-income settings.
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Affiliation(s)
- Tyler M Barrett
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, USA
| | - Georgia C Titcomb
- Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, Colorado, USA
| | - Mark M Janko
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
| | - Michelle Pender
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
| | - Kayla Kauffman
- Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, California, USA
| | - Alma Solis
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, USA
| | - Maheriniaina Toky Randriamoria
- Association Vahatra, Antananarivo, Madagascar
- Zoologie et Biodiversité Animale, Domaine Sciences et Technologies, Université d'Antananarivo, Antananarivo, Madagascar
| | - Hillary S Young
- Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, California, USA
| | - Peter J Mucha
- Department of Mathematics, Dartmouth College, Hanover, New Hampshire, USA
| | - James Moody
- Department of Sociology, Duke University, Durham, North Carolina, USA
| | - Randall A Kramer
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
| | - Voahangy Soarimalala
- Association Vahatra, Antananarivo, Madagascar
- Institut des Sciences et Techniques de l'Environnement, University of Fianarantsoa, Fianarantsoa, Madagascar
| | - Charles L Nunn
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, USA
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
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3
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Elhania N, Moullec G, Kestens Y. Using confirmatory principal component analysis to uncover the interplay between social and spatial factors among older adults: An exploratory study. Health Place 2024; 90:103173. [PMID: 39276755 DOI: 10.1016/j.healthplace.2024.103173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/07/2023] [Accepted: 01/04/2024] [Indexed: 09/17/2024]
Abstract
This study examines the complex interplay between social and spatial structures among older adults, emphasizing the interest in considering the social composition of activity spaces and the spatial characteristics of social networks. There is a growing interest in the collection and analysis of both social and daily mobility spatial information to better understand people-place interactions and determinants of health. Yet, few analyses have explored how the social and spatial dimensions of people's lives relate. In this exploratory study, we analyze how social and spatial indicators collected with the VERITAS-Social questionnaire among 98 older adults in Montréal, Canada, relate, using confirmatory principal component analysis. The aim of the article is to provide empirical evidence on the reduction of dimensions of measures related to social networks, activity spaces, and combined socio-spatial structures.
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Affiliation(s)
- Nadra Elhania
- Université de Montréal, École de Santé Publique, Département de Médecine Sociale et Préventive, Montréal, Canada.
| | - Gregory Moullec
- Université de Montréal, École de Santé Publique, Département de Médecine Sociale et Préventive, Montréal, Canada; Centre de Recherche Du CIUSSS Du Nord-de-l'Île-de-Montréal, Montreal, QC, Canada.
| | - Yan Kestens
- Université de Montréal, École de Santé Publique, Département de Médecine Sociale et Préventive, Montréal, Canada; Centre de Recherche en Santé Publique (CReSP), Montreal, QC, Canada.
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4
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Cardenas NC, Sanchez F, Lopes FPN, Machado G. Coupling spatial statistics with social network analysis to estimate distinct risk areas of disease circulation to improve risk-based surveillance. Transbound Emerg Dis 2022; 69:e2757-e2768. [PMID: 35694801 PMCID: PMC9796646 DOI: 10.1111/tbed.14627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/25/2022] [Accepted: 06/10/2022] [Indexed: 01/01/2023]
Abstract
Most animal disease surveillance systems concentrate efforts in blocking transmission pathways and tracing back infected contacts while not considering the risk of transporting animals into areas with elevated disease risk. Here, we use a suite of spatial statistics and social network analysis to characterize animal movement among areas with an estimated distinct risk of disease circulation to ultimately enhance surveillance activities. Our model utilized equine infectious anemia virus (EIAV) outbreaks, between-farm horse movements, and spatial landscape data from 2015 through 2017. We related EIAV occurrence and the movement of horses between farms with climate variables that foster conditions for local disease propagation. We then constructed a spatially explicit model that allows the effect of the climate variables on EIAV occurrence to vary through space (i.e., non-stationary). Our results identified important areas in which in-going movements were more likely to result in EIAV infections and disease propagation. Municipalities were then classified as having high 56 (11.3%), medium 48 (9.66%), and low 393 (79.1%) spatial risk. The majority of the movements were between low-risk areas, altogether representing 68.68% of all animal movements. Meanwhile, 9.48% were within high-risk areas, and 6.20% were within medium-risk areas. Only 5.37% of the animals entering low-risk areas came from high-risk areas. On the other hand, 4.91% of the animals in the high-risk areas came from low- and medium-risk areas. Our results demonstrate that animal movements and spatial risk mapping could be used to make informed decisions before issuing animal movement permits, thus potentially reducing the chances of reintroducing infection into areas of low risk.
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Affiliation(s)
- Nicolas C. Cardenas
- Department of Population Health and PathobiologyCollege of Veterinary MedicineNorth Carolina State UniversityRaleighNorth CarolinaUSA
| | - Felipe Sanchez
- Department of Population Health and PathobiologyCollege of Veterinary MedicineNorth Carolina State UniversityRaleighNorth CarolinaUSA,Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighNorth CarolinaUSA
| | - Francisco P. N. Lopes
- Departamento de Defesa AgropecuáriaSecretaria da AgriculturaPecuária e Desenvolvimento Rural (SEAPDR)Porto AlegreBrazil
| | - Gustavo Machado
- Department of Population Health and PathobiologyCollege of Veterinary MedicineNorth Carolina State UniversityRaleighNorth CarolinaUSA
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5
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Wu L, Peng Q, Lemke M, Hu T, Gong X. Spatial social network research: a bibliometric analysis. COMPUTATIONAL URBAN SCIENCE 2022; 2:21. [PMID: 37096207 PMCID: PMC10115482 DOI: 10.1007/s43762-022-00045-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 05/13/2022] [Indexed: 04/26/2023]
Abstract
A restless and dynamic intellectual landscape has taken hold in the field of spatial social network studies, given the increasingly attention towards fine-scale human dynamics in this urbanizing and mobile world. The measuring parameters of such dramatic growth of the literature include scientific outputs, domain categories, major journals, countries, institutions, and frequently used keywords. The research in the field has been characterized by fast development of relevant scholarly articles and growing collaboration among and across institutions. The Journal of Economic Geography, Annals of the Association of American Geographers, and Urban Studies ranked first, second, and third, respectively, according to average citations. The United States, United Kingdom, and China were the countries that yielded the most published studies in the field. The number of international collaborative studies published in non-native English-speaking countries (such as France, Italy, and the Netherlands) were higher than native English-speaking countries. Wuhan University, the University of Oxford, and Harvard University were the universities that published the most in the field. "Twitter", "big data", "networks", "spatial analysis", and "social capital" have been the major keywords over the past 20 years. At the same time, the keywords such as "social media", "Twitter", "big data", "geography", "China", "human mobility", "machine learning", "GIS", "location-based social networks", "clustering", "data mining", and "location-based services" have attracted increasing attention in that same time frame, indicating the future research trends.
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Affiliation(s)
- Ling Wu
- Texas Research Data Center, Texas A&M University, College Station, USA
| | - Qiong Peng
- Department of Computer Science, Northeastern University, Boston, USA
| | - Michael Lemke
- Department of Social Sciences, University of Houston-Downtown, Houston, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, USA
| | - Xi Gong
- Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, USA
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6
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Kauffman K, Werner CS, Titcomb G, Pender M, Rabezara JY, Herrera JP, Shapiro JT, Solis A, Soarimalala V, Tortosa P, Kramer R, Moody J, Mucha PJ, Nunn C. Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar. J R Soc Interface 2022; 19:20210690. [PMID: 35016555 PMCID: PMC8753172 DOI: 10.1098/rsif.2021.0690] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/16/2021] [Indexed: 11/12/2022] Open
Abstract
Social and spatial network analysis is an important approach for investigating infectious disease transmission, especially for pathogens transmitted directly between individuals or via environmental reservoirs. Given the diversity of ways to construct networks, however, it remains unclear how well networks constructed from different data types effectively capture transmission potential. We used empirical networks from a population in rural Madagascar to compare social network survey and spatial data-based networks of the same individuals. Close contact and environmental pathogen transmission pathways were modelled with the spatial data. We found that naming social partners during the surveys predicted higher close-contact rates and the proportion of environmental overlap on the spatial data-based networks. The spatial networks captured many strong and weak connections that were missed using social network surveys alone. Across networks, we found weak correlations among centrality measures (a proxy for superspreading potential). We conclude that social network surveys provide important scaffolding for understanding disease transmission pathways but miss contact-specific heterogeneities revealed by spatial data. Our analyses also highlight that the superspreading potential of individuals may vary across transmission modes. We provide detailed methods to construct networks for close-contact transmission pathogens when not all individuals simultaneously wear GPS trackers.
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Affiliation(s)
- Kayla Kauffman
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | - Courtney S. Werner
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Georgia Titcomb
- Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | | | - Jean Yves Rabezara
- Science de la Nature et Valorisation des Ressources Naturelles, Centre Universitaire Régional de la SAVA, Antalaha, Madagascar
| | | | - Julie Teresa Shapiro
- Department of Life Sciences, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Alma Solis
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Duke Global Health Institute, Durham, NC 27156, USA
| | | | - Pablo Tortosa
- UMR Processus Infectieux en Milieu Insulaire Tropical (PIMIT), Université de La Réunion, Ile de La Réunion, France
| | - Randall Kramer
- Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
| | - James Moody
- Department of Sociology, Duke University, Durham, NC 27708, USA
| | - Peter J. Mucha
- Department of Mathematics, Dartmouth College, Hanover, NH 03755, USA
| | - Charles Nunn
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
- Duke Global Health Institute, Durham, NC 27156, USA
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7
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Famulare M, Wong W, Haque R, Platts-Mills JA, Saha P, Aziz AB, Ahmed T, Islam MO, Uddin MJ, Bandyopadhyay AS, Yunus M, Zaman K, Taniuchi M. Multiscale model for forecasting Sabin 2 vaccine virus household and community transmission. PLoS Comput Biol 2021; 17:e1009690. [PMID: 34932560 PMCID: PMC8726461 DOI: 10.1371/journal.pcbi.1009690] [Citation(s) in RCA: 4] [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: 12/11/2020] [Revised: 01/04/2022] [Accepted: 11/29/2021] [Indexed: 11/19/2022] Open
Abstract
Since the global withdrawal of Sabin 2 oral poliovirus vaccine (OPV) from routine immunization, the Global Polio Eradication Initiative (GPEI) has reported multiple circulating vaccine-derived poliovirus type 2 (cVDPV2) outbreaks. Here, we generated an agent-based, mechanistic model designed to assess OPV-related vaccine virus transmission risk in populations with heterogeneous immunity, demography, and social mixing patterns. To showcase the utility of our model, we present a simulation of mOPV2-related Sabin 2 transmission in rural Matlab, Bangladesh based on stool samples collected from infants and their household contacts during an mOPV2 clinical trial. Sabin 2 transmission following the mOPV2 clinical trial was replicated by specifying multiple, heterogeneous contact rates based on household and community membership. Once calibrated, the model generated Matlab-specific insights regarding poliovirus transmission following an accidental point importation or mass vaccination event. We also show that assuming homogeneous contact rates (mass action), as is common of poliovirus forecast models, does not accurately represent the clinical trial and risks overestimating forecasted poliovirus outbreak probability. Our study identifies household and community structure as an important source of transmission heterogeneity when assessing OPV-related transmission risk and provides a calibratable framework for expanding these analyses to other populations. Trial Registration: ClinicalTrials.gov This trial is registered with clinicaltrials.gov, NCT02477046.
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Affiliation(s)
- Michael Famulare
- Institute for Disease Modeling, Global Good, Intellectual Ventures, Bellevue, Washington, United States of America
| | - Wesley Wong
- Institute for Disease Modeling, Global Good, Intellectual Ventures, Bellevue, Washington, United States of America
| | - Rashidul Haque
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - James A. Platts-Mills
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, United States of America
| | - Parimalendu Saha
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Asma B. Aziz
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Tahmina Ahmed
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Md Ohedul Islam
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Md Jashim Uddin
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, United States of America
| | | | - Mohammed Yunus
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Khalequ Zaman
- International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
| | - Mami Taniuchi
- Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Engineering Systems and Environment, University of Virginia, Charlottesville, Virginia, United States of America
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8
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Pepin KM, Golnar A, Podgórski T. Social structure defines spatial transmission of African swine fever in wild boar. J R Soc Interface 2021; 18:20200761. [PMID: 33468025 DOI: 10.1098/rsif.2020.0761] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The spatial spread of infectious disease is determined by spatial and social processes such as animal space use and family group structure. Yet, the impacts of social processes on spatial spread remain poorly understood and estimates of spatial transmission kernels (STKs) often exclude social structure. Understanding the impacts of social structure on STKs is important for obtaining robust inferences for policy decisions and optimizing response plans. We fit spatially explicit transmission models with different assumptions about contact structure to African swine fever virus surveillance data from eastern Poland from 2014 to 2015 and evaluated how social structure affected inference of STKs and spatial spread. The model with social structure provided better inference of spatial spread, predicted that approximately 80% of transmission events occurred within family groups, and that transmission was weakly female-biased (other models predicted weakly male-biased transmission). In all models, most transmission events were within 1.5 km, with some rare events at longer distances. Effective reproductive numbers were between 1.1 and 2.5 (maximum values between 4 and 8). Social structure can modify spatial transmission dynamics. Accounting for this additional contact heterogeneity in spatial transmission models could provide more robust inferences of STKs for policy decisions, identify best control targets and improve transparency in model uncertainty.
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Affiliation(s)
- Kim M Pepin
- National Wildlife Research Center, USDA, APHIS, Wildlife Services, 4101 Laporte Avenue, Fort Collins, CO 80526, USA
| | - Andrew Golnar
- National Wildlife Research Center, USDA, APHIS, Wildlife Services, 4101 Laporte Avenue, Fort Collins, CO 80526, USA
| | - Tomasz Podgórski
- Mammal Research Institute, Polish Academy of Sciences, Stoczek 1, 17-230 Białowieża, Poland.,Department of Game Management and Wildlife Biology, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Kamýcká 129, 165 00 Praha 6, Czech Republic
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9
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Xu X, Hu J, Lyu X, Huang H, Cheng X. Exploring the Interdisciplinary Nature of Precision Medicine:Network Analysis and Visualization. JMIR Med Inform 2021; 9:e23562. [PMID: 33427681 PMCID: PMC7834937 DOI: 10.2196/23562] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/02/2020] [Accepted: 12/09/2020] [Indexed: 12/26/2022] Open
Abstract
Background Interdisciplinary research is an important feature of precision medicine. However, the accurate cross-disciplinary status of precision medicine is still unclear. Objective The aim of this study is to present the nature of interdisciplinary collaboration in precision medicine based on co-occurrences and social network analysis. Methods A total of 7544 studies about precision medicine, published between 2010 and 2019, were collected from the Web of Science database. We analyzed interdisciplinarity with descriptive statistics, co-occurrence analysis, and social network analysis. An evolutionary graph and strategic diagram were created to clarify the development of streams and trends in disciplinary communities. Results The results indicate that 105 disciplines are involved in precision medicine research and cover a wide range. However, the disciplinary distribution is unbalanced. Current cross-disciplinary collaboration in precision medicine mainly focuses on clinical application and technology-associated disciplines. The characteristics of the disciplinary collaboration network are as follows: (1) disciplinary cooperation in precision medicine is not mature or centralized; (2) the leading disciplines are absent; (3) the pattern of disciplinary cooperation is mostly indirect rather than direct. There are 7 interdisciplinary communities in the precision medicine collaboration network; however, their positions in the network differ. Community 4, with disciplines such as genetics and heredity in the core position, is the most central and cooperative discipline in the interdisciplinary network. This indicates that Community 4 represents a relatively mature direction in interdisciplinary cooperation in precision medicine. Finally, according to the evolution graph, we clearly present the development streams of disciplinary collaborations in precision medicine. We describe the scale and the time frame for development trends and distributions in detail. Importantly, we use evolution graphs to accurately estimate the developmental trend of precision medicine, such as biological big data processing, molecular imaging, and widespread clinical applications. Conclusions This study can help researchers, clinicians, and policymakers comprehensively understand the overall network of interdisciplinary cooperation in precision medicine. More importantly, we quantitatively and precisely present the history of interdisciplinary cooperation and accurately predict the developing trends of interdisciplinary cooperation in precision medicine.
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Affiliation(s)
- Xin Xu
- General Medicine Ward, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jiming Hu
- School of Information Management, Wuhan University, Wuhan, China
| | - Xiaoguang Lyu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - He Huang
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xingyu Cheng
- Department of Radiology, Ezhou Central Hospital, Ezhou, China
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10
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Bannister-Tyrrell M, Krit M, Sluydts V, Tho S, Sokny M, Mean V, Kim S, Menard D, Grietens KP, Abrams S, Hens N, Coosemans M, Bassat Q, van Hensbroek MB, Durnez L, Van Bortel W. Households or Hotspots? Defining Intervention Targets for Malaria Elimination in Ratanakiri Province, Eastern Cambodia. J Infect Dis 2020; 220:1034-1043. [PMID: 31028393 PMCID: PMC6688056 DOI: 10.1093/infdis/jiz211] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 04/25/2019] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Malaria "hotspots" have been proposed as potential intervention units for targeted malaria elimination. Little is known about hotspot formation and stability in settings outside sub-Saharan Africa. METHODS Clustering of Plasmodium infections at the household and hotspot level was assessed over 2 years in 3 villages in eastern Cambodia. Social and spatial autocorrelation statistics were calculated to assess clustering of malaria risk, and logistic regression was used to assess the effect of living in a malaria hotspot compared to living in a malaria-positive household in the first year of the study on risk of malaria infection in the second year. RESULTS The crude prevalence of Plasmodium infection was 8.4% in 2016 and 3.6% in 2017. Living in a hotspot in 2016 did not predict Plasmodium risk at the individual or household level in 2017 overall, but living in a Plasmodium-positive household in 2016 strongly predicted living in a Plasmodium-positive household in 2017 (Risk Ratio, 5.00 [95% confidence interval, 2.09-11.96], P < .0001). There was no consistent evidence that malaria risk clustered in groups of socially connected individuals from different households. CONCLUSIONS Malaria risk clustered more clearly in households than in hotspots over 2 years. Household-based strategies should be prioritized in malaria elimination programs in this region.
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Affiliation(s)
| | | | - Vincent Sluydts
- Institute of Tropical Medicine, Antwerp.,University of Antwerp, Belgium
| | - Sochantha Tho
- National Center for Parasitology, Entomology and Malaria Control, Phnom Penh
| | - Mao Sokny
- National Center for Parasitology, Entomology and Malaria Control, Phnom Penh
| | - Vanna Mean
- Ratanakiri Provincial Health Department, Banlung
| | | | | | | | - Steven Abrams
- University of Antwerp, Belgium.,University of Hasselt, Belgium
| | - Niel Hens
- University of Antwerp, Belgium.,University of Hasselt, Belgium
| | | | - Quique Bassat
- ISGlobal, Hospital Clínic-Universitat de Barcelona, Spain.,Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique.,Catalan Institution for Research and Advanced Studies, Barcelona, Spain
| | | | - Lies Durnez
- Institute of Tropical Medicine, Antwerp.,University of Antwerp, Belgium
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11
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Krisztin T, Piribauer P, Wögerer M. The spatial econometrics of the coronavirus pandemic. LETTERS IN SPATIAL AND RESOURCE SCIENCES 2020; 13:209-218. [PMID: 33269031 PMCID: PMC7395580 DOI: 10.1007/s12076-020-00254-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 07/09/2020] [Indexed: 05/19/2023]
Abstract
In this paper we use spatial econometric specifications to model daily infection rates of COVID-19 across countries. Using recent advances in Bayesian spatial econometric techniques, we particularly focus on the time-dependent importance of alternative spatial linkage structures such as the number of flight connections, relationships in international trade, and common borders. The flexible model setup allows to study the intensity and type of spatial spillover structures over time. Our results show notable spatial spillover mechanisms in the early stages of the virus with international flight linkages as the main transmission channel. In later stages, our model shows a sharp drop in the intensity spatial spillovers due to national travel bans, indicating that travel restrictions led to a reduction of cross-country spillovers.
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Affiliation(s)
- Tamás Krisztin
- International Institute for Applied Systems Analysis (IIASA), Schloßplatz 1, 2361 Laxenburg, Austria
| | | | - Michael Wögerer
- International Institute for Applied Systems Analysis (IIASA), Schloßplatz 1, 2361 Laxenburg, Austria
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12
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Abstract
Our work is motivated by a desire to incorporate the vast wealth of social network data into the framework of spatial models. We introduce a method for modeling the spatial correlations that exist over a social network. In particular, we model attributes measured for each member of the network as a continuous process over the social space created by their connections. Our method simultaneously models the unobserved locations of network members in social space and the spatial process that exists over that space based on the observed network connections and nodal attributes. The model is evaluated through simulation studies and applied to the importance ranking for a network of emergency response organizations and the physical activity habits of teenage girls. The introduced methods incorporate network data into the spatial framework, expanding traditional models to include this often relevant source of additional information.
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Affiliation(s)
- Joseph T. Ciminelli
- University of Rochester, 500 Joseph Wilson Boulevard, RC Box 270138, Rochester, NY 14623
| | - Tanzy Love
- University of Rochester, 265 Crittenden Boulevard, Box 630, Rochester, NY 14642
| | - Tong Tong Wu
- University of Rochester, 265 Crittenden Boulevard, Box 630, Rochester, NY 14642
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13
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Liu J, Jiang H, Zhang H, Guo C, Wang L, Yang J, Nie S. Use of social network analysis and global sensitivity and uncertainty analyses to better understand an influenza outbreak. Oncotarget 2018; 8:43417-43426. [PMID: 28177887 PMCID: PMC5522157 DOI: 10.18632/oncotarget.15076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 01/11/2017] [Indexed: 11/25/2022] Open
Abstract
In the summer of 2014, an influenza A(H3N2) outbreak occurred in Yichang city, Hubei province, China. A retrospective study was conducted to collect and interpret hospital and epidemiological data on it using social network analysis and global sensitivity and uncertainty analyses. Results for degree (χ2=17.6619, P<0.0001) and betweenness(χ2=21.4186, P<0.0001) centrality suggested that the selection of sampling objects were different between traditional epidemiological methods and newer statistical approaches. Clique and network diagrams demonstrated that the outbreak actually consisted of two independent transmission networks. Sensitivity analysis showed that the contact coefficient (k) was the most important factor in the dynamic model. Using uncertainty analysis, we were able to better understand the properties and variations over space and time on the outbreak. We concluded that use of newer approaches were significantly more efficient for managing and controlling infectious diseases outbreaks, as well as saving time and public health resources, and could be widely applied on similar local outbreaks.
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Affiliation(s)
- Jianhua Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.,Department of Infectious Diseases, Center for Disease Control and Prevention, Yichang City, Hubei, China
| | - Hongbo Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Hao Zhang
- Department of Infectious Diseases, Center for Disease Control and Prevention, Yichang City, Hubei, China
| | - Chun Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Lei Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.,Department of Infectious Diseases, Center for Disease Control and Prevention, Yichang City, Hubei, China
| | - Jing Yang
- Department of Infectious Diseases, Center for Disease Control and Prevention, Yichang City, Hubei, China
| | - Shaofa Nie
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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14
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Investigating the spatial distribution of growth anomalies affecting Montipora capitata corals in a 3-dimensional framework. J Invertebr Pathol 2016; 140:51-57. [PMID: 27555383 DOI: 10.1016/j.jip.2016.08.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Revised: 08/10/2016] [Accepted: 08/16/2016] [Indexed: 11/22/2022]
Abstract
Diseases have caused significant reductions in coral populations throughout the global ocean. Despite a substantial effort to thoroughly characterize the epizootiology and etiology of coral diseases, little is known about the distribution and spatial clustering of disease lesions on affected coral colonies. This study investigated spatial clustering of the coral disease, growth anomaly (GA), which exhibits high levels of prevalence and severity in Montipora capitata and other corals at Wai'ōpae, southeast Hawai'i Island. Like many other coral diseases, the patterns of disease spread and transmissibility of GA remains unknown. We utilized cutting-edge 3D reconstruction techniques to map the precise spatial distribution of GAs on affected coral colonies. Three statistical measures, Ripley's K, Moran's I, and the Kolmogorov-Smirnov test were used to determine if the GA lesions were distributed in a non-random pattern. Each measure showed the GA lesions exhibited distinct spatial clustering on all ten affected colonies analyzed in this study. Our study is not only the first 3D analysis of intra-colony disease clustering, but also provides a novel approach for investigating and quantifying levels of disease clustering in order to improve our understanding of coral disease epizootiology, transmission, and etiology.
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Evans CR, Onnela JP, Williams DR, Subramanian S. Multiple contexts and adolescent body mass index: Schools, neighborhoods, and social networks. Soc Sci Med 2016; 162:21-31. [DOI: 10.1016/j.socscimed.2016.06.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 04/27/2016] [Accepted: 06/01/2016] [Indexed: 12/20/2022]
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16
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Zhou B. Applying the Clique Percolation Method to analyzing cross-market branch banking network structure: the case of Illinois. SOCIAL NETWORK ANALYSIS AND MINING 2016. [DOI: 10.1007/s13278-016-0318-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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17
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Logan JJ, Jolly AM, Blanford JI. The Sociospatial Network: Risk and the Role of Place in the Transmission of Infectious Diseases. PLoS One 2016; 11:e0146915. [PMID: 26840891 PMCID: PMC4739620 DOI: 10.1371/journal.pone.0146915] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Accepted: 12/23/2015] [Indexed: 11/18/2022] Open
Abstract
Control of sexually transmitted infections and blood-borne pathogens is challenging due to their presence in groups exhibiting complex social interactions. In particular, sharing injection drug use equipment and selling sex (prostitution) puts people at high risk. Previous work examining the involvement of risk behaviours in social networks has suggested that social and geographic distance of persons within a group contributes to these pathogens’ endemicity. In this study, we examine the role of place in the connectedness of street people, selected by respondent driven sampling, in the transmission of blood-borne and sexually transmitted pathogens. A sample of 600 injection drug users, men who have sex with men, street youth and homeless people were recruited in Winnipeg, Canada from January to December, 2009. The residences of participants and those of their social connections were linked to each other and to locations where they engaged in risk activity. Survey responses identified 101 unique sites where respondents participated in injection drug use or sex transactions. Risk sites and respondents’ residences were geocoded, with residence representing the individuals. The sociospatial network and estimations of geographic areas most likely to be frequented were mapped with network graphs and spatially using a Geographic Information System (GIS). The network with the most nodes connected 7.7% of respondents; consideration of the sociospatial network increased this to 49.7%. The mean distance between any two locations in the network was within 3.5 kilometres. Kernel density estimation revealed key activity spaces where the five largest networks overlapped. Here, the combination of spatial and social entities in network analysis defines the overlap of vulnerable populations in risk space, over and above the person to person links. Implications of this work are far reaching, not just for understanding transmission dynamics of sexually transmitted infections by identifying activity “hotspots” and their intersection with each social network, but also for the spread of other diseases (e.g. tuberculosis) and targeting prevention services.
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Affiliation(s)
- James J. Logan
- School of Epidemiology, Public Health and Preventative Medicine, University of Ottawa, Ottawa, Ontario, Canada
- Department of Geography, Dutton Institute of e-Education and GeoVISTA Center, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail:
| | - Ann M. Jolly
- School of Epidemiology, Public Health and Preventative Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Justine I. Blanford
- Department of Geography, Dutton Institute of e-Education and GeoVISTA Center, The Pennsylvania State University, University Park, Pennsylvania, United States of America
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18
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Ribeiro FKC, Pan W, Bertolde A, Vinhas SA, Peres RL, Riley L, Palaci M, Maciel EL. Genotypic and Spatial Analysis of Mycobacterium tuberculosis Transmission in a High-Incidence Urban Setting. Clin Infect Dis 2015; 61:758-66. [PMID: 25948063 DOI: 10.1093/cid/civ365] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2014] [Accepted: 04/28/2015] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Genotyping Mycobacterium tuberculosis isolates allows study of dynamics of tuberculosis transmission, while geoprocessing allows spatial analysis of clinical and epidemiological data. Here, genotyping data and spatial analysis were combined to characterize tuberculosis transmission in Vitória, Brazil, to identify distinct neighborhoods and risk factors associated with recent tuberculosis transmission. METHODS From 2003 to 2007, 503 isolates were genotyped by IS6110 restriction fragment length polymorphism (RFLP) and spoligotyping. The analysis included kernel density estimation, K-function analysis, and a t test distance analysis. Mycobacterium tuberculosis isolates belonging to identical RFLP patterns (clusters) were considered to represent recent tuberculosis infection (cases). RESULTS Of 503 genotyped isolates, 242 (48%) were categorized into 70 distinct clusters belonging to 12 RFLP families. The proportion of recent transmission was 34.2%. Kernel density maps indicated 3 areas of intense concentration of cases. K-function analysis of the largest RFLP clusters and families showed they co-localized in space. The distance analysis confirmed these results and demonstrated that unique strain patterns (controls) randomly distributed in space. A logit model identified young age, positive smear test, and lower Index of Quality of Urban Municipality as risk factors for recent transmission. The predicted probabilities for each neighborhood were mapped and identified neighborhoods with high risk for recent transmission. CONCLUSIONS Spatial and genotypic clustering of M. tuberculosis isolates revealed ongoing active transmission of tuberculosis caused by a small subset of strains in specific neighborhoods of the city. Such information provides an opportunity to target tuberculosis transmission control, such as through rigorous and more focused contact investigation programs.
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Affiliation(s)
| | - William Pan
- Duke Global Health Institute and Nicholas School of Environment, Duke University, Durham, North Carolina
| | - Adelmo Bertolde
- Department of Statistics, Federal University of Espírito Santo, Vitória, Brazil
| | - Solange Alves Vinhas
- Graduate Program in Infectious Diseases, Federal University of Espírito Santo, Vitória, Brazil
| | - Renata Lyrio Peres
- Graduate Program in Infectious Diseases, Federal University of Espírito Santo, Vitória, Brazil
| | - Lee Riley
- Division of Infectious Disease and Vaccinology, School of Public Health, University of California, Berkeley
| | - Moisés Palaci
- Graduate Program in Infectious Diseases, Federal University of Espírito Santo, Vitória, Brazil
| | - Ethel Leonor Maciel
- Graduate Program in Public Health, Federal University of Espírito Santo, Vitória, Brazil
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Li W, Chen T, Wentz EA, Fan C. NMMI: A Mass Compactness Measure for Spatial Pattern Analysis of Areal Features. ACTA ACUST UNITED AC 2014. [DOI: 10.1080/00045608.2014.941732] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Perkins JM, Subramanian SV, Christakis NA. Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries. Soc Sci Med 2014; 125:60-78. [PMID: 25442969 DOI: 10.1016/j.socscimed.2014.08.019] [Citation(s) in RCA: 153] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Revised: 08/07/2014] [Accepted: 08/17/2014] [Indexed: 11/16/2022]
Abstract
In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex webs of dynamic social relationships. Harnessing such information may be especially important in contexts where resources are limited and people depend on their direct and indirect connections for support.
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Affiliation(s)
- Jessica M Perkins
- Department of Health Policy, Harvard University, 14 Story St., 4th Floor, Cambridge, MA 02138, USA.
| | - S V Subramanian
- Department of Social and Behavioral Sciences, Harvard School of Public Health, 677 Huntington Ave., Kresge Building 7th Floor, Boston, MA 02115, USA.
| | - Nicholas A Christakis
- Yale Institute for Network Science, 17 Hillhouse Ave., Room 223, New Haven, CT 06520, USA.
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21
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Metcalf SS. Modeling Social Ties and Household Mobility. ANNALS OF THE ASSOCIATION OF AMERICAN GEOGRAPHERS. ASSOCIATION OF AMERICAN GEOGRAPHERS 2014; 104:40-59. [PMID: 25035520 PMCID: PMC4096934 DOI: 10.1080/00045608.2013.846152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2012] [Revised: 05/01/2013] [Accepted: 05/01/2013] [Indexed: 06/03/2023]
Abstract
Underlying the aggregate phenomena of persistent problems such as urban sprawl and spatial socio-economic disparity is the individual choice of where to live. This study develops an agent-based model to simulate social and economic influences on neighborhood choice. With Danville, Illinois as an empirical context, a pattern-oriented approach is employed to examine the role of social ties in shaping intra-urban household mobility. In the model, household agents decide whether and where to relocate within the community based upon factors such as neighborhood attractiveness, affordability, and the density of a household's social network in the prospective block group. Social network and neighborhood choices are encoded with logit utility functions. The relative influence of factors affecting the formation of social ties in the simulated social network, such as geographic proximity, similarity of income, race, and presence of children, are adjusted using parameter variation to create alternative model settings. Simulated migration patterns resulting from different network and neighborhood choice coefficients are compared with observed migration patterns over a two-year period. Based upon 1000 simulation experiments, a regression of homeowner migration error (the difference between simulated and observed migration) relative to the parameter settings revealed components of social network choice such as income, race, and probability of local ties to be significant in matching observed migration patterns. A non-linear effect of simulated social networks on household mobility and thus migration error was exhibited in this study.
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Affiliation(s)
- Sara S Metcalf
- Department of Geography, The State University of New York at Buffalo, Buffalo, New York 14261 USA
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Perez-Heydrich C, Furgurson JM, Giebultowicz S, Winston JJ, Yunus M, Streatfield PK, Emch M. Social and spatial processes associated with childhood diarrheal disease in Matlab, Bangladesh. Health Place 2013; 19:45-52. [PMID: 23178328 PMCID: PMC3537872 DOI: 10.1016/j.healthplace.2012.10.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2012] [Revised: 10/02/2012] [Accepted: 10/08/2012] [Indexed: 11/21/2022]
Abstract
We develop novel methods for conceptualizing geographic space and social networks to evaluate their respective and combined contributions to childhood diarrheal incidence. After defining maternal networks according to direct familial linkages between females, and road networks using satellite imagery of the study area, we use a spatial econometrics model to evaluate the significance of correlation terms relating childhood diarrheal incidence to the incidence observed within respective networks. Disease was significantly clustered within road networks across time, but only inconsistently correlated within maternal networks. These methods could be widely applied to systems in which both social and spatial processes jointly influence health outcomes.
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Affiliation(s)
- Carolina Perez-Heydrich
- Carolina Population Center, University of North Carolina at Chapel Hill, CB# 8120, University Square, 123 West Franklin Street, Chapel Hill, NC 27516-2524, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, 3101 McGavran-Greenberg, CB#7420, Chapel Hill, NC 27599, USA
| | - Jill M. Furgurson
- Carolina Population Center, University of North Carolina at Chapel Hill, CB# 8120, University Square, 123 West Franklin Street, Chapel Hill, NC 27516-2524, USA
- Department of Geography, University of North Carolina at Chapel Hill, Saunders Hall, Campus Box 3220, Chapel Hill, NC 27599-3220, USA
| | - Sophia Giebultowicz
- Carolina Population Center, University of North Carolina at Chapel Hill, CB# 8120, University Square, 123 West Franklin Street, Chapel Hill, NC 27516-2524, USA
- Department of Geography, University of North Carolina at Chapel Hill, Saunders Hall, Campus Box 3220, Chapel Hill, NC 27599-3220, USA
| | - Jennifer J. Winston
- Carolina Population Center, University of North Carolina at Chapel Hill, CB# 8120, University Square, 123 West Franklin Street, Chapel Hill, NC 27516-2524, USA
- Department of Geography, University of North Carolina at Chapel Hill, Saunders Hall, Campus Box 3220, Chapel Hill, NC 27599-3220, USA
| | - Mohammad Yunus
- International Centre for Diarrhoeal Disease Research, GPO Box 128, Dhaka 1000, Bangladesh
| | - Peter Kim Streatfield
- International Centre for Diarrhoeal Disease Research, GPO Box 128, Dhaka 1000, Bangladesh
| | - Michael Emch
- Carolina Population Center, University of North Carolina at Chapel Hill, CB# 8120, University Square, 123 West Franklin Street, Chapel Hill, NC 27516-2524, USA
- Department of Geography, University of North Carolina at Chapel Hill, Saunders Hall, Campus Box 3220, Chapel Hill, NC 27599-3220, USA
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