Visual Analytics of Geo-social Interaction Patterns for Epidemic Control

Overview

Journal Int J Health Geogr

Publisher Biomed Central

Specialties Health Services
Public Health

Date 2016 Aug 12

PMID 27510908

Citations 4

Authors

Wei Luo

Affiliations

Soon will be listed here.

Abstract

Background: Human interaction and population mobility determine the spatio-temporal course of the spread of an airborne disease. This research views such spreads as geo-social interaction problems, because population mobility connects different groups of people over geographical locations via which the viruses transmit. Previous research argued that geo-social interaction patterns identified from population movement data can provide great potential in designing effective pandemic mitigation. However, little work has been done to examine the effectiveness of designing control strategies taking into account geo-social interaction patterns.

Methods: To address this gap, this research proposes a new framework for effective disease control; specifically this framework proposes that disease control strategies should start from identifying geo-social interaction patterns, designing effective control measures accordingly, and evaluating the efficacy of different control measures. This framework is used to structure design of a new visual analytic tool that consists of three components: a reorderable matrix for geo-social mixing patterns, agent-based epidemic models, and combined visualization methods.

Results: With real world human interaction data in a French primary school as a proof of concept, this research compares the efficacy of vaccination strategies between the spatial-social interaction patterns and the whole areas. The simulation results show that locally targeted vaccination has the potential to keep infection to a small number and prevent spread to other regions. At some small probability, the local control strategies will fail; in these cases other control strategies will be needed. This research further explores the impact of varying spatial-social scales on the success of local vaccination strategies. The results show that a proper spatial-social scale can help achieve the best control efficacy with a limited number of vaccines.

Conclusions: The case study shows how GS-EpiViz does support the design and testing of advanced control scenarios in airborne disease (e.g., influenza). The geo-social patterns identified through exploring human interaction data can help target critical individuals, locations, and clusters of locations for disease control purposes. The varying spatial-social scales can help geographically and socially prioritize limited resources (e.g., vaccines).

Citing Articles

Understanding epidemic spread patterns: a visual analysis approach.

Wu J, Niu Z, Liu X Health Syst (Basingstoke). 2024; 13(3):229-245.

PMID: 39175497 PMC: 11338210. DOI: 10.1080/20476965.2024.2308286.

An analytical tool to support public policies and isolation barriers against SARS-CoV-2 based on mobility patterns and socio-economic aspects.

Silva J, de Lima Silva D, Ferreira Junior N, de Almeida Filho A Appl Soft Comput. 2023; 138:110177.

PMID: 36923646 PMC: 9991329. DOI: 10.1016/j.asoc.2023.110177.

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review.

Chishtie J, Marchand J, Turcotte L, Bielska I, Babineau J, Cepoiu-Martin M J Med Internet Res. 2020; 22(12):e17892.

PMID: 33270029 PMC: 7716797. DOI: 10.2196/17892.

A large-scale location-based social network to understanding the impact of human geo-social interaction patterns on vaccination strategies in an urbanized area.

Luo W, Gao P, Cassels S Comput Environ Urban Syst. 2019; 72:78-87.

PMID: 30983651 PMC: 6457472. DOI: 10.1016/j.compenvurbsys.2018.06.008.

References

Anderson R, Fraser C, Ghani A, Donnelly C, Riley S, Ferguson N . Epidemiology, transmission dynamics and control of SARS: the 2002-2003 epidemic. Philos Trans R Soc Lond B Biol Sci. 2004; 359(1447):1091-105. PMC: 1693389. DOI: 10.1098/rstb.2004.1490. View

Rhodes C, Anderson R . Epidemic thresholds and vaccination in a lattice model of disease spread. Theor Popul Biol. 1997; 52(2):101-18. DOI: 10.1006/tpbi.1997.1323. View

Van den Broeck W, Gioannini C, Goncalves B, Quaggiotto M, Colizza V, Vespignani A . The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect Dis. 2011; 11:37. PMC: 3048541. DOI: 10.1186/1471-2334-11-37. View

Cliff A, Haggett P . Time, travel and infection. Br Med Bull. 2004; 69:87-99. DOI: 10.1093/bmb/ldh011. View

Mills C, Robins J, Lipsitch M . Transmissibility of 1918 pandemic influenza. Nature. 2004; 432(7019):904-6. PMC: 7095078. DOI: 10.1038/nature03063. View

Emanuel E, Wertheimer A . Public health. Who should get influenza vaccine when not all can?. Science. 2006; 312(5775):854-5. DOI: 10.1126/science.1125347. View

Ferguson N, Cummings D, Cauchemez S, Fraser C, Riley S, Meeyai A . Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature. 2005; 437(7056):209-14. DOI: 10.1038/nature04017. View

Stehle J, Voirin N, Barrat A, Cattuto C, Isella L, Pinton J . High-resolution measurements of face-to-face contact patterns in a primary school. PLoS One. 2011; 6(8):e23176. PMC: 3156713. DOI: 10.1371/journal.pone.0023176. View

Carrat F, Luong J, Lao H, Salle A, Lajaunie C, Wackernagel H . A 'small-world-like' model for comparing interventions aimed at preventing and controlling influenza pandemics. BMC Med. 2006; 4:26. PMC: 1626479. DOI: 10.1186/1741-7015-4-26. View

10.

Keeling M, Eames K . Networks and epidemic models. J R Soc Interface. 2006; 2(4):295-307. PMC: 1578276. DOI: 10.1098/rsif.2005.0051. View

11.

Salathe M, Kazandjieva M, Lee J, Levis P, Feldman M, Jones J . A high-resolution human contact network for infectious disease transmission. Proc Natl Acad Sci U S A. 2010; 107(51):22020-5. PMC: 3009790. DOI: 10.1073/pnas.1009094108. View

12.

Henry N, Fekete J, McGuffin M . NodeTrix: a hybrid visualization of social networks. IEEE Trans Vis Comput Graph. 2007; 13(6):1302-9. DOI: 10.1109/TVCG.2007.70582. View

13.

Leung G, Hedley A, Ho L, Chau P, Wong I, Thach T . The epidemiology of severe acute respiratory syndrome in the 2003 Hong Kong epidemic: an analysis of all 1755 patients. Ann Intern Med. 2004; 141(9):662-73. DOI: 10.7326/0003-4819-141-9-200411020-00006. View

14.

Gemmetto V, Barrat A, Cattuto C . Mitigation of infectious disease at school: targeted class closure vs school closure. BMC Infect Dis. 2015; 14:695. PMC: 4297433. DOI: 10.1186/s12879-014-0695-9. View

15.

Longini Jr I, Halloran M . Strategy for distribution of influenza vaccine to high-risk groups and children. Am J Epidemiol. 2005; 161(4):303-6. DOI: 10.1093/aje/kwi053. View

16.

Gao P, Bian L . Scale Effects on Spatially Embedded Contact Networks. Comput Environ Urban Syst. 2016; 59:142-151. PMC: 4966681. DOI: 10.1016/j.compenvurbsys.2016.06.002. View

17.

Diekmann O, Heesterbeek J, Metz J . On the definition and the computation of the basic reproduction ratio R0 in models for infectious diseases in heterogeneous populations. J Math Biol. 1990; 28(4):365-82. DOI: 10.1007/BF00178324. View

18.

Fraser C, Donnelly C, Cauchemez S, Hanage W, Van Kerkhove M, Hollingsworth T . Pandemic potential of a strain of influenza A (H1N1): early findings. Science. 2009; 324(5934):1557-61. PMC: 3735127. DOI: 10.1126/science.1176062. View

19.

Eubank S, Guclu H, Anil Kumar V, Marathe M, Srinivasan A, Toroczkai Z . Modelling disease outbreaks in realistic urban social networks. Nature. 2004; 429(6988):180-4. DOI: 10.1038/nature02541. View

20.

Koopman J . Modeling infection transmission. Annu Rev Public Health. 2004; 25:303-26. DOI: 10.1146/annurev.publhealth.25.102802.124353. View