IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v13y2016i3p291-d65235.html
   My bibliography  Save this article

Spatio-Temporal Distribution Characteristics and Trajectory Similarity Analysis of Tuberculosis in Beijing, China

Author

Listed:
  • Lan Li

    (School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China
    Key Laboratory of Digital Mapping and Land information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China)

  • Yuliang Xi

    (School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China
    Key Laboratory of Digital Mapping and Land information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China)

  • Fu Ren

    (School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China
    Key Laboratory of GIS, Ministry of Education, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China
    Key Laboratory of Digital Mapping and Land information Application Engineering, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China
    Collaborative Innovation Center of Geospatial Technology, Wuhan University, 129 Luoyu Rd., Wuhan 430079, China)

Abstract

Tuberculosis (TB) is an infectious disease with one of the highest reported incidences in China. The detection of the spatio-temporal distribution characteristics of TB is indicative of its prevention and control conditions. Trajectory similarity analysis detects variations and loopholes in prevention and provides urban public health officials and related decision makers more information for the allocation of public health resources and the formulation of prioritized health-related policies. This study analysed the spatio-temporal distribution characteristics of TB from 2009 to 2014 by utilizing spatial statistics, spatial autocorrelation analysis, and space-time scan statistics. Spatial statistics measured the TB incidence rate (TB patients per 100,000 residents) at the district level to determine its spatio-temporal distribution and to identify characteristics of change. Spatial autocorrelation analysis was used to detect global and local spatial autocorrelations across the study area. Purely spatial, purely temporal and space-time scan statistics were used to identify purely spatial, purely temporal and spatio-temporal clusters of TB at the district level. The other objective of this study was to compare the trajectory similarities between the incidence rates of TB and new smear-positive (NSP) TB patients in the resident population (NSPRP)/new smear-positive TB patients in the TB patient population (NSPTBP)/retreated smear-positive (RSP) TB patients in the resident population (RSPRP)/retreated smear-positive TB patients in the TB patient population (RSPTBP) to detect variations and loopholes in TB prevention and control among the districts in Beijing. The incidence rates in Beijing exhibited a gradual decrease from 2009 to 2014. Although global spatial autocorrelation was not detected overall across all of the districts of Beijing, individual districts did show evidence of local spatial autocorrelation: Chaoyang and Daxing were Low-Low districts over the six-year period. The purely spatial scan statistics analysis showed significant spatial clusters of high and low incidence rates; the purely temporal scan statistics showed the temporal cluster with a three-year period from 2009 to 2011 characterized by a high incidence rate; and the space-time scan statistics analysis showed significant spatio-temporal clusters. The distribution of the mean centres (MCs) showed that the general distributions of the NSPRP MCs and NSPTBP MCs were to the east of the incidence rate MCs. Conversely, the general distributions of the RSPRP MCs and the RSPTBP MCs were to the south of the incidence rate MCs. Based on the combined analysis of MC distribution characteristics and trajectory similarities, the NSP trajectory was most similar to the incidence rate trajectory. Thus, more attention should be focused on the discovery of NSP patients in the western part of Beijing, whereas the northern part of Beijing needs intensive treatment for RSP patients.

Suggested Citation

  • Lan Li & Yuliang Xi & Fu Ren, 2016. "Spatio-Temporal Distribution Characteristics and Trajectory Similarity Analysis of Tuberculosis in Beijing, China," IJERPH, MDPI, vol. 13(3), pages 1-17, March.
  • Handle: RePEc:gam:jijerp:v:13:y:2016:i:3:p:291-:d:65235
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/13/3/291/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/13/3/291/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ellen Brooks-Pollock & Gareth O. Roberts & Matt J. Keeling, 2014. "A dynamic model of bovine tuberculosis spread and control in Great Britain," Nature, Nature, vol. 511(7508), pages 228-231, July.
    2. Wenyi Sun & Jianhua Gong & Jieping Zhou & Yanlin Zhao & Junxiang Tan & Abdoul Nasser Ibrahim & Yang Zhou, 2015. "A Spatial, Social and Environmental Study of Tuberculosis in China Using Statistical and GIS Technology," IJERPH, MDPI, vol. 12(2), pages 1-24, January.
    3. In-Chan Ng & Tzai-Hung Wen & Jann-Yuan Wang & Chi-Tai Fang, 2012. "Spatial Dependency of Tuberculosis Incidence in Taiwan," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Peter Congdon, 2016. "Spatiotemporal Frameworks for Infectious Disease Diffusion and Epidemiology," IJERPH, MDPI, vol. 13(12), pages 1-4, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ellen Brooks-Pollock & Leon Danon & Hester Korthals Altes & Jennifer A Davidson & Andrew M T Pollock & Dick van Soolingen & Colin Campbell & Maeve K Lalor, 2020. "A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-14, March.
    2. Horan, Richard D. & Fenichel, Eli P. & Finnoff, David & Wolf, Christopher A., 2015. "Managing dynamic epidemiological risks through trade," Journal of Economic Dynamics and Control, Elsevier, vol. 53(C), pages 192-207.
    3. Kai Cao & Kun Yang & Chao Wang & Jin Guo & Lixin Tao & Qingrong Liu & Mahara Gehendra & Yingjie Zhang & Xiuhua Guo, 2016. "Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory," IJERPH, MDPI, vol. 13(5), pages 1-8, May.
    4. C. E. Dangerfield & A. E. Whalley & N. Hanley & C. A. Gilligan, 2018. "What a Difference a Stochastic Process Makes: Epidemiological-Based Real Options Models of Optimal Treatment of Disease," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 70(3), pages 691-711, July.
    5. Sifat A Moon & Lee W Cohnstaedt & D Scott McVey & Caterina M Scoglio, 2019. "A spatio-temporal individual-based network framework for West Nile virus in the USA: Spreading pattern of West Nile virus," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-24, March.
    6. Peter Brommesson & Uno Wennergren & Tom Lindström, 2016. "Spatiotemporal Variation in Distance Dependent Animal Movement Contacts: One Size Doesn’t Fit All," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-20, October.
    7. Lambert, Sébastien & Gilot-Fromont, Emmanuelle & Toïgo, Carole & Marchand, Pascal & Petit, Elodie & Garin-Bastuji, Bruno & Gauthier, Dominique & Gaillard, Jean-Michel & Rossi, Sophie & Thébault, Anne, 2020. "An individual-based model to assess the spatial and individual heterogeneity of Brucella melitensis transmission in Alpine ibex," Ecological Modelling, Elsevier, vol. 425(C).
    8. Gehendra Mahara & Chao Wang & Kun Yang & Sipeng Chen & Jin Guo & Qi Gao & Wei Wang & Quanyi Wang & Xiuhua Guo, 2016. "The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models," IJERPH, MDPI, vol. 13(11), pages 1-15, November.
    9. Yanguang Chen, 2020. "New framework of Getis-Ord’s indexes associating spatial autocorrelation with interaction," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-25, July.
    10. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2018. "Control fast or control smart: When should invading pathogens be controlled?," PLOS Computational Biology, Public Library of Science, vol. 14(2), pages 1-21, February.
    11. Montagnon, Pierre, 2020. "Stability of piecewise deterministic Markovian metapopulation processes on networks," Stochastic Processes and their Applications, Elsevier, vol. 130(3), pages 1515-1544.
    12. Zongyuan Xia & Bo Tang & Long Qin & Huiguo Zhang & Xijian Hu, 2023. "Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China," Mathematics, MDPI, vol. 11(19), pages 1-15, October.
    13. Ying Mao & Rongxin He & Bin Zhu & Jinlin Liu & Ning Zhang, 2020. "Notifiable Respiratory Infectious Diseases in China: A Spatial–Temporal Epidemiology Analysis," IJERPH, MDPI, vol. 17(7), pages 1-15, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:13:y:2016:i:3:p:291-:d:65235. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.