IDEAS home Printed from https://ideas.repec.org/p/hhs/gunsru/2010_001.html
   My bibliography  Save this paper

Modelling the spatial patterns of influenza incidence in Sweden

Author

Listed:
  • Schiöler, Linus

    (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)

Abstract

Information about the spatial spread of epidemics can be useful for many purposes. The spatial aspect of Swedish influenza data was analyzed with the main aim of finding patterns that could be useful for statistical surveillance of the outbreak, i.e. for detecting an increase in incidence as soon as possible. In Sweden, two types of data are collected during the influenza season: laboratory diagnosed cases (LDI), collected by a number of laboratories, and cases of influenza-like illness (ILI), c... merollected by a number of selected physicians. Quality problems were found for both types of data but were most severe for ILI. No evidence for a geographical pattern was found. Instead, it was found that the influenza outbreak starts at about the same time in the major cities and then occurs in the rest of the country. The data were divided into two groups, a metropolitan group representing the major cities and a locality group representing the rest of the country. The properties of the metropolitan group and the locality group were studied and it was found that the time difference in the onset of the outbreak was about one week. Both parametric and nonparametric regression models were suggested.

Suggested Citation

  • Schiöler, Linus, 2010. "Modelling the spatial patterns of influenza incidence in Sweden," Research Reports 2010:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    • Handle: RePEc:hhs:gunsru:2010_001
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/2077/23389
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andersson, Eva & Bock, David & Frisén, Marianne, 2007. "Modeling influenza incidence for the purpose of on-line monitoring," Research Reports 2007:5, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    2. Marianne Frisen & Eva Andersson & Linus Schioler, 2010. "Evaluation of multivariate surveillance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2089-2100.
    3. AfDB AfDB, . "AfDB Group Annual Report 2005," Annual Report, African Development Bank, number 61 edited by Koua Louis Kouakou.
    4. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
    5. Frisén, Marianne & Andersson, Eva & Schiöler, Linus, 2007. "Robust outbreak surveillance of epidemics in Sweden," Research Reports 2007:12, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    6. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    Full references (including those not matched with items on IDEAS)

    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. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    2. Marianne Frisén, 2014. "Spatial outbreak detection based on inference principles for multivariate surveillance," IISE Transactions, Taylor & Francis Journals, vol. 46(8), pages 759-769, August.
    3. Schiöler, Linus, 2009. "Explorative analysis of spatial patterns of influenza incidences in Sweden 1999—2008," Research Reports 2008:5, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    4. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    5. Frisén, Marianne, 2008. "Introduction to financial surveillance," Research Reports 2008:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    6. Schiöler, Linus & Frisén, Marianne, 2008. "On statistical surveillance of the performance of fund managers," Research Reports 2008:4, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    7. Frisén, Marianne & Andersson, Eva, 2008. "Semiparametric surveillance of outbreaks," Research Reports 2007:11, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    8. Andersson, Eva, 2008. "Hotelling´s T2 Method in Multivariate On-line Surveillance. On the Delay of an Alarm," Research Reports 2008:3, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    9. David H Chae & Sean Clouston & Mark L Hatzenbuehler & Michael R Kramer & Hannah L F Cooper & Sacoby M Wilson & Seth I Stephens-Davidowitz & Robert S Gold & Bruce G Link, 2015. "Association between an Internet-Based Measure of Area Racism and Black Mortality," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    10. Xiaoli Wang & Shuangsheng Wu & C Raina MacIntyre & Hongbin Zhang & Weixian Shi & Xiaomin Peng & Wei Duan & Peng Yang & Yi Zhang & Quanyi Wang, 2015. "Using an Adjusted Serfling Regression Model to Improve the Early Warning at the Arrival of Peak Timing of Influenza in Beijing," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    11. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    12. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    13. Diamandis, Panayiotis F., 2008. "Financial liberalization and changes in the dynamic behaviour of emerging market volatility: Evidence from four Latin American equity markets," Research in International Business and Finance, Elsevier, vol. 22(3), pages 362-377, September.
    14. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    15. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.
    16. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    17. Adewumi, Matthew Olaniyi & Animashaun, Jubril Olayinka, 2013. "Households’ Dietary Diversity, Farm Income and Technical Efficiency Correlates: Empirical Evidence from Small-scale Farming Households in Nigeria," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 5(4), pages 1-9, December.
    18. Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    19. Dong Ding & Axel Gandy & Georg Hahn, 2020. "A simple method for implementing Monte Carlo tests," Computational Statistics, Springer, vol. 35(3), pages 1373-1392, September.
    20. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.

    More about this item

    Keywords

    Influenza; Sweden; onset of outbreak; statistical models; spatial; monitoring;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:hhs:gunsru:2010_001. 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: Linus Schiöler (email available below). General contact details of provider: http://www.statistics.gu.se/ .

    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.