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Applications of Extreme Value Theory in Public Health

Author

Listed:
  • Maud Thomas
  • Magali Lemaitre
  • Mark L Wilson
  • Cécile Viboud
  • Youri Yordanov
  • Hans Wackernagel
  • Fabrice Carrat

Abstract

Objectives: We present how Extreme Value Theory (EVT) can be used in public health to predict future extreme events. Methods: We applied EVT to weekly rates of Pneumonia and Influenza (P&I) deaths over 1979–2011. We further explored the daily number of emergency department visits in a network of 37 hospitals over 2004–2014. Maxima of grouped consecutive observations were fitted to a generalized extreme value distribution. The distribution was used to estimate the probability of extreme values in specified time periods. Results: An annual P&I death rate of 12 per 100,000 (the highest maximum observed) should be exceeded once over the next 30 years and each year, there should be a 3% risk that the P&I death rate will exceed this value. Over the past 10 years, the observed maximum increase in the daily number of visits from the same weekday between two consecutive weeks was 1133. We estimated at 0.37% the probability of exceeding a daily increase of 1000 on each month. Conclusion: The EVT method can be applied to various topics in epidemiology thus contributing to public health planning for extreme events.

Suggested Citation

  • Maud Thomas & Magali Lemaitre & Mark L Wilson & Cécile Viboud & Youri Yordanov & Hans Wackernagel & Fabrice Carrat, 2016. "Applications of Extreme Value Theory in Public Health," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-7, July.
    • Handle: RePEc:plo:pone00:0159312
    DOI: 10.1371/journal.pone.0159312
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    References listed on IDEAS

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    1. L. de Haan, 1990. "Fighting the arch–enemy with mathematics‘," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 44(2), pages 45-68, June.
    2. Jiangpeng Chen & Xun Lei & Li Zhang & Bin Peng, 2015. "Using Extreme Value Theory Approaches to Forecast the Probability of Outbreak of Highly Pathogenic Influenza in Zhejiang, China," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-10, February.
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    Cited by:

    1. Jue Tao Lim & Yiting Han & Borame Sue Lee Dickens & Lee Ching Ng & Alex R Cook, 2020. "Time varying methods to infer extremes in dengue transmission dynamics," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-19, October.
    2. Maud Thomas & Holger Rootzén, 2022. "Real‐time prediction of severe influenza epidemics using extreme value statistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 376-394, March.
    3. Paolo Riccardo Morganti, 2021. "Extreme Value Theory and Auction Models," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(2), pages 1-15, Abril - J.
    4. Ahmed, Hanan, 2022. "Extreme value statistics using related variables," Other publications TiSEM 246f0f13-701c-4c0d-8e09-e, Tilburg University, School of Economics and Management.
    5. Kiriliouk, Anna, 2020. "Hypothesis testing for tail dependence parameters on the boundary of the parameter space," Econometrics and Statistics, Elsevier, vol. 16(C), pages 121-135.
    6. Yohann Moanahere Chiu & Fateh Chebana & Belkacem Abdous & Diane Bélanger & Pierre Gosselin, 2021. "Cardiovascular Health Peaks and Meteorological Conditions: A Quantile Regression Approach," IJERPH, MDPI, vol. 18(24), pages 1-14, December.
    7. Paolo Riccardo Morganti, 2021. "Extreme Value Theory and Auction Models," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(2), pages 1-15, Abril - J.

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