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Time series modelling of childhood diseases: a dynamical systems approach

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  • B. F. Finkenstädt
  • B. T. Grenfell

Abstract

A key issue in the dynamical modelling of epidemics is the synthesis of complex mathematical models and data by means of time series analysis. We report such an approach, focusing on the particularly well‐documented case of measles. We propose the use of a discrete time epidemic model comprising the infected and susceptible class as state variables. The model uses a discrete time version of the susceptible–exposed–infected–recovered type epidemic models, which can be fitted to observed disease incidence time series. We describe a method for reconstructing the dynamics of the susceptible class, which is an unobserved state variable of the dynamical system. The model provides a remarkable fit to the data on case reports of measles in England and Wales from 1944 to 1964. Morever, its systematic part explains the well‐documented predominant biennial cyclic pattern. We study the dynamic behaviour of the time series model and show that episodes of annual cyclicity, which have not previously been explained quantitatively, arise as a response to a quicker replenishment of the susceptible class during the baby boom, around 1947.

Suggested Citation

  • B. F. Finkenstädt & B. T. Grenfell, 2000. "Time series modelling of childhood diseases: a dynamical systems approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 187-205.
  • Handle: RePEc:bla:jorssc:v:49:y:2000:i:2:p:187-205
    DOI: 10.1111/1467-9876.00187
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    Cited by:

    1. Chuard, Caroline & Schwandt, Hannes & Becker, Alex & Haraguchi, Masahiko, 2022. "Economic vs. Epidemiological Approaches to Measuring the Human Capital Impacts of Infectious Disease Elimination," IZA Discussion Papers 15420, Institute of Labor Economics (IZA).
    2. Kimberly M. Thompson, 2016. "Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis," Risk Analysis, John Wiley & Sons, vol. 36(7), pages 1383-1403, July.
    3. Rachel E. Baker & Ayesha S. Mahmud & C. Jessica E. Metcalf, 2018. "Dynamic response of airborne infections to climate change: predictions for varicella," Climatic Change, Springer, vol. 148(4), pages 547-560, June.
    4. Alexander D Becker & Bryan T Grenfell, 2017. "tsiR: An R package for time-series Susceptible-Infected-Recovered models of epidemics," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-10, September.
    5. Joanna N. Lahey & Marianne H. Wanamaker, 2022. "Effects of Restrictive Abortion Legislation on Cohort Mortality Evidence from 19th Century Law Variation," NBER Working Papers 30201, National Bureau of Economic Research, Inc.
    6. H. J. Whitaker & C. P. Farrington, 2004. "Infections with Varying Contact Rates: Application to Varicella," Biometrics, The International Biometric Society, vol. 60(3), pages 615-623, September.
    7. Calsina, Àngel & Cuadrado, Sílvia & Vidiella, Blai & Sardanyés, Josep, 2023. "About ghost transients in spatial continuous media," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    8. Patrick W. Schmidt, 2020. "Inference under Superspreading: Determinants of SARS-CoV-2 Transmission in Germany," Papers 2011.04002, arXiv.org.
    9. Victor Zakharov & Yulia Balykina & Igor Ilin & Andrea Tick, 2022. "Forecasting a New Type of Virus Spread: A Case Study of COVID-19 with Stochastic Parameters," Mathematics, MDPI, vol. 10(20), pages 1-18, October.
    10. Metcalf, C.J.E. & Lessler, J. & Klepac, P. & Morice, A. & Grenfell, B.T. & Bjørnstad, O.N., 2012. "Structured models of infectious disease: Inference with discrete data," Theoretical Population Biology, Elsevier, vol. 82(4), pages 275-282.
    11. Frits Bijleveld & Jacques Commandeur & Phillip Gould & Siem Jan Koopman, 2008. "Model‐based measurement of latent risk in time series with applications," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 265-277, January.
    12. Julliard, Christian & Shi, Ran & Yuan, Kathy, 2023. "The spread of COVID-19 in London: Network effects and optimal lockdowns," Journal of Econometrics, Elsevier, vol. 235(2), pages 2125-2154.
    13. Maria Bekker‐Nielsen Dunbar & Felix Hofmann & Leonhard Held & the SUSPend modelling consortium, 2022. "Assessing the effect of school closures on the spread of COVID‐19 in Zurich," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 131-142, November.
    14. David M Williams & Amy C Dechen Quinn & William F Porter, 2014. "Informing Disease Models with Temporal and Spatial Contact Structure among GPS-Collared Individuals in Wild Populations," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
    15. Wan Yang & Liang Wen & Shen-Long Li & Kai Chen & Wen-Yi Zhang & Jeffrey Shaman, 2017. "Geospatial characteristics of measles transmission in China during 2005−2014," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-21, April.
    16. Mikael Jagan & Michelle S deJonge & Olga Krylova & David J D Earn, 2020. "Fast estimation of time-varying infectious disease transmission rates," PLOS Computational Biology, Public Library of Science, vol. 16(9), pages 1-39, September.
    17. Hao Yu & Xu Sun & Wei Deng Solvang & Xu Zhao, 2020. "Reverse Logistics Network Design for Effective Management of Medical Waste in Epidemic Outbreaks: Insights from the Coronavirus Disease 2019 (COVID-19) Outbreak in Wuhan (China)," IJERPH, MDPI, vol. 17(5), pages 1-25, March.
    18. David A Rasmussen & Oliver Ratmann & Katia Koelle, 2011. "Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-11, August.

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