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Capturing the time-varying drivers of an epidemic using stochastic dynamical systems

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  • Dureau, Joseph
  • Kalogeropoulos, Konstantinos
  • Baguelin, Marc

Abstract

Epidemics are often modeled using non-linear dynamical systems observed through partial and noisy data. In this paper, we consider stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects, etc.). These models assign diffusion processes to the time-varying parameters, and our inferential procedure is based on a suitably adjusted adaptive particle Markov chain Monte Carlo algorithm. The performance of the proposed computational methods is validated on simulated data and the adopted model is applied to the 2009 H1N1 pandemic in England. In addition to estimating the effective contact rate trajectories, the methodology is applied in real time to provide evidence in related public health decisions. Diffusion-driven susceptible exposed infected retired-type models with age structure are also introduced.

Suggested Citation

  • Dureau, Joseph & Kalogeropoulos, Konstantinos & Baguelin, Marc, 2013. "Capturing the time-varying drivers of an epidemic using stochastic dynamical systems," LSE Research Online Documents on Economics 41749, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:41749
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    References listed on IDEAS

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    1. Golightly, A. & Wilkinson, D.J., 2008. "Bayesian inference for nonlinear multivariate diffusion models observed with error," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1674-1693, January.
    2. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    3. Kalogeropoulos, Konstantinos, 2007. "Likelihood-based inference for a class of multivariate diffusions with unobserved paths," LSE Research Online Documents on Economics 31423, London School of Economics and Political Science, LSE Library.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    5. Yvo Pokern & Andrew M. Stuart & Petter Wiberg, 2009. "Parameter estimation for partially observed hypoelliptic diffusions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 49-73, January.
    6. Neil Ferguson, 2007. "Capturing human behaviour," Nature, Nature, vol. 446(7137), pages 733-733, April.
    7. 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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    Citations

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    Cited by:

    1. Iguchi, Yuga & Beskos, Alexandros & Graham, Matthew M., 2024. "Parameter inference for degenerate diffusion processes," Stochastic Processes and their Applications, Elsevier, vol. 174(C).
    2. Guenette,Justin Damien & Yamazaki,Takefumi, 2021. "Projecting the Economic Consequences of the COVID-19 Pandemic," Policy Research Working Paper Series 9589, The World Bank.
    3. Harrison Hong & Neng Wang & Jinqiang Yang, 2020. "Implications of Stochastic Transmission Rates for Managing Pandemic Risks," NBER Working Papers 27218, National Bureau of Economic Research, Inc.
    4. Gourieroux, C. & Jasiak, J., 2023. "Time varying Markov process with partially observed aggregate data: An application to coronavirus," Journal of Econometrics, Elsevier, vol. 232(1), pages 35-51.
    5. Păcurar, Cristina-Maria & Necula, Bogdan-Radu, 2020. "An analysis of COVID-19 spread based on fractal interpolation and fractal dimension," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Edwin van Leeuwen & Petra Klepac & Dominic Thorrington & Richard Pebody & Marc Baguelin, 2017. "fluEvidenceSynthesis: An R package for evidence synthesis based analysis of epidemiological outbreaks," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-12, November.
    7. Spooner, Fiona & Abrams, Jesse F. & Morrissey, Karyn & Shaddick, Gavin & Batty, Michael & Milton, Richard & Dennett, Adam & Lomax, Nik & Malleson, Nick & Nelissen, Natalie & Coleman, Alex & Nur, Jamil, 2021. "A dynamic microsimulation model for epidemics," Social Science & Medicine, Elsevier, vol. 291(C).

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    More about this item

    Keywords

    Bayesian inference; particle MCMC; population epidemic model; time-varying parameters;
    All these keywords.

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health

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