IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0074208.html
   My bibliography  Save this article

Interior-Point Methods for Estimating Seasonal Parameters in Discrete-Time Infectious Disease Models

Author

Listed:
  • Daniel P Word
  • James K Young
  • Derek A T Cummings
  • Sopon Iamsirithaworn
  • Carl D Laird

Abstract

Infectious diseases remain a significant health concern around the world. Mathematical modeling of these diseases can help us understand their dynamics and develop more effective control strategies. In this work, we show the capabilities of interior-point methods and nonlinear programming (NLP) formulations to efficiently estimate parameters in multiple discrete-time disease models using measles case count data from three cities. These models include multiplicative measurement noise and incorporate seasonality into multiple model parameters. Our results show that nearly identical patterns are estimated even when assuming seasonality in different model parameters, and that these patterns show strong correlation to school term holidays across very different social settings and holiday schedules. We show that interior-point methods provide a fast and flexible approach to parameterizing models that can be an alternative to more computationally intensive methods.

Suggested Citation

  • Daniel P Word & James K Young & Derek A T Cummings & Sopon Iamsirithaworn & Carl D Laird, 2013. "Interior-Point Methods for Estimating Seasonal Parameters in Discrete-Time Infectious Disease Models," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-13, October.
  • Handle: RePEc:plo:pone00:0074208
    DOI: 10.1371/journal.pone.0074208
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0074208
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0074208&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0074208?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. William E. Hart & Carl Laird & Jean-Paul Watson & David L. Woodruff, 2012. "Pyomo – Optimization Modeling in Python," Springer Optimization and Its Applications, Springer, edition 127, number 978-1-4614-3226-5, December.
    2. 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.
    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. 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.
    2. 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.
    3. 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.
    4. Yingjie Fan & Frank Schwartz & Stefan Voß & David L. Woodruff, 2017. "Stochastic programming for flexible global supply chain planning," Flexible Services and Manufacturing Journal, Springer, vol. 29(3), pages 601-633, December.
    5. Denis Valle & James Clark, 2013. "Improving the Modeling of Disease Data from the Government Surveillance System: A Case Study on Malaria in the Brazilian Amazon," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-14, November.
    6. Reveron Baecker, Beneharo & Candas, Soner, 2022. "Co-optimizing transmission and active distribution grids to assess demand-side flexibilities of a carbon-neutral German energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    7. 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.
    8. 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.
    9. Bernard Cazelles & Clara Champagne & Joseph Dureau, 2018. "Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models," PLOS Computational Biology, Public Library of Science, vol. 14(8), pages 1-26, August.
    10. Caroline Chuard & Hannes Schwandt & Alexander D. Becker & Masahiko Haraguchi, 2022. "Economic vs. Epidemiological Approaches to Measuring the Human Capital Impacts of Infectious Disease Elimination," NBER Working Papers 30202, National Bureau of Economic Research, Inc.
    11. 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.
    12. Alejandro Pena-Bello & Edward Barbour & Marta C. Gonzalez & Selin Yilmaz & Martin K. Patel & David Parra, 2020. "How Does the Electricity Demand Profile Impact the Attractiveness of PV-Coupled Battery Systems Combining Applications?," Energies, MDPI, vol. 13(15), pages 1-19, August.
    13. 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.
    14. 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.
    15. 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.
    16. Patrick W. Schmidt, 2020. "Inference under Superspreading: Determinants of SARS-CoV-2 Transmission in Germany," Papers 2011.04002, arXiv.org.
    17. 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.
    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.
    19. 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.
    20. Saki Takahashi & Qiaohong Liao & Thomas P Van Boeckel & Weijia Xing & Junling Sun & Victor Y Hsiao & C Jessica E Metcalf & Zhaorui Chang & Fengfeng Liu & Jing Zhang & Joseph T Wu & Benjamin J Cowling , 2016. "Hand, Foot, and Mouth Disease in China: Modeling Epidemic Dynamics of Enterovirus Serotypes and Implications for Vaccination," PLOS Medicine, Public Library of Science, vol. 13(2), pages 1-29, February.

    More about this item

    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:plo:pone00:0074208. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.