IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-53095-7.html
   My bibliography  Save this article

Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics

Author

Listed:
  • Hyukpyo Hong

    (KAIST
    Institute for Basic Science
    University of Wisconsin–Madison)

  • Eunjin Eom

    (Korea University)

  • Hyojung Lee

    (Kyungpook National University)

  • Sunhwa Choi

    (National Institute for Mathematical Sciences)

  • Boseung Choi

    (Institute for Basic Science
    Korea University
    College of Public Health)

  • Jae Kyoung Kim

    (KAIST
    Institute for Basic Science)

Abstract

Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.

Suggested Citation

  • Hyukpyo Hong & Eunjin Eom & Hyojung Lee & Sunhwa Choi & Boseung Choi & Jae Kyoung Kim, 2024. "Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53095-7
    DOI: 10.1038/s41467-024-53095-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-53095-7
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-53095-7?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. Helen J Wearing & Pejman Rohani & Matt J Keeling, 2005. "Appropriate Models for the Management of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 2(7), pages 1-1, July.
    2. Katelyn M Gostic & Lauren McGough & Edward B Baskerville & Sam Abbott & Keya Joshi & Christine Tedijanto & Rebecca Kahn & Rene Niehus & James A Hay & Pablo M De Salazar & Joel Hellewell & Sophie Meaki, 2020. "Practical considerations for measuring the effective reproductive number, Rt," PLOS Computational Biology, Public Library of Science, vol. 16(12), pages 1-21, December.
    3. Serina Chang & Emma Pierson & Pang Wei Koh & Jaline Gerardin & Beth Redbird & David Grusky & Jure Leskovec, 2021. "Mobility network models of COVID-19 explain inequities and inform reopening," Nature, Nature, vol. 589(7840), pages 82-87, January.
    4. Qingchao Jiang & Xiaoming Fu & Shifu Yan & Runlai Li & Wenli Du & Zhixing Cao & Feng Qian & Ramon Grima, 2021. "Neural network aided approximation and parameter inference of non-Markovian models of gene expression," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    5. Felix Dewald & Isabelle Suárez & Ronja Johnen & Jan Grossbach & Roberto Moran-Tovar & Gertrud Steger & Alexander Joachim & Gibran Horemheb Rubio & Mira Fries & Florian Behr & Joao Kley & Andreas Lingn, 2022. "Effective high-throughput RT-qPCR screening for SARS-CoV-2 infections in children," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    6. Guangping Huang & Ling Li, 2009. "A mathematical model of infectious diseases," Annals of Operations Research, Springer, vol. 168(1), pages 41-80, April.
    7. Ndaïrou, Faïçal & Area, Iván & Nieto, Juan J. & Torres, Delfim F.M., 2020. "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    8. Elisabeta Vergu & Henri Busson & Pauline Ezanno, 2010. "Impact of the Infection Period Distribution on the Epidemic Spread in a Metapopulation Model," PLOS ONE, Public Library of Science, vol. 5(2), pages 1-16, February.
    9. Xingjie Hao & Shanshan Cheng & Degang Wu & Tangchun Wu & Xihong Lin & Chaolong Wang, 2020. "Reconstruction of the full transmission dynamics of COVID-19 in Wuhan," Nature, Nature, vol. 584(7821), pages 420-424, August.
    10. Tipsri, S. & Chinviriyasit, W., 2015. "The effect of time delay on the dynamics of an SEIR model with nonlinear incidence," Chaos, Solitons & Fractals, Elsevier, vol. 75(C), pages 153-172.
    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. Hee-Koung Joeng & Abidemi K. Adeniji & Naitee Ting & Ming-Hui Chen, 2022. "Estimation of Discrete Survival Function through Modeling Diagnostic Accuracy for Mismeasured Outcome Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 105-138, April.
    2. Lorenzo Pellis & Paul J. Birrell & Joshua Blake & Christopher E. Overton & Francesca Scarabel & Helena B. Stage & Ellen Brooks‐Pollock & Leon Danon & Ian Hall & Thomas A. House & Matt J. Keeling & Jon, 2022. "Estimation of reproduction numbers in real time: Conceptual and statistical challenges," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S1), pages 112-130, November.
    3. Lahrouz, A. & El Mahjour, H. & Settati, A. & Bernoussi, A., 2018. "Dynamics and optimal control of a non-linear epidemic model with relapse and cure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 299-317.
    4. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    5. Boeing, Philipp & Wang, Yihan, 2021. "Decoding China's Covid-19 "virus exceptionalism": Community-based digital contact tracing in Wuhan," ZEW Discussion Papers 21-028, ZEW - Leibniz Centre for European Economic Research.
    6. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    7. Basnarkov, Lasko, 2021. "SEAIR Epidemic spreading model of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. Yoon, Jisung & Park, Jinseo & Yun, Jinhyuk & Jung, Woo-Sung, 2023. "Quantifying knowledge synchronization with the network-driven approach," Journal of Informetrics, Elsevier, vol. 17(4).
    9. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    10. X. Angela Yao & Andrew Crooks & Bin Jiang & Jukka Krisp & Xintao Liu & Haosheng Huang, 2023. "An overview of urban analytical approaches to combating the Covid-19 pandemic," Environment and Planning B, , vol. 50(5), pages 1133-1143, June.
    11. Reese Richardson & Emile Jorgensen & Philip Arevalo & Tobias M. Holden & Katelyn M. Gostic & Massimo Pacilli & Isaac Ghinai & Shannon Lightner & Sarah Cobey & Jaline Gerardin, 2022. "Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    12. Mishra, A.M. & Purohit, S.D. & Owolabi, K.M. & Sharma, Y.D., 2020. "A nonlinear epidemiological model considering asymptotic and quarantine classes for SARS CoV-2 virus," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    13. Lifeng Zhang & Roy E. Welsch & Zhi Cao, 2022. "The Transmission, Infection Prevention, and Control during the COVID-19 Pandemic in China: A Retrospective Study," IJERPH, MDPI, vol. 19(5), pages 1-15, March.
    14. Richard C. Larson, 2007. "Simple Models of Influenza Progression Within a Heterogeneous Population," Operations Research, INFORMS, vol. 55(3), pages 399-412, June.
    15. 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.
    16. Robin N Thompson & Christopher A Gilligan & Nik J Cunniffe, 2016. "Detecting Presymptomatic Infection Is Necessary to Forecast Major Epidemics in the Earliest Stages of Infectious Disease Outbreaks," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-18, April.
    17. Wan, Jinming & Ichinose, Genki & Small, Michael & Sayama, Hiroki & Moreno, Yamir & Cheng, Changqing, 2022. "Multilayer networks with higher-order interaction reveal the impact of collective behavior on epidemic dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    18. Palatella, Luigi & Vanni, Fabio & Lambert, David, 2021. "A phenomenological estimate of the true scale of CoViD-19 from primary data," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    19. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," Working Papers 21-18, Federal Reserve Bank of Philadelphia.
    20. Wang, Yupeng & Shimokawa, Satoru, 2024. "A trade-off between lives and the economy? Subsidizing dining out under the COVID-19 pandemic in Japan," Food Policy, Elsevier, vol. 124(C).

    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:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53095-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.