IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v14y2022i1d10.1007_s12561-021-09320-8.html
   My bibliography  Save this article

Is 14-Days a Sensible Quarantine Length for COVID-19? Examinations of Some Associated Issues with a Case Study of COVID-19 Incubation Times

Author

Listed:
  • Yasin Khadem Charvadeh

    (University of Western Ontario)

  • Grace Y. Yi

    (University of Western Ontario
    University of Western Ontario)

  • Yuan Bian

    (University of Western Ontario)

  • Wenqing He

    (University of Western Ontario)

Abstract

To confine the spread of an infectious disease, setting a sensible quarantine time is crucial. To this end, it is imperative to well understand the distribution of incubation times of the disease. Regarding the ongoing COVID-19 pandemic, 14-days is commonly taken as a quarantine time to curb the virus spread in balancing the impacts of COVID-19 on diverse aspects of the society, including public health, economy, and humanity perspectives, etc. However, setting a sensible quarantine time is not trivial and it depends on various underlying factors. In this article, we take an angle of examining the distribution of the COVID-19 incubation time using likelihood-based methods. Our study is carried out on a dataset of 178 COVID-19 cases dated from January 20, 2020 to February 29, 2020, with the information of exposure periods and dates of symptom onset collected. To gain a good understanding of possible scenarios, we employ different models to describe incubation times of COVID-19. Our findings suggest that statistically, the 14-day quarantine time may not be long enough to control the probability of an early release of infected individuals to be small. While the size of the study data is not large enough to offer us a definitely acceptable quarantine time, and further in practice, the decision-makers may take account of other factors related to social and economic concerns to set up a practically acceptable quarantine time, our study demonstrates useful methods to determine a reasonable quarantine time from a statistical standpoint. Further, it reveals some associated complexity for fully understanding the COVID-19 incubation time distribution.

Suggested Citation

  • Yasin Khadem Charvadeh & Grace Y. Yi & Yuan Bian & Wenqing He, 2022. "Is 14-Days a Sensible Quarantine Length for COVID-19? Examinations of Some Associated Issues with a Case Study of COVID-19 Incubation Times," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(1), pages 175-190, April.
    • Handle: RePEc:spr:stabio:v:14:y:2022:i:1:d:10.1007_s12561-021-09320-8
    DOI: 10.1007/s12561-021-09320-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-021-09320-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-021-09320-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
    2. Marie Laure Delignette-Muller & Christophe Dutang, 2015. "fitdistrplus : An R Package for Fitting Distributions," Post-Print hal-01616147, HAL.
    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. Schulte, Benedikt & Sachs, Anna-Lena, 2020. "The price-setting newsvendor with Poisson demand," European Journal of Operational Research, Elsevier, vol. 283(1), pages 125-137.
    2. Avanzi, Benjamin & Taylor, Greg & Wang, Melantha & Wong, Bernard, 2021. "SynthETIC: An individual insurance claim simulator with feature control," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 296-308.
    3. K. G. Reddy & M. G. M. Khan, 2020. "stratifyR: An R Package for optimal stratification and sample allocation for univariate populations," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(3), pages 383-405, September.
    4. Chen, Shang & He, Liang & Cao, Yinxuan & Wang, Runhong & Wu, Lianhai & Wang, Zhao & Zou, Yufeng & Siddique, Kadambot H.M. & Xiong, Wei & Liu, Manshuang & Feng, Hao & Yu, Qiang & Wang, Xiaoming & He, J, 2021. "Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 258(C).
    5. Riva-Palacio, Alan & Leisen, Fabrizio, 2021. "Compound vectors of subordinators and their associated positive Lévy copulas," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    6. Minji Lee & Sun Ju Chung & Youngjo Lee & Sera Park & Jun-Gun Kwon & Dai Jin Kim & Donghwan Lee & Jung-Seok Choi, 2020. "Investigation of Correlated Internet and Smartphone Addiction in Adolescents: Copula Regression Analysis," IJERPH, MDPI, vol. 17(16), pages 1-12, August.
    7. Veronesi, F. & Grassi, S. & Raubal, M., 2016. "Statistical learning approach for wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 836-850.
    8. Phillip M. Gurman & Tom Ross & Andreas Kiermeier, 2018. "Quantitative Microbial Risk Assessment of Salmonellosis from the Consumption of Australian Pork: Minced Meat from Retail to Burgers Prepared and Consumed at Home," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2625-2645, December.
    9. Adam R. Martin & Rachel O. Mariani & Kimberley A. Cathline & Michael Duncan & Nicholas J. Paroshy & Gavin Robertson, 2022. "Soil Compaction Drives an Intra-Genotype Leaf Economics Spectrum in Wine Grapes," Agriculture, MDPI, vol. 12(10), pages 1-16, October.
    10. Héctor Nájera & David Gordon, 2023. "A Monte Carlo Study of Some Empirical Methods to Find the Optimal Poverty Line in Multidimensional Poverty Measurement," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 167(1), pages 391-419, June.
    11. Athanasios Zisos & Georgia-Konstantina Sakki & Andreas Efstratiadis, 2023. "Mixing Renewable Energy with Pumped Hydropower Storage: Design Optimization under Uncertainty and Other Challenges," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    12. Valentas Gruzauskas & Aurelija Burinskiene & Andrius Krisciunas, 2023. "Application of Information-Sharing for Resilient and Sustainable Food Delivery in Last-Mile Logistics," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    13. Amanda M. Wilson & Kelly A. Reynolds & Marc P. Verhougstraete & Robert A. Canales, 2019. "Validation of a Stochastic Discrete Event Model Predicting Virus Concentration on Nurse Hands," Risk Analysis, John Wiley & Sons, vol. 39(8), pages 1812-1824, August.
    14. Wang, Xialin & Nuppenau, Ernst-August, 2021. "Modelling payments for ecosystem services for solving future water conflicts at spatial scales: The Okavango River Basin example," Ecological Economics, Elsevier, vol. 184(C).
    15. Sarra Ghaddab & Manel Kacem & Christian Peretti & Lotfi Belkacem, 2023. "Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty," Empirical Economics, Springer, vol. 65(3), pages 1105-1127, September.
    16. Kun Mo Lee & Min Hyeok Lee & Jong Seok Lee & Joo Young Lee, 2020. "Uncertainty Analysis of Greenhouse Gas (GHG) Emissions Simulated by the Parametric Monte Carlo Simulation and Nonparametric Bootstrap Method," Energies, MDPI, vol. 13(18), pages 1-15, September.
    17. Alexander Webb & Pramesh Kumar & Alireza Khani, 2020. "Estimation of passenger waiting time using automatically collected transit data," Public Transport, Springer, vol. 12(2), pages 299-311, June.
    18. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    19. Bejarano, Adriana C. & Wells, Randall S. & Costa, Daniel P., 2017. "Development of a bioenergetic model for estimating energy requirements and prey biomass consumption of the bottlenose dolphin Tursiops truncatus," Ecological Modelling, Elsevier, vol. 356(C), pages 162-172.
    20. Negahban, Ashkan, 2019. "Simulation-based estimation of the real demand in bike-sharing systems in the presence of censoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 317-332.

    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:spr:stabio:v:14:y:2022:i:1:d:10.1007_s12561-021-09320-8. 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.springer.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.