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Adaptive COVID-19 Screening of a Subpopulation

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Studies in Theoretical and Applied Statistics (SIS 2021)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 406))

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Abstract

Methods are sought to test adaptively whether a subpopulation proportion follows the same time evolution as the population proportion. The motivating case study is the COVID-19 screening in a university community, taking into account the time evolution of the pandemic in the whole country.

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References

  1. European Center for Disease prevention and Control: COVID-19 clusters and outbreaks in occupational settings in the EU/EEA and the UK (2020)

    Google Scholar 

  2. Buckeridge, D.L., Burkom, H., Campbell, M., Hogan, W.R., Moore, A.W.: Algorithms for rapid outbreak detection: a research synthesis. J. Biomed. Inform. (2005). https://doi.org/10.1016/j.jbi.2004.11.007

    Article  Google Scholar 

  3. Leclère, B., Buckeridge, D.L., Boëlle, P.Y., Astagneau, P., Lepelletier, D.: Automated detection of hospital outbreaks: a systematic review of methods. PLOS ONE (2017). https://doi.org/10.1371/journal.pone.0176438

  4. Tukey, J.: Exploratory Data Analysis. Addison-Wesley Pub, Co (1977)

    MATH  Google Scholar 

  5. Hawkins, D.M.: Identification of Outliers. Springer (1980)

    Google Scholar 

  6. Montgomery, D.C.: Introduction to Statistical Quality Control. Wiley (2019)

    Google Scholar 

  7. Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods. Springer (2009)

    Google Scholar 

  8. Stoica, P., Selen, Y.: Model-order selection. IEEE Sig. Process. Mag. (2004). https://doi.org/10.1109/msp.2004.1311138

    Article  Google Scholar 

  9. Schwarz, G.: Estimating the dimension of a model. Ann. Stat. (1978). https://doi.org/10.1214/aos/1176344136

    Article  MathSciNet  MATH  Google Scholar 

  10. https://github.com/pcm-dpc/COVID-19/. Cited 20 Dec 2021

  11. Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. In: Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character (1927). https://doi.org/10.1098/rspa.1927.0118

  12. Amongero, M., Bibbona, E., Mastrantonio, G.: Analysing the Covid-19 pandemic in Italy with the SIPRO model. Book of short papers SIS 2021

    Google Scholar 

  13. Giordano, G., Blanchini, F., Bruno, R., et al.: Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat. Med. (2020). https://doi.org/10.1038/s41591-020-0883-7

    Article  Google Scholar 

  14. Giordano, G., Colaneri, M., Filippo, A.D., et al.: Modeling vaccination rollouts, SARS-CoV-2 variants and the requirement for non-pharmaceutical interventions in Italy. Nat. Med. (2021). https://doi.org/10.1038/s41591-021-01334-5

    Article  Google Scholar 

  15. Kerr, C.C., Stuart, R.M., Mistry, D., et al.: Covasim: an agent-based model of COVID-19 dynamics and interventions. PLOS Comput. Biol. (2021). https://doi.org/10.1371/journal.pcbi.1009149

    Article  Google Scholar 

  16. Farcomeni, A., Maruotti, A., Divino, F., Jona-Lasinio, G., Lovison, G.: An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian regions. Biom. J. (2020). https://doi.org/10.1002/bimj.202000189

    Article  Google Scholar 

  17. Fokas, A.S., Dikaios, N., Kastis, G.A.: Mathematical models and deep learning for predicting the number of individuals reported to be infected with SARS-CoV-2. J. R. Soc. Interface (2020). https://doi.org/10.1098/rsif.2020.0494

    Article  Google Scholar 

  18. Kissler, S.M., Tedijanto, C., Goldstein, E., Grad, Y.H., Lipsitch, M.: Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science (2020). https://doi.org/10.1126/science.abb5793

    Article  Google Scholar 

  19. https://cran.r-project.org/web/packages/forecast/index.html. Cited 20 Dec 2021

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Acknowledgements

The authors would like to thank Paola Lerario and Maurizio Galetto for insights and details on the organization of the screening procedure in Politecnico di Torino and Enrico Bibbona for some critical discussions. The authors would like to thank also two anonymous reviewers, who greatly helped to improve the quality of the manuscript.

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Correspondence to Fulvio Di Stefano .

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Di Stefano, F., Gasparini, M. (2022). Adaptive COVID-19 Screening of a Subpopulation. In: Salvati, N., Perna, C., Marchetti, S., Chambers, R. (eds) Studies in Theoretical and Applied Statistics . SIS 2021. Springer Proceedings in Mathematics & Statistics, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-031-16609-9_8

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