A COVINDEX based on a GAM beta regression model with an application to the COVID-19 pandemic in Italy

04/03/2021 ∙ by Luca Scrucca, et al. ∙ Università Perugia 0

Detecting changes in COVID-19 disease transmission over time is a key indicator of epidemic growth. Near real-time monitoring of the pandemic growth is crucial for policy makers and public health officials who need to make informed decisions about whether to enforce lockdowns or allow certain activities. The effective reproduction number Rt is the standard index used in many countries for this goal. However, it is known that due to the delays between infection and case registration, its use for decision making is somewhat limited. In this paper a near real-time COVINDEX is proposed for monitoring the evolution of the pandemic. The index is computed from predictions obtained from a GAM beta regression for modelling the test positive rate as a function of time. The proposal is illustrated using data on COVID-19 pandemic in Italy and compared with Rt. A simple chart is also proposed for monitoring local and national outbreaks by policy makers and public health officials.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

page 8

page 9

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1 Introduction

2 Statistical Model for the Test Positive Rate

3 COVINDEX as a Monitoring and Decision-Making Tool

4 Application to Italian COVID-19 Pandemic

5 A comparison of COVINDEX with the effective reproduction number

6 Final comments

References

  • Adam (2020) Adam, D. (2020). A guide to R – the pandemic’s misunderstood metric. Nature, 583(7816):346–348. https://www.nature.com/articles/d41586-020-02009-w.
  • Cori et al. (2013) Cori, A., Ferguson, N. M., Fraser, C., and Cauchemez, S. (2013). A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology, 178(9):1505–1512.
  • Douma and Weedon (2019) Douma, J. C. and Weedon, J. T. (2019). Analysing continuous proportions in ecology and evolution: A practical introduction to beta and Dirichlet regression. Methods in Ecology and Evolution, 10(9):1412–1430.
  • Ferrari and Cribari-Neto (2004) Ferrari, S. and Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31(7):799–815.
  • Gelman and Hill (2006) Gelman, A. and Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
  • Gostic et al. (2020) Gostic, K. M., McGough, L., Baskerville, E. B., Abbott, S., Joshi, K., Tedijanto, C., Kahn, R., Niehus, R., Hay, J. A., De Salazar, P. M., et al. (2020). Practical considerations for measuring the effective reproductive number, . PLoS Computational Biology, 16(12):e1008409. DOI: https://doi.org/10.1371/journal.pcbi.1008409.
  • Guzzetta and Merler (2020) Guzzetta, G. and Merler, S. (2020). Stime della trasmissibilità di SARS-CoV-2 in Italia. EpiCentro - Istituto Superiore di Sanità: https://www.epicentro.iss.it/coronavirus/open-data/rt.pdf.
  • Haroz et al. (2015) Haroz, S., Kosara, R., and Franconeri, S. L. (2015). The connected scatterplot for presenting paired time series. IEEE Transactions on Visualization and Computer Graphics, 22(9):2174–2186.
  • Hastie and Tibshirani (1990) Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models, volume 43. CRC press.
  • Hilton and Keeling (2020) Hilton, J. and Keeling, M. J. (2020). Estimation of country-level basic reproductive ratios for novel coronavirus (SARS-CoV-2/COVID-19) using synthetic contact matrices. PLoS Computational Biology, 16(7):e1008031.
  • Li et al. (2020) Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., Ren, R., Leung, K. S., Lau, E. H., Wong, J. Y., et al. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine, 382:1199–1207.
  • McCullagh and Nelder (1989) McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models. Chapman and Hall, CRC, London, 2nd edition.
  • Nazar and Elfadil (2021) Nazar, Z. and Elfadil, A. (2021). The estimations of the COVID-19 incubation period: A scoping reviews of the literature. Journal of Infection and Public Health, Available online 8 February 2021. DOI: https://doi.org/10.1016/j.jiph.2021.01.019.
  • Presidenza del Consiglio dei Ministri – Dipartimento della Protezione Civile (2020) Presidenza del Consiglio dei Ministri – Dipartimento della Protezione Civile (2020). Dati COVID-19 Italia. GitHub repository: https://github.com/pcm-dpc/COVID-19.
  • Wilke (2019) Wilke, C. O. (2019).

    Fundamentals of data visualization: a primer on making informative and compelling figures

    .
    O’Reilly Media.
  • Wood (2017) Wood, S. N. (2017). Generalized Additive Models: an introduction with R. CRC press, 2nd edition.
  • World Health Organization (2019) World Health Organization (2019). Public health criteria to adjust public health and social measures in the context of COVID-19. Annex to Considerations in adjusting public health and social measures in the context of COVID-19, 12 May 2020: https://apps.who.int/iris/bitstream/handle/10665/332073/WHO-2019-nCoV-Adjusting_PH_measures-Criteria-2020.1-eng.pdf.
  • World Health Organization (2020) World Health Organization (2020). Considerations for implementing and adjusting public health and social measures in the context of COVID-19. Iterim guidance, 4 November 2020: https://www.who.int/publications/i/item/considerations-in-adjusting-public-health-and-social-measures-in-the-context-of-covid-19-interim-guidance.
  • Zeileis and Cribari-Neto (2010) Zeileis, A. and Cribari-Neto, F. (2010). Beta regression in R. Journal of Statistical Software, 34(2):1–24.