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. 2021 Mar 4:9:e10819.
doi: 10.7717/peerj.10819. eCollection 2021.

CoViD-19: an automatic, semiparametric estimation method for the population infected in Italy

Affiliations

CoViD-19: an automatic, semiparametric estimation method for the population infected in Italy

Livio Fenga. PeerJ. .

Abstract

To date, official data on the number of people infected with the SARS-CoV-2-responsible for the Covid-19-have been released by the Italian Government just on the basis of a non-representative sample of population which tested positive for the swab. However a reliable estimation of the number of infected, including asymptomatic people, turns out to be crucial in the preparation of operational schemes and to estimate the future number of people, who will require, to different extents, medical attentions. In order to overcome the current data shortcoming, this article proposes a bootstrap-driven, estimation procedure for the number of people infected with the SARS-CoV-2. This method is designed to be robust, automatic and suitable to generate estimations at regional level. Obtained results show that, while official data at March the 12th report 12.839 cases in Italy, people infected with the SARS-CoV-2 could be as high as 105.789.

Keywords: Autoregressive metric; Covid-19; Maximum entropy bootstrap; Model uncertainty; Number of Italian people infected.

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Conflict of interest statement

The author declares that they have no competing interests.

Figures

Figure 1
Figure 1. Percentage ratio deaths/new cases for the following Italian regions: Piemonte, Lombardia, Veneto, Liguria and Friuli-Venezia-Giulia.
Figure 2
Figure 2. Percentage ratio deaths/new cases for the following Italian regions Emilia, Toscana, Marche, Lazio and Abruzzo.
Figure 3
Figure 3. Percentage ratio deaths/new cases for the following Italian regions: Molise, Campania, Puglia, Basilicata and Calabria.
Figure 4
Figure 4. Percentage ratio deaths/new cases for the following Italian regions: Sicilia, Valle d’Aosta, Sardegna.
Figure 5
Figure 5. Percentage ratio deaths/new cases for the following Italian regions: Bolzano, Trento, Umbria.
Figure 6
Figure 6. Contraction of the infection—and time to death: delay structure.

References

    1. Andre’es MA, Pena D, Romo J. Forecasting time series with sieve bootstrap. Journal of Statistical Planning and Inference. 2002;100(1):1–11. doi: 10.1016/S0378-3758(01)00092-1. - DOI
    1. Barnard J, Rubin DB. Miscellanea: small-sample degrees of freedom with multiple imputation. Biometrika. 1999;86(4):948–955. doi: 10.1093/biomet/86.4.948. - DOI
    1. Berkowitz J, Kilian L. Recent developments in bootstrapping time series. Econometric Reviews. 2000;19(1):1–48. doi: 10.1080/07474930008800457. - DOI
    1. Carlstein E. The use of subseries values for estimating the variance of a general statistic from a stationary sequence. Annals of Statistics. 1986;14(3):1171–1179. doi: 10.1214/aos/1176350057. - DOI
    1. Clayton D, Hills M. Statistical models in epidemiology. Oxford: Oxford University Press; 2013.

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