Literature DB >> 33717677

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

Livio Fenga1.   

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.
© 2021 Fenga.

Entities:  

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

Year:  2021        PMID: 33717677      PMCID: PMC7937344          DOI: 10.7717/peerj.10819

Source DB:  PubMed          Journal:  PeerJ        ISSN: 2167-8359            Impact factor:   2.984


  1 in total

1.  Incidence, prevalence, and evidence. Scientific problems in epidemiologic statistics for the occurrence of cancer.

Authors:  A R Feinstein; J M Esdaile
Journal:  Am J Med       Date:  1987-01       Impact factor: 4.965

  1 in total
  2 in total

1.  Modelling the downhill of the Sars-Cov-2 in Italy and a universal forecast of the epidemic in the world.

Authors:  Gabriele Martelloni; Gianluca Martelloni
Journal:  Chaos Solitons Fractals       Date:  2020-07-01       Impact factor: 9.922

2.  Predictive Capacity of COVID-19 Test Positivity Rate.

Authors:  Livio Fenga; Mauro Gaspari
Journal:  Sensors (Basel)       Date:  2021-04-01       Impact factor: 3.576

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.