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.
Highlights
Covid-19 epidemic has severely hit Italy, and its spread throughout Europe is expected soon
Official data on the infection in Italy are based on non-random, non-representative samples of the population: people are tested for Covid-19 on the condition that some symptoms related to the virus are present
It is widespread opinion in the scientific community that current official data on the diffusion of SARS-CoV-2, responsible of the correlated disease, COIVD-19,among population, are likely to suffer from a strong downward bias
Summary
Covid-19 epidemic has severely hit Italy, and its spread throughout Europe is expected soon. Official data on the infection in Italy are based on non-random, non-representative samples of the population: people are tested for Covid-19 on the condition that some symptoms related to the virus are present These data can ensure a proper estimation of the number of both deaths and hospitalizations due to the virus and are crucial for the optimization of the available resources. The presented procedure is designed to reduce the impact of the biasing components on the parameter estimations, by employing a resampling scheme, called Maximum Entropy Bootstrap (MEBOOT) proposed by Vinod & López-de Lacalle (2009) This bootstrap method is suitable in this context: as it will be outlined in the sequel it is designed to work with a broad class of time series (including non stationary ones) and—by virtue of its inherent simplicity—is able to generate bona fide replications in the case of short time series. T 1⁄4 1; . . . T À 1 and define intervals It constructed on ct and rt, using ad hoc weights obtained by solving the following set of equations:
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