ABSTRACT
COVID-19 infections can spread silently, due to the simultaneous presence of significant numbers of both critical and asymptomatic to mild cases. While, for the former reliable data are available (in the form of number of hospitalization and/or beds in intensive care units), this is not the case of the latter. Hence, analytical tools designed to generate reliable forecast and future scenarios, should be implemented to help decision-makers to plan ahead (e.g., medical structures and equipment). Previous work of one of the authors shows that an alternative formulation of the Test Positivity Rate (TPR), i.e., the proportion of the number of persons tested positive in a given day, exhibits a strong correlation with the number of patients admitted in hospitals and intensive care units. In this paper, we investigate the lagged correlation structure between the newly defined TPR and the hospitalized people time series, exploiting a rigorous statistical model, the Seasonal Auto Regressive Moving Average (SARIMA). The rigorous analytical framework chosen, i.e., the stochastic processes theory, allowed for a reliable forecasting about 12 days ahead of those quantities. The proposed approach would also allow decision-makers to forecast the number of beds in hospitals and intensive care units needed 12 days ahead. The obtained results show that a standardized TPR index is a valuable metric to monitor the growth of the COVID-19 epidemic. The index can be computed on daily basis and it is probably one of the best forecasting tools available today for predicting hospital and intensive care units overload, being an optimal compromise between simplicity of calculation and accuracy.
Subject(s)
COVID-19 , Forecasting , Humans , Intensive Care Units , Models, Statistical , SARS-CoV-2ABSTRACT
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.
ABSTRACT
A compounded method—exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques—is presented. The resulting algorithm has been successfully tested to predict the turning point reached by the epidemic curve followed by the COVID-19 virus in Italy. Future lines of research, which include the generalization of the method to a broad set of distribution, will be finally given. [ABSTRACT FROM AUTHOR] Copyright of Journal of Probability & Statistics is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
This paper provides a model-based method for the forecast of the total number of currently COVID-19 positive individuals and of the occupancy of the available intensive care units in Italy. The predictions obtained—for a time horizon of 10 days starting from March 29th—will be provided at a national as well as at a more disaggregated level, following a criterion based on the magnitude of the phenomenon. While those regions hit the most by the pandemic have been kept separated, those less affected regions have been aggregated into homogeneous macroareas. Results show that—within the forecast period considered (March 29th–April 7th)—all of the Italian regions will show a decreasing number of COVID-19 positive people. The same will be observed for the number of people who will need to be hospitalized in an intensive care unit. These estimates are valid under constancy of the government’s current containment policies. In this scenario, northern regions will remain the most affected ones, whereas no significant outbreaks are foreseen in the southern regions.