A compounded method&#x2014;exploiting the searching capabilities of an operation research algorithm and the power of bootstrap techniques&#x2014;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.

Livio Fenga

Carlo Del Castello

COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data

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&#x2014;for a time horizon of 10&#x2009;days starting from March 29th&#x2014;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&#x2014;within the forecast period considered (March 29th&#x2013;April 7th)&#x2014;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&#x2019;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.

Forecasting the COVID-19 Diffusion in Italy and the Related Occupancy of Intensive Care Units

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Table of Contents

Table of Contents: 2021

COVID-19: Metaheuristic Optimization-Based Forecast Method on Time-Dependent Bootstrapped Data

Forecasting the COVID-19 Diffusion in Italy and the Related Occupancy of Intensive Care Units