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

Livio Fenga | Zendy; Carlo Del Castello | Zendy

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

Author(s) -

Livio Fenga,

Carlo Del Castello

Publication year - 2021

Publication title -

journal of probability and statistics

Language(s) - English

Resource type - Journals

eISSN - 1687-9538

pISSN - 1687-952X

DOI - 10.1155/2021/1235973

Subject(s) - covid-19 , metaheuristic , generalization , mathematical optimization , set (abstract data type) , data set , point (geometry) , computer science , power (physics) , data mining , mathematics , artificial intelligence , medicine , mathematical analysis , physics , geometry , disease , pathology , quantum mechanics , infectious disease (medical specialty) , programming language , virology , outbreak , biology

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.

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