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A Knowledge Extraction from Epidemic Control Simulation

Authors:
Masaaki Kunigami
Takamasa Kikuchi
Takao Terano

Keywords: epidemic model; Data Envelopment Analysis, DEA classification

Abstract:
In a social simulation of infection control policies using an epidemic model, it is necessary to consider the latent perspectives of stakeholders focusing on various costs or effects. Therefore, we classify the simulation results produced by various combinations of control policies from the perspective of latent evaluation. In this paper, we use the data envelopment analysis (DEA) method to classify the results of epidemic control simulations. DEA is an analytic method that measures and compares the efficiency of multiple input multiple output systems' performance data. From various latent evaluation perspectives, DEA classification allows us to endogenously extract knowledge about the superiority of each control policy over the others from various latent evaluation perspectives. This approach is a good example of classification and knowledge extraction for a set of social simulation logs.

Pages: 21 to 26

Copyright: Copyright (c) IARIA, 2021

Publication date: July 18, 2021

Published in: conference

ISSN: 2308-4375

ISBN: 978-1-61208-874-7

Location: Nice, France

Dates: from July 18, 2021 to July 22, 2021