Analysis and Modeling of Dynamic Processes to Measure Social Behavior Change in the Context of COVID-19

被引：0

作者：

Pucheta, Julian ^{[1
]}

Salas, Carlos ^{[2
]}

Herrera, Martin ^{[2
]}

Daniel Patino, Hector ^{[3
]}

Rodriguez Riveros, Cristian ^{[4
]}

机构：

[1] Univ Nacl Cordoba, Fac Ciencias Exactas Fis & Nat, Cordoba, Argentina

[2] Univ Nacl Catamarca, Fac Tecnol & Ciencias Aplicadas, Catamarca, Argentina

[3] Univ Nacl San Juan, Fac Ingn, Inst Automat, San Juan, Argentina

[4] Univ Amsterdam, Wiskunde Informat, Amsterdam, Netherlands

来源：

2020 IEEE CONGRESO BIENAL DE ARGENTINA, ARGENCON | 2020年

关键词：

Systems identification; Dynamic process modeling; Dynamic processes; COVID-19;

D O I：

10.1109/ARGENCON49523.2020.9505520

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A methodology based on dynamic process modeling and time-series forecasting is described to establish changes in social behavior with respect to the new rules of coexistence required by COVID-19. The measurement of daily infections is the output of the system being identified, and the input is the mobility generated by automated systems such as Apple or Google. If social behavior regarding the pandemic were perfect, the number of daily infections would tend to decrease even as people's mobility increases, and would be described by a concave function of time; that is, there would be a peak and then a downward trend. On the other hand, when the evolution of daily infections is a curve described as a convex function of time, it is more difficult to establish parameters that allow us to determine whether or not social behavior is adapting to the new rules of coexistence required by COVID-19. This paper proposes a methodology that measures the improvement in society's behavior in the context of COVID.

引用

页数：6

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