计算机科学
缩放比例
原始数据
传输(电信)
编码(集合论)
领域(数学分析)
数据挖掘
数学
电信
数学分析
几何学
集合(抽象数据类型)
程序设计语言
作者
Peter J. Thomas,Aidan Marvell
标识
DOI:10.1038/s41598-022-26552-w
摘要
The scaling behaviour of agent-based computational models, to evaluate transmission risks of infectious diseases, is addressed. To this end we use an existing computational code, made available in the public domain by its author, to analyse the system dynamics from a general perspective. The goal being to obtain deeper insight into the system behaviour than can be obtained from considering raw data alone. The data analysis collapses the output data for infection numbers and leads to closed-form expressions for the results. It is found that two parameters are sufficient to summarize the system development and the scaling of the data. One of the parameters characterizes the overall system dynamics. It represents a scaling factor for time when expressed in iteration steps of the computational code. The other parameter identifies the instant when the system adopts its maximum infection rate. The data analysis methodology presented constitutes a means for a quantitative intercomparison of predictions for infection numbers, and infection dynamics, for data produced by different models and can enable a quantitative comparison to real-world data.
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