Data-Driven Prediction in Complex Systems of Virus Evolution and Global Warming  

复杂系统的数据驱动预测——新冠病毒演化和全球气候变暖

作　　者：LUO Liaofu LÜJun 罗辽复;吕军(内蒙古大学物理科学与技术学院,呼和浩特010021;内蒙古工业大学理学院,呼和浩特010051)

机构地区：[1]School of Physical Science and Technology,Inner Mongolia University,Hohhot 010021,China [2]College of Science,Inner Mongolia University of Technology,Hohhot 010051,China

出　　处：《内蒙古大学学报(自然科学版)》2025年第1期1-7,共7页Journal of Inner Mongolia University:Natural Science Edition

基　　金：Natural science foundation of Inner Mongolia(2024LHMS06018);The basic scientific research funding for directly affiliated universities in the Inner Mongolia(JY20250094)。

摘　　要：A complex system is inherently high-dimensional.Recent studies indicate that,even without complete knowledge of its evolutionary dynamics,the future behavior of such a system can be predicted using time-series data(data-driven prediction).This suggests that the essential dynamics of a complex system can be captured through a low-dimensional representation.Virus evolution and climate change are two examples of complex,time-varying systems.In this article,we show that mutations in the spike protein provide valuable data for predicting SARS-CoV-2 variants,forecasting the possible emergence of the new macro-lineage Q in the near future.Our analysis also demonstrates that carbon dioxide concentration is a reliable indicator for predicting the evolution of the climate system,extending global surface air temperature(GSAT)forecasts through 2500.对于实际复杂系统,由于内在机制过于复杂,在无法获知其完整动力学方程情况下,可以通过演化产生的时间序列来进行预测。本文对两个复杂系统进行数据驱动预测。对新冠病毒演化问题,依据病毒刺突蛋白的氨基酸突变数据进行突变株的谱系预测,发现新的Q谱系可能在最近产生。对全球气候变暖问题,证明二氧化碳浓度是一个好的气候演化指标,由此出发将全球地表温度预测一直推广到2500年。

关 键 词：data-drivenprediction complex system virus evolution global warming 

分 类 号：N94[自然科学总论—系统科学] O231.5[理学—运筹学与控制论]