基于SIR模型预测新型冠状病毒在广东省的传播
SIR Model-Based Prediction of Infected Population of Corona Virus in Guangdong Province

自2019年底以来，全球爆发的COVID-19疫情对全球公共卫生系统带来了前所未有的挑战。传染病模型，尤其是SIR模型，成为疾病传播和防控研究的关键工具。本文采用数据驱动的建模方法研究新型冠状病毒在广东省的传播，首先采集广东省疫情初期的数据，然后通过采用有限差分方法和数据拟合求解得到SIR模型的两个重要参数：传染率和康复率，最后应用龙格库塔方法对SIR模型进行数值求解。本文的模型可以更好地理解疫情传播过程，为决策者提供科学依据，有助于政府和卫生机构制定更合理的防控措施和资源分配策略。
Since the end of 2019, the global outbreak of the COVID-19 pandemic has presented unprecedented challenges to the global public health system. The SIR model have become essential tools for researching disease transmission and control. This paper employs a data-driven modeling approach to study the spread of the novel coronavirus in Guangdong Province. Initially, data from the early stages of the pandemic in Guangdong Province is collected. Subsequently, by utilizing finite difference methods and data fitting, two crucial parameters of the SIR model, namely the transmission rate and recovery rate, are obtained. Finally, the Runge-Kutta method is applied for numerical solutions of the SIR model. The model presented in this paper offers a better understanding of the pandemic transmission process, providing decision-makers with a scientific foundation. This contributes to the formulation of more rational prevention and control measures, as well as resource allocation strategies by governments and health organizations.