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1 引言准确地预测传染病流行的发生时间,即对疾病在时间维上的分布作出准确的描述,对疾病的预防与管理均具有重大意义。理论流行病学方法的发展,已使得人们能够用数学模型来刻画传染病流行的数量变化过程。迄今所采用的数工具主要限于微分方程和概率统计方法。当把疾病在人群中的传播现为(确定性的)动力学过程时,就用微分方程去刻画它;当把它视为随机过程时,就用概率统计方法去描述之。然而,把疾病的传播与流行视为确定性的动力学过程,并据此去求出感染力等参数,就可能因疾病传播中的随机性而使预测结果与真实情形有较大的距离;而把它视为随机过程,则需要有关随机变量的概率分布的
1 INTRODUCTION It is of great significance to accurately predict the time of occurrence of an epidemic of infectious diseases, that is, to accurately describe the distribution of the disease in time dimension, and to prevent and manage the disease. The development of theoretical epidemiological methods has enabled one to use mathematical models to characterize the quantitative change in the spread of an epidemic. The tools used so far are mainly limited to differential equations and statistical methods of probability. When the spread of a disease in a population is a (deterministic) kinetic process, it is characterized by a differential equation; when it is considered a stochastic process, it is described by a statistical approach to probability. However, considering the spread and prevalence of disease as a deterministic kinetic process and deriving parameters such as infectivity based on it, it is possible that there is a large distance between the predicted result and the actual situation due to the randomness in disease transmission. And treat it as a stochastic process, you need the probability distribution of random variables