基于拟合优度检验的无症状感染者统计模型分析——以上海疫情为例
Statistical Model Analysis of Asymptomatic Patients Based on Goodness of Fit Test—Taking the Shanghai Epidemic as an Example

新冠病毒的不断变异和大规模人群接种疫苗等原因，使得疫情爆发地区中的无症状感染者增多。分析和掌握无症状感染者的增长规律，对制定防疫政策和精准防疫措施有重要的参考意义。本文对2022年上海疫情中无症状感染者数据进行统计分布拟合，分析拟合分布的偏态、峰值和分位点，发现伽马分布和对数正态分布与真实分布较为近似，并通过EDF统计量对拟合分布作拟合优度检验，结果显示伽马分布检验p值较大，检验通过率较高，其拟合效果较好。
Due to the continuous mutation of novel coronavirus and mass vaccination of the population, the number of asymptomatic patients in regional outbreaks has increased. Analyzing and mastering the growth pattern of asymptomatic infections has important reference significance for formulating epidemic prevention policies and precise epidemic prevention measures. This paper performs sta-tistical distribution fitting on the data of asymptomatic patients in the Shanghai epidemic in 2022, analyzes skewness, peaks, and quantiles of fitted distributions, it is found that the gamma distribu-tion and lognormal distribution are more approximate to the true distribution. In this paper, EDF statistic is used to test the goodness of fit of the fitting distributions. The results show that the p-value of the gamma distribution test is large, and the test pass rate is high, indicating that the fit-ting effect is good.