Rt<1啦,上海疫情流行趋势分析 r语言

上海新冠疫情可视化面板
基本再生数R0是指在一个完全易感人群中，没有任何干预措施的情况下，一个被传染病病原体感染的个体所能引起的第二代感染病例数，常用于衡量病原体自身的传播力。(“流行病学专家解读
| 传染病的有效再生数与基本再生数,” n.d.)
时变再生数Rt可以理解为真实世界(往往有干预措施，人群不是完全易感)情形下，t时刻的再生数,
受干预措施，感染者及易感者比例等因素影响，随时间变化而变化。隔离等干预措施的目标在于将Rt降低至1以下，Rt?\ 使用R语言EpiEstim包估计Rt(Thompson et al.
2019)，需要假设系列间隔(serial
interval)的分布,本文中假设系列间隔均值为4天，标准差为2天(“新型冠状病毒Omicron变异株的流行病学特征及其科学防控建议,”
n.d.)。使用EpiEstim包默认的1周滑动窗进行分析。结果如下：

library(tidyverse) library(EpiEstim) library(patchwork) ## load data case.asym.wider.sh<-read.csv('https://raw.githubusercontent.com/shalom-lab/covid.sh/main/local/share/case.asym.wider.sh.csv')# observation cases<-case.asym.wider.sh %>% select(date,pos) %>% mutate(date=as.Date(date)) %>% rename(I=pos,dates=date)## make config config <- make_config( mean_si = 4, std_si = 2 )## estimate res <- estimate_R( incid = cases, method = "parametric_si", config = config )#plot(res)res.r<-res$R %>% as_tibble() %>% rename(mean=`Mean(R)`,std=`Std(R)`,lbd=`Quantile.0.025(R)`,ubd=`Quantile.0.975(R)`) %>% mutate(date=cases$dates[res$R$t_end])res.si <- as_tibble(list(time=as.integer(str_sub(names(res$si_distr),2)), frequency=as.vector(res$si_distr)))p1<-ggplot(data = https://www.it610.com/article/cases,aes(x=dates,y=I))+ geom_col(fill="#AD002AFF")+ scale_x_date(date_breaks = "2 days",date_labels = "%m/%d",expand = c(0,0.5))+ labs(x="",y="每日新增阳性数",title="Epidemic curve")+ theme_bw()+ theme(axis.text.x = element_text(angle=45,vjust=0.5,hjust = 0.5))p2<-ggplot(data = https://www.it610.com/article/res.r,aes(x=date,y=mean))+ geom_ribbon(aes(ymin=lbd,ymax=ubd),fill="#AD002AFF",alpha=0.2)+ geom_line(size=1,colour= "#AD002AFF")+ geom_hline(yintercept = 1,size=1,lty=2)+ scale_x_date(date_breaks = "2 days",date_labels = "%m/%d",expand = c(0,0.5),limits = c(as.Date('2022-03-09'),Sys.Date()-1))+ labs(x="",y="时变再生数Rt",title='')+ theme_bw()+ theme(axis.text.x = element_text(angle=45,vjust=0.5,hjust = 0.5))p3<-ggplot(data = https://www.it610.com/article/res.si,aes(x=time,y=frequency))+ geom_line()+ labs(x="Time",y="Frequency",title='Assumptive Serial Interval Distribution')+ theme_bw()p1+p2+plot_layout(ncol = 1) p3

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结果显示目前时变再生数Rt已降至1以下，Rt持续处于1以下将表示疫情得到有效控制,希望继续保持，期待早日解封~
参考资料 [1] Thompson, R. N., J. E. Stockwin, R. D. van Gaalen, J. A. Polonsky, Z. N.
Kamvar, P. A. Demarsh, E. Dahlqwist, et al. 2019. “Improved Inference of
Time-Varying Reproduction Numbers During Infectious Disease Outbreaks.”
Epidemics 29 (December): 100356.
https://doi.org/10.1016/j.epi....
[2] “新型冠状病毒Omicron变异株的流行病学特征及其科学防控建议.”
http://mp.weixin.qq.com/s?__b....
[3] “流行病学专家解读 | 传染病的有效再生数与基本再生数.”
http://mp.weixin.qq.com/s?__b....
【Rt<1啦,上海疫情流行趋势分析】