Hypothesis testing of Poisson rates in COVID-19 offspring distributions  

作　　者：Rui Luo 

机构地区：[1]Department of Systems Engineering,City University of Hong Kong,Kowloon Town,Hong Kong Special Administrative Region

出　　处：《Infectious Disease Modelling》2023年第4期980-1001,共22页传染病建模（英文）

基　　金：supported by a grant from City University of Hong Kong (Project No.9610639).

摘　　要：In the present study,we undertake the task of hypothesis testing in the context of Poissondistributed data.The primary objective of our investigation is to ascertain whether two distinct sets of discrete data share the same Poisson rate.We delve into a comprehensive review and comparative analysis of various frequentist and Bayesian methodologies specifically designed to address this problem.Among these are the conditional test,the likelihood ratio test,and the Bayes factor.Additionally,we employ the posterior predictive p-value in our analysis,coupled with its corresponding calibration procedures.As the culmination of our investigation,we apply these diverse methodologies to test both simulated datasets and real-world data.The latter consists of the offspring distributions linked to COVID-19 cases in two disparate geographies-Hong Kong and Rwanda.This allows us to provide a practical demonstration of the methodologies’applications and their potential implications in the field of epidemiology.

关 键 词：Poisson distribution Hypothesis testing Bayes factor Posterior predictive P-VALUE COVID-19 

分 类 号：R563.1[医药卫生—呼吸系统]