论文标题
在大流行的情况下，动态竞争风险建模Covid-19
Dynamic Competing Risk Modeling COVID-19 in a Pandemic Scenario
论文作者
论文摘要
2019年冠状病毒疾病的出现（Covid-19）在美国迫使联邦和地方政府采取遏制措施。此外，这种情况的严重性激发了研究和临床界的参与，目的是为该疾病开发有效的治疗方法。本文提出了一个时间动态预测模型，对受感染者的风险具有竞争风险，并为决策者开发了一个简单的工具，可以在何时实施严格的遏制措施以及不同治疗方法如何增加或抑制感染病例方面比较不同的策略。比较了两种类型的遏制策略：（1）一种可以长期满足公民需求的恒定遏制策略； （2）一种自适应遏制策略，其严格的水平在整个时间变化。我们考虑对疾病的有效治疗如何影响大流行的情况。为了说明，我们考虑一个人口为280万和200个初始感染病例的地区，假设死亡率为4％，而如果有新药可用，则死亡率为2％。我们的结果表明，与恒定的遏制策略相比，自适应遏制策略缩短了暴发的长度并减少了每日最大案件数量。这与疾病的有效治疗计划可以最大程度地降低死亡率。
The emergence of coronavirus disease 2019 (COVID-19) in the United States has forced federal and local governments to implement containment measures. Moreover, the severity of the situation has sparked engagement by both the research and clinical community with the goal of developing effective treatments for the disease. This article proposes a time dynamic prediction model with competing risks for the infected individual and develops a simple tool for policy makers to compare different strategies in terms of when to implement the strictest containment measures and how different treatments can increase or suppress infected cases. Two types of containment strategies are compared: (1) a constant containment strategy that could satisfy the needs of citizens for a long period; and (2) an adaptive containment strategy whose strict level changes across time. We consider how an effective treatment of the disease can affect the dynamics in a pandemic scenario. For illustration we consider a region with population 2.8 million and 200 initial infectious cases assuming a 4% mortality rate compared with a 2% mortality rate if a new drug is available. Our results show compared with a constant containment strategy, adaptive containment strategies shorten the outbreak length and reduce maximum daily number of cases. This, along with an effective treatment plan for the disease can minimize death rate.
