Since instances of coronavirus disease 2019 (COVID-19) community spread emerged in the United States , federal and local governments have implemented multiple containment measures. However, in order to satisfy the needs of its citizens, the strictest containment measures can be only executed for short period. This article compares two types of containment strategies : a constant containment strategy that could satisfy the needs of citizens for a long period and an adaptive containment strategy whose strict level changes across time. When to implement the strictest measure is also of interest. A dynamic prediction model is proposed and a simple tool is developed for policy makers to compare different containment strategies. As an example, a region with population 2.8 million and 200 initial infectious cases is considered assuming a 4\% mortality rate . We found that compared with a constant containment strategy, adaptive containment strategies shorten the outbreak length and executing the strictest measures early will cause less mortality .
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 .
R
Since instances
The emergence
D
community spread emerged
R
,
has forced
R
have implemented multiple
to implement
R
However, in order to satisfy the needs of its citizens, the
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
R
can be only executed for short period. This article compares two
and how different treatments can increase or suppress infected cases. Two
R
:
are compared: (1)
R
and
; and (2)
R
When to implement the strictest measure is also of interest. A dynamic prediction model is proposed and a simple tool is developed for policy makers to compare different containment strategies. As an example,
We consider how an effective treatment of the disease can affect the dynamics in a pandemic scenario. For illustration we consider
D
is considered
R
. We found that
compared with a 2\% mortality rate if a new drug is available. Our results show
R
executing the strictest measures early will cause less mortality
reduce maximum daily number of cases. This, along with an effective treatment plan for the disease can minimize death rate