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The emergence of coronavirus disease 2019 (COVID-19) in the United States hasforced federal and local governments to implement containment measures.Moreover, the severity of the situation has sparked engagement by both theresearch and clinical community with the goal of developing effectivetreatments for the disease. This article proposes a time dynamic predictionmodel with competing risks for the infected individual and develops a simpletool for policy makers to compare different strategies in terms of when toimplement the strictest containment measures and how different treatments canincrease or suppress infected cases. Two types of containment strategies arecompared: (1) a constant containment strategy that could satisfy the needs ofcitizens for a long period; and (2) an adaptive containment strategy whosestrict level changes across time. We consider how an effective treatment of thedisease can affect the dynamics in a pandemic scenario. For illustration weconsider a region with population 2.8 million and 200 initial infectious casesassuming a 4% mortality rate compared with a 2% mortality rate if a new drug isavailable. Our results show compared with a constant containment strategy,adaptive containment strategies shorten the outbreak length and reduce maximumdaily number of cases. This, along with an effective treatment plan for thedisease can minimize death rate.

Dynamic Competing Risk Modeling COVID-19 in a Pandemic Scenario