论文标题
使用隐藏的马尔可夫模型对COVID-19大流行的监视
Surveillance of COVID-19 Pandemic using Hidden Markov Model
论文作者
论文摘要
在过去的几个月中，COVID-19使全世界陷入困境。特别是大流行的速度使每个人都措手不及。世界各地的政府通过施加锁定，停止/限制旅行并强制社会疏远而做出回应。从积极的一面来看，每天在各个地区收集的活动病例，恢复和死亡的信息都广泛可用。但是，尤其具有挑战性的是通过称为超级宣传者的无症状携带者来追踪疾病的传播。在本文中，我们着眼于应用隐藏的马尔可夫模型，以更好地评估传播程度。这种分析的结果对于政府以校准方式设计所需的干预措施/反应很有用。我们选择分析与印度情况有关的数据。
COVID-19 pandemic has brought the whole world to a stand-still over the last few months. In particular the pace at which pandemic has spread has taken everybody off-guard. The Governments across the world have responded by imposing lock-downs, stopping/restricting travel and mandating social distancing. On the positive side there is wide availability of information on active cases, recoveries and deaths collected daily across regions. However, what has been particularly challenging is to track the spread of the disease by asymptomatic carriers termed as super-spreaders. In this paper we look at applying Hidden Markov Model to get a better assessment of extent of spread. The outcome of such analysis can be useful to Governments to design the required interventions/responses in a calibrated manner. The data we have chosen to analyze pertains to Indian scenario.
