The recent COVID-19 pandemic has promoted vigorous scientific activity in an effort to understand, advice and control the pandemic. Data is now freely available at a staggering rate worldwide. Unfortunately, this unprecedented level of information contains a variety of data sources and formats, and the models do not always conform to the description of the data. Health officials have recognized the need for more accurate models that can adjust to sudden changes, such as produced by changes in behavior or social restrictions. In this work we formulate a model that fits a ``SIR''-type model concurrently with a statistical change detection test on the data. The result is a piece wise autonomous ordinary differential equation, whose parameters change at various points in time (automatically learned from the data). The main contributions of our model are: (a) providing interpretation of the parameters, (b) determining which parameters of the model are more important to produce changes in the spread of the disease, and (c) using data-driven discovery of sudden changes in the evolution of the pandemic. Together, these characteristics provide a new model that better describes the situation and thus, provides better quality of information for decision making.
翻译:近期的COVID-19大流行促进了活跃的科学活动,以努力理解、咨询和控制这一流行病,现在全世界可以以惊人的速度免费获得数据;不幸的是,这一前所未有的信息水平包含各种数据来源和格式,模型并不总是符合数据说明; 卫生官员认识到需要有更准确的模型,能够适应突发变化,如行为变化或社会限制造成的突然变化; 在这项工作中,我们在数据统计变化检测测试的同时,制定适合“SIR”型模型的模型; 结果是有一条明智的自主普通差异方程式,其参数在不同时间点的变化(自动从数据中学习); 我们模型的主要贡献是:(a) 提供参数解释,(b) 确定模型的哪些参数对于改变疾病的传播更为重要,(c) 利用数据驱动的发现来改变该流行病的演变。这些特征共同提供了一个新的模型,可以更好地描述情况,从而提供更好的决策信息质量。