论文标题
估计，监测和预测COVID-19流行病：应用于NYC数据的时空方法
Estimating, Monitoring, and Forecasting the Covid-19 Epidemics: A Spatio-Temporal Approach Applied to NYC Data
论文作者
论文摘要
我们提出了一个SEIR型元群体模型，以模拟和监测COVID-19的流行进化。基本模型由七个隔间组成，即易感性（S），暴露（E），三个感染性类别，恢复（R）和已故（D）。我们在不同的空间位置为N年龄和性别群体定义了这些隔室。因此，所得模型对于每个年龄段的性别和地点都有所有流行病学类别。它们之间的混合是通过时间依赖性感染率矩阵完成的。该模型通过纽约市及其行政区每日新感染的曲线进行校准，包括人口普查数据以及每个年龄段的感染，住院和死亡的比例。我们最终获得了与报告曲线相匹配的模型，并预测了不同地方和年龄段的准确感染信息。
We propose an SEIR-type meta-population model to simulate and monitor the Covid-19 epidemic evolution. The basic model consists of seven compartments, namely susceptible (S), exposed (E), three infective classes, recovered (R), and deceased (D). We define these compartments for n age and gender groups in m different spatial locations. So, the resulting model has, for each age group, gender, and place, all epidemiological classes. The mixing between them is accomplished by means of time-dependent infection rate matrices. The model is calibrated with the curve of daily new infections in New York City and its boroughs, including census data, and the proportions of infections, hospitalizations, and deaths for each age range. We end up with a model that matches the reported curves and predicts accurately infection information for different places and age classes.
