Universidade Federal de Santa Catarina, Campus Trindade, 88, Florianopolis, SC, 040-900, Brazil.
Federal University of Rio Grande do Sul, PO Box 15064, Av. Bento Gonçalves, 9500, Porto Alegre, RS, 91501-970, Brazil.
Sci Rep. 2021 Apr 27;11(1):9089. doi: 10.1038/s41598-021-88281-w.
We propose a susceptible-exposed-infective-recovered-type (SEIR-type) meta-population model to simulate and monitor the (COVID-19) epidemic evolution. The basic model consists of seven categories, namely, susceptible (S), exposed (E), three infective classes, recovered (R), and deceased (D). We define these categories for n age and sex groups in m different spatial locations. Therefore, the resulting model contains all epidemiological classes for each age group, sex, and location. 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 finally obtain a model that matches the reported curves and predicts accurate infection information for different locations and age classes.
我们提出了一个易感-暴露-感染-恢复型（SEIR 型）的元种群模型，用于模拟和监测（COVID-19）疫情的演变。基本模型由七个类别组成，即易感（S）、暴露（E）、三种感染类别、恢复（R）和死亡（D）。我们为 m 个不同空间位置的 n 个年龄和性别组定义了这些类别。因此，该模型包含了每个年龄组、性别和地点的所有流行病学类别。通过时变感染率矩阵来实现它们之间的混合。该模型使用纽约市及其行政区的每日新增感染曲线进行校准，包括人口普查数据以及每个年龄范围的感染、住院和死亡比例。我们最终得到了一个与报告曲线匹配并能准确预测不同地点和年龄组感染信息的模型。
