论文标题
建模COVID-19-III：印度地方性传播
Modelling COVID-19-III: endemic spread in India
论文作者
论文摘要
当特定人群中的疾病表现出稳定的患病率时被称为地方性。我们解决了关于19岁在印度的流行程度的相关问题。有几种研究流行行为的现有模型，例如传统时间sir模型的扩展或Held等人的时空流行模型。 （2005年）及其扩展。我们提出了一个可以在各种空间分辨率下部署的最先进的线性模型状态下的“时空重力模型”。在更精细的空间尺度上，在Covid-19的背景下没有常规和质量协变量的情况下，我们利用除空运乘客数量等外部协变量，使我们能够有效地捕获当地的流动性和社交互动。这使得拟议的模型与现有模型不同。提出的重力模型不仅会产生一致的估计器，而且在应用于印度COVID-19数据时，还会优于其他模型。
A disease in a given population is termed endemic when it exhibits a steady prevalence. We address the pertinent question as to what extent COVID-19 has turned endemic in India. There are several existing models for studying endemic behaviour, such as the extensions of the traditional temporal SIR model or the spatio-temporal endemic-epidemic model of Held et al. (2005) and its extensions. We propose a "spatio-temporal Gravity model" in a state of the art generalised linear model set up that can be deployed at various spatial resolutions. In absence of routine and quality covariates in the context of COVID-19 at finer spatial scales, we make use of extraneous covariates like air-traffic passenger count that enables us to capture the local mobility and social interactions effectively. This makes the proposed model different from the existing models. The proposed gravity model not only produces consistent estimators, but also outperforms the other models when applied to Indian COVID-19 data.
