论文标题
基于实际地理和人口数据的COVID-19建模
COVID-19 Modeling Based on Real Geographic and Population Data
论文作者
论文摘要
际旅行是打击大流行的最重要参数之一。持续的共同19岁大流行导致涉及城间连接的不同计算研究。在这项研究中，使用新的网络模型评估了诸如Covid-19的流行病（例如COVID-19）期间中心联系的影响。该模型考虑了实际的地理社区和人口密度数据。该新模型通过省级联系和人口将其应用于实际的土耳其数据。具有混合晶格模型的蒙特卡洛算法应用于具有8,802个数据点的晶格。结果表明，通过估计死亡人数，疾病蔓延和流行病终止，基于人群和地理互动的现实世界中，该模型在对现实世界中的Covid-19模型中非常有效。
Intercity travel is one of the most important parameters for combating a pandemic. The ongoing COVID-19 pandemic has resulted in different computational studies involving intercity connections. In this study, the effects of intercity connections during an epidemic such as COVID-19 are evaluated using a new network model. This model considers the actual geographic neighborhood and population density data. This new model is applied to actual Turkish data by the means of provincial connections and populations. A Monte Carlo algorithm with a hybrid lattice model is applied to a lattice with 8,802 data points. Results show that this model is quantitatively very efficient in modeling real world COVID-19 epidemic data based on populations and geographical intercity connections, by the means of estimating the number of deaths, disease spread, and epidemic termination.
