论文标题
参数化模型是由物理过程动机，用于研究Covid-19的传播流行
Parametrization Model Motivated from Physical Processes for Studying the Spread of COVID-19 Epidemic
论文作者
论文摘要
除人类健康的风险和损失之外，新病毒Covid-19的爆发在包括基本和应用科学研究在内的广泛人类活动中也引起了非常严重的问题，主要是涉及世界范围的合作。我们所有人都希望在日常病例曲线中快速预测一个转折点。在这项工作中，我们面临着COVID-19病毒疾病扩散的问题，主要是为了创建一个可靠的数学模型，该模型描述了孤立社会，城市甚至整个国家的这种机制。利用粒子探测器物理中出现的类似机制，我们集中在所谓的n度量的半高斯功能上。这种方法可以在受感染者的每日报告案件的数据分析中提供一些非常有用的优势。将此模型应用于数据，直到提交这项工作为止，我们已经确定了正在研究的社会中的公民的平均感染时间。我们还将该模型应用于其他国家的报告案件，并进行了有用的比较和结论。
The outbreak of the new virus COVID-19, beyond the human health risks and loss, has caused also very serious problems in a wide range of human activities, including the basic and applied scientific research, mainly that concern world wide collaborations. It is desirable to all of us to have the prospect of quickly predicting a turning point in the daily cases curve of the disease. In this work we face the problem of COVID-19 virus disease spreading by aiming mostly to create a reliable mathematical model describing this mechanism for an isolated society, for cities or even for a whole country. Drawing upon similar mechanisms appearing in the particle detector Physics, we concentrated to the so called, semi-gaussian function of n-degree. This approach can provide some very useful advantages in the data analysis of the daily reported cases of the infected people. Applying this model and fitting to the data, reported until the submission of this work, we have determined, among others, the mean infection time for a citizen in the society under study. We also applied and adopted this model to the reported cases in other countries and we have performed useful comparisons and conclusions.
