评估哈萨克斯坦COVID-19曲线变平的时间相关限制和控制措施的影响
Assessing the effects of time-dependent restrictions and control actions to flatten the curve of COVID-19 in Kazakhstan
摘要：
本文件评估国家层面的时间限制及控制措施及其对抗击COVID-19疫情的影响。通过分析该国第一波COVID-19期间的传播动态，可以更好地量化和理解为使曲线变平而采取的各级控制行动的有效性。这反过来又有助于有关当局更好地规划和控制随后的大流行浪潮。为了实现这一目标，首先开发了一个确定性的流行病的人口模型，考虑到模型参数的时间依赖性，特别是不断变化的再生数的值，这是用来描述这种流行病的传播动力学的关键措施之一。然后，可以通过使用数值优化技术将模型拟合到真实世界数据或通过诱导新闻平台中记录的ad-hoc控制动作来估计模型的再现数以及其他关键参数。本文使用基于哈萨克斯坦共和国第一波COVID-19数据的案例研究对该模型进行了验证。该模型适合提供模拟中的两个设置的估计;时不变和时变（有界约束）参数。最后，使用四种情景与时间相关的控制措施，以确定哪些会更好地反映实际情况进行了一些预测。
英文摘要：
This paper presents the assessment of time-dependent national-level restrictions and control actions and their effects in fighting the COVID-19 pandemic. By analysing the transmission dynamics during the first wave of COVID-19 in the country, the effectiveness of the various levels of control actions taken to flatten the curve can be better quantified and understood. This in turn can help the relevant authorities to better plan for and control the subsequent waves of the pandemic. To achieve this, a deterministic population model for the pandemic is firstly developed to take into consideration the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measures used to describe the transmission dynamics of this pandemic. The reproduction number alongside other key parameters of the model can then be estimated by fitting the model to real-world data using numerical optimisation techniques or by inducing ad-hoc control actions as recorded in the news platforms. In this paper, the model is verified using a case study based on the data from the first wave of COVID-19 in the Republic of Kazakhstan. The model is fitted to provide estimates for two settings in simulations; time-invariant and time-varying (with bounded constraints) parameters. Finally, some forecasts are made using four scenarios with time-dependent control measures so as to determine which would reflect on the actual situations better.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
