Turkish Aerospace, R&D and Prototype Operations Directorate, Department of Innovation, Advanced Materials, Processes and Energies Technology Center, Ankara, Turkey.
Disaster Med Public Health Prep. 2022 Feb;16(1):214-222. doi: 10.1017/dmp.2020.322. Epub 2020 Sep 9.
The ongoing coronavirus disease 2019 (COVID-19) pandemic, which was initially identified in December 2019 in the city of Wuhan in China, poses a major threat to worldwide health care. By August 04, 2020, there were globally 695,848 deaths (Johns Hopkins University, https://coronavirus.jhu.edu/map.html). A total of 5765 of them come from Turkey (Johns Hopkins University, https://coronavirus.jhu.edu/map.html). As a result, various governments and their respective populations have taken strong measures to control the spread of the pandemic. In this study, a model that is by construction able to describe both government actions and individual reactions in addition to the well-known exponential spread is presented. Moreover, the influence of the weather is included. This approach demonstrates a quantitative method to track these dynamic influences. This makes it possible to numerically estimate the influence that various private or state measures that were put into effect to contain the pandemic had at time t. This might serve governments across the world by allowing them to plan their actions based on quantitative data to minimize the social and economic consequences of their containment strategies.
A compartmental model based on SEIR that includes the risk perception of the population by an additional differential equation and uses an implicit time-dependent transmission rate is constructed. Within this model, the transmission rate depends on temperature, population, and government actions, which in turn depend on time. The model was tested using different scenarios, with the different dynamic influences being mathematically switched on and off. In addition, the real data of infected coronavirus cases in Turkey were compared with the results of the model.
The mathematical study of the influence of the different parameters is presented through different scenarios. Remarkably, the last scenario is also an example of a theoretical mitigation strategy that shows its maximum in August 2020. In addition, the results of the model are compared with the real data from Turkey using conventional fitting that shows good agreement.
Although most countries activated their pandemic plans, significant disruptions in health-care systems occurred. The framework of this model seems to be valid for a numerical analysis of dynamic processes that occur during the COVID-19 outbreak due to weather and human reactions. As a result, the effects of the measures introduced could be better planned in advance by use of this model.
2019 年冠状病毒病（COVID-19）疫情最初于 2019 年 12 月在中国武汉市发现，对全球卫生保健构成重大威胁。截至 2020 年 8 月 4 日，全球共有 695848 人死亡（约翰霍普金斯大学，https://coronavirus.jhu.edu/map.html）。其中共有 5765 人来自土耳其（约翰霍普金斯大学，https://coronavirus.jhu.edu/map.html）。因此，各国政府及其各自的民众采取了强有力的措施来控制疫情的传播。在这项研究中，提出了一种模型，该模型可以构造性地描述政府的行动以及个人的反应，此外还描述了众所周知的指数传播。此外，还包括了天气的影响。该方法展示了一种跟踪这些动态影响的定量方法。这使得可以对各种为控制疫情而实施的私人或国家措施在 t 时刻的影响进行数值估计。这可以使世界各国政府能够根据定量数据来规划其行动，以最大程度地减少其遏制策略的社会和经济后果。
构建了一个基于 SEIR 的房室模型，该模型包括人口对风险的感知，通过附加的微分方程，并使用隐时变的传播率。在该模型中，传播率取决于温度、人口和政府的行动，而政府的行动又取决于时间。该模型使用不同的场景进行了测试，不同的动态影响被数学上打开和关闭。此外，还将土耳其受感染冠状病毒病例的实际数据与模型的结果进行了比较。
通过不同的场景呈现了不同参数的影响的数学研究。值得注意的是，最后一个场景也是一种理论缓解策略的示例，该策略在 2020 年 8 月达到峰值。此外，还使用常规拟合将模型的结果与土耳其的实际数据进行了比较，结果显示拟合良好。
尽管大多数国家都启动了大流行计划，但卫生保健系统仍受到重大干扰。由于天气和人类反应，该模型的框架似乎适用于对 COVID-19 爆发期间发生的动态过程进行数值分析。因此，可以通过使用该模型更好地预先计划引入的措施的效果。
