Mathematical models are useful in epidemiology to understand COVID-19 contagion dynamics. We aim to demonstrate the effectiveness of parameter regression methods to calibrate an established epidemiological model describing infection rates subject to active, varying non-pharmaceutical interventions (NPIs). We assess the potential of established chemical engineering modelling principles and practice applied to epidemiological systems. We exploit the sophisticated parameter regression functionality of a commercial chemical engineering simulator with piecewise continuous integration, event and discontinuity management. We develop a strategy for calibrating and validating a model. Our results using historic data from 4 countries provide insights into on-going disease suppression measures, while visualisation of reported data provides up-to-date condition monitoring of the pandemic status. The effective reproduction number response to NPIs is non-linear with variable response rate, magnitude and direction. Our purpose is developing a methodology without presenting a fully optimised model, or attempting to predict future course of the COVID-19 pandemic.

中文翻译：

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COVID-19：机械模型校准受到积极和不同的非药物干预的影响


数学模型在流行病学中有助于了解 COVID-19 的传染动态。我们的目的是证明参数回归方法的有效性，以校准已建立的流行病学模型，该模型描述了受积极的、不同的非药物干预措施（NPI）影响的感染率。我们评估已建立的化学工程建模原理和实践应用于流行病学系统的潜力。我们利用商业化学工程模拟器的复杂参数回归功能，进行分段连续集成、事件和不连续性管理。我们制定了校准和验证模型的策略。我们使用来自 4 个国家的历史数据得出的结果提供了对正在进行的疾病抑制措施的见解，而报告数据的可视化则提供了对大流行状况的最新状况监测。 NPI 的有效再生数响应是非线性的，响应率、幅度和方向各不相同。我们的目的是开发一种方法，而不是提出完全优化的模型，或尝试预测 COVID-19 大流行的未来进程。