This work aims to model, simulate and provide insights into the dynamics and control of COVID-19 infection rates. Using an established epidemiological model augmented with a time-varying disease transmission rate allows daily model calibration using COVID-19 case data from countries around the world. This hybrid model provides predictive forecasts of the cumulative number of infected cases. It also reveals the dynamics associated with disease suppression, demonstrating the time to reduce the effective, time-dependent, reproduction number. Model simulations provide insights into the outcomes of disease suppression measures and the predicted duration of the pandemic. Visualisation of reported data provides up-to-date condition monitoring, while daily model calibration allows for a continued and updated forecast of the current state of the pandemic.

这项工作旨在对COVID-19感染率的动态和控制进行建模，模拟并提供见解。使用建立的流行病学模型并随疾病传播率的变化而变化，可以使用来自世界各国的COVID-19病例数据进行每日模型校准。该混合模型提供了感染病例累计数量的预测性预测。它还揭示了与疾病抑制相关的动力学，展示了减少时间的有效时间依赖性繁殖次数。模型模拟提供了对疾病抑制措施的结果以及预计的大流行持续时间的见解。所报告数据的可视化提供了最新的状态监视，而每日模型校准允许对流行病的当前状态进行持续和更新的预测。