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Abstract：
We present two different approaches for modeling the spread of the COVID-19 pandemic. Both approaches are based on the population classes susceptible, exposed, infectious, quarantined, and recovered and allow for an arbitrary number of subgroups with different infection rates and different levels of testing. The first model is derived from a set of ordinary differential equations that incorporates the rates at which population transitions take place among classes. The other is a particle model, which is a specific case of crowd simulation model, in which the disease is transmitted through particle collisions and infection rates are varied by adjusting the particle velocities. The parameters of these two models are tuned using information on COVID-19 from the literature and country-specific data, including the effect of restrictions as they were imposed and lifted. We demonstrate the applicability of both models using data from Cyprus, for which we find that both models yield very similar results, giving confidence in the predictions.
摘要：
我们提出了两种不同的方法来模拟COVID-19大流行的传播。两种方法都基于易感人群类别，暴露,传染性,已隔离,并恢复，并允许任意数量的具有不同感染率和不同测试水平的亚组。第一个模型是从一组常微分方程中得出的，该微分方程包含了类别之间发生人口过渡的速率。另一个是粒子模型，这是人群模拟模型的一个特例，其中疾病通过粒子碰撞传播，感染率通过调整粒子速度而变化。这两个模型的参数是使用文献中关于COVID-19的信息和特定国家的数据进行调整的，包括施加和解除限制的影响。我们使用塞浦路斯的数据证明了这两个模型的适用性，我们发现这两个模型产生非常相似的结果，对预测有信心。
