论文标题
通过空间分辨的易感暴露感染的（SEIRD）模型模拟COVID-19与异质扩散模型的传播（SEIRD）模型
Simulating the spread of COVID-19 via spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion
论文作者
论文摘要
我们提出了基于偏微分方程与异质扩散模型的部分微分方程的易感性暴露感染的（SEIRD）数学模型的早期版本。该模型描述了共同-19大流行的时空传播，并旨在基于人类习惯和地理特征来捕获动力学。 To test the model, we compare the outputs generated by a finite-element solver with measured data over the Italian region of Lombardy, which has been heavily impacted by this crisis between February and April 2020. Our results show a strong qualitative agreement between the simulated forecast of the spatio-temporal COVID-19 spread in Lombardy and epidemiological data collected at the municipality level.其他模拟探讨了放松锁定限制的替代方案，这表明重新开放策略应解释当地人口密度和传染的具体动态。因此，我们认为，我们模型的数据驱动模拟最终可以告知卫生当局采取有效的大流行措施，并预测关键医疗资源的地理分配。
We present an early version of a Susceptible-Exposed-Infected-Recovered-Deceased (SEIRD) mathematical model based on partial differential equations coupled with a heterogeneous diffusion model. The model describes the spatio-temporal spread of the COVID-19 pandemic, and aims to capture dynamics also based on human habits and geographical features. To test the model, we compare the outputs generated by a finite-element solver with measured data over the Italian region of Lombardy, which has been heavily impacted by this crisis between February and April 2020. Our results show a strong qualitative agreement between the simulated forecast of the spatio-temporal COVID-19 spread in Lombardy and epidemiological data collected at the municipality level. Additional simulations exploring alternative scenarios for the relaxation of lockdown restrictions suggest that reopening strategies should account for local population densities and the specific dynamics of the contagion. Thus, we argue that data-driven simulations of our model could ultimately inform health authorities to design effective pandemic-arresting measures and anticipate the geographical allocation of crucial medical resources.
