论文标题
基于移动性的大流行模型 - 与19号案例研究
Mobility Based SIR Model For Pandemics -- With Case Study Of COVID-19
论文作者
论文摘要
在过去的十年中，人类面临许多不同的大流行病，例如SARS，H1N1和目前新颖的冠状病毒（Covid-19）。一方面，科学家专注于疫苗接种，另一方面，有必要提出模型，以帮助我们理解这些大流行病的传播，因为它可以帮助政府和其他相关机构做好充分的准备，尤其是从大流行中，尤其是从类似于Covid-19的速度传播。某种流行病变成大流行病的主要原因是世界不同地区之间的连通性，这使得在全球范围内更容易影响更广泛的地理区域。此外，世界不同地区的人口分布和社会连贯性是不均匀的。因此，一旦流行病进入一个地区，局部人口分布就起着重要作用。受这些想法的启发，我们提出了一个基于流动性的流行病模型，尤其考虑了大流行的情况。据我们所知，该模型首先是此类模型，它考虑了全球不同地理位置的人口分布和连通性。除了介绍模型的数学证明外，我们还使用合成数据进行了广泛的模拟，以证明我们的模型的推广性。为了展示模型的更广泛范围，我们使用模型来预测爱沙尼亚的Covid-19案例。
In the last decade, humanity has faced many different pandemics such as SARS, H1N1, and presently novel coronavirus (COVID-19). On one side, scientists are focusing on vaccinations, and on the other side, there is a need to propose models that can help us in understanding the spread of these pandemics as it can help governmental and other concerned agencies to be well prepared, especially from pandemics, which spreads faster like COVID-19. The main reason for some epidemic turning into pandemics is the connectivity among different regions of the world, which makes it easier to affect a wider geographical area, often worldwide. In addition, the population distribution and social coherence in the different regions of the world is non-uniform. Thus, once the epidemic enters a region, then the local population distribution plays an important role. Inspired by these ideas, we proposed a mobility-based SIR model for epidemics, which especially takes into account pandemic situations. To the best of our knowledge, this model is first of its kind, which takes into account the population distribution and connectivity of different geographic locations across the globe. In addition to presenting the mathematical proof of our model, we have performed extensive simulations using synthetic data to demonstrate our model's generalizability. To demonstrate the wider scope of our model, we used our model to forecast the COVID-19 cases for Estonia.
