论文标题
sirnet：使用混合神经网络模型来理解社会距离措施，可用于COVID-19
SIRNet: Understanding Social Distancing Measures with Hybrid Neural Network Model for COVID-19 Infectious Spread
论文作者
论文摘要
SARS-COV-2传染性暴发已经迅速遍及全球，并促成了不同的政策，以实现身体距离以减轻其影响。在这项研究中，我们提出了一种新的混合机器学习模型Sirnet，以预测Covid-19大流行的传播，并将其与流行病学模型相结合。我们使用分类的时空显式手机移动性数据作为物理距离的替代标记，以及人口加权密度和其他局部数据点。我们在不同的地理粒度上证明，政策领导者目前正在讨论的物理距离方案的范围在后期的后果上存在显着差异，从病毒灭绝到几乎完全人口的流行。当前的移动性转化点在地理区域各不相同。 siRNET的实验结果建立了这种局部迁移率的初步界限，渐近地诱导了遏制。该模型可以支持研究非药理干预措施和方法，以最大程度地减少社会副产品损害和控制机制。
The SARS-CoV-2 infectious outbreak has rapidly spread across the globe and precipitated varying policies to effectuate physical distancing to ameliorate its impact. In this study, we propose a new hybrid machine learning model, SIRNet, for forecasting the spread of the COVID-19 pandemic that couples with the epidemiological models. We use categorized spatiotemporally explicit cellphone mobility data as surrogate markers for physical distancing, along with population weighted density and other local data points. We demonstrate at varying geographical granularity that the spectrum of physical distancing options currently being discussed among policy leaders have epidemiologically significant differences in consequences, ranging from viral extinction to near complete population prevalence. The current mobility inflection points vary across geographical regions. Experimental results from SIRNet establish preliminary bounds on such localized mobility that asymptotically induce containment. The model can support in studying non-pharmacological interventions and approaches that minimize societal collateral damage and control mechanisms for an extended period of time.
