论文标题

比较英国Covid-19的不同出口场景的不同出口方案,并评估预测的不确定性

Comparison of different exit scenarios from the lock-down for COVID-19 epidemic in the UK and assessing uncertainty of the predictions

论文作者

Zhigljavsky, Anatoly, Whitaker, Roger, Fesenko, Ivan, Kremnizer, Kobi, Noonan, Jack

论文摘要

鉴于当前的数据并假设处理流行病的情况不同,我们对英国的共同-19流行病的进一步发展进行了建模。在这项研究中,我们进一步扩展了在\ cite {us}中建议的随机模型,并在其中纳入了所有这些都可以了解有关特征病毒行为及其引起的疾病的参数的知识。我们使用的模型灵活,全面,可以快速运行,并允许我们纳入以下内容: - 处理流行病的时间依赖性策略;人口的空间异质性和不同地区流行病的异质性; - 特定人群的特征,尤其是具有特定医学前途径和老年人的人。 标准的流行病学模型(例如SIR及其许多修改)不够灵活,因此在需要使用上面特征的研究中不够精确。决策者可以从使用更好,更灵活的模型中获得严重的好处,因为他们可以避免细微的锁定,更好地计划基于当地人口数据的退出策略,在不同领域的流行阶段的不同阶段,向特定人群提出了具体建议;所有这些都会导致对经济的影响较小,改善了对NHS地区需求的预测,从而允许智能资源分配。

We model further development of the COVID-19 epidemic in the UK given the current data and assuming different scenarios of handling the epidemic. In this research, we further extend the stochastic model suggested in \cite{us} and incorporate in it all available to us knowledge about parameters characterising the behaviour of the virus and the illness induced by it. The models we use are flexible, comprehensive, fast to run and allow us to incorporate the following: -time-dependent strategies of handling the epidemic; -spatial heterogeneity of the population and heterogeneity of development of epidemic in different areas; -special characteristics of particular groups of people, especially people with specific medical pre-histories and elderly. Standard epidemiological models such as SIR and many of its modifications are not flexible enough and hence are not precise enough in the studies that requires the use of the features above. Decision-makers get serious benefits from using better and more flexible models as they can avoid of nuanced lock-downs, better plan the exit strategy based on local population data, different stages of the epidemic in different areas, making specific recommendations to specific groups of people; all this resulting in a lesser impact on economy, improved forecasts of regional demand upon NHS allowing for intelligent resource allocation.

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