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variability scaling and capacity planning in Covid-19 pandemic

Fundamental Research 2023年第4期3卷 627-639页

作者： L.Jeff Hong Guangwu Liu Jun Luo Jingui Xie School of Management and School of Data Science Fudan UniversityShanghai 200433China College of Business City University of Hong KongKowloon TongHong Kong 999077China Antai College of Economics and Management Shanghai Jiao Tong UniversityShanghai 200030China School of Management Technical University of MunichHeilbronn 74076Germany Munich Data Science Institute Technical University of MunichMunich 80333Germany

Capacity planning is a very important global challenge in the face of Covid-19 *** order to hedge against the fluctuations in the random demand and to take advantage of risk pooling effect,one needs to have a good understanding of the variabilities in the demand of ***,Covid-19 predictive models that are widely used in capacity planning typically often predict the mean values of the demands(often through the predictions of the mean values of the confirmed cases and deaths)in both the temporal and spatial *** seldom provide trustworthy prediction or estimation of demand variabilities,and therefore,are insufficient for proper capacity *** by the literature on variability scaling in the areas of physics and biology,we discovered that in the Covid-19 pandemic,both the confirmed cases and deaths exhibit a common variability scaling law between the average of the demand μ and its standard deviationσ,that is,σ ∝ μ^(β),where the scaling parameterμis typically in the range of 0.65 to 1,and the scaling law exists in both the temporal and spatial *** on the mechanism of contagious diseases,we further build a stylized network model to explain the variability scaling *** finally provide simple models that may be used for capacity planning in both temporal and spatial dimensions,with only the predicted mean demand values from typical Covid-19 predictive models and the standard deviations of the demands derived from the variability scaling law.

关键词： Covid-19 Capacity planning variability scaling Demand aggregation Network model Risk pooling effect

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