Variability scaling and capacity planning in Covid-19 pandemic

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)627-639
Journal / PublicationFundamental Research
Volume3
Issue number4
Online published13 May 2022
Publication statusPublished - Jul 2023

Link(s)

Abstract

Capacity planning is a very important global challenge in the face of Covid-19 pandemic. In 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 resources. However, 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 dimensions. They seldom provide trustworthy prediction or estimation of demand variabilities, and therefore, are insufficient for proper capacity planning. Motivated 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 deviation σ, that is, σμβ, where the scaling parameter β is typically in the range of 0.65 to 1, and the scaling law exists in both the temporal and spatial dimensions. Based on the mechanism of contagious diseases, we further build a stylized network model to explain the variability scaling phenomena. We 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. © 2022 The Authors. Publishing Services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

Research Area(s)

  • Capacity planning, Covid-19, Demand aggregation, Network model, Risk pooling effect, Variability scaling

Citation Format(s)

Variability scaling and capacity planning in Covid-19 pandemic. / Jeff Hong, L.; Liu, Guangwu; Luo, Jun et al.
In: Fundamental Research, Vol. 3, No. 4, 07.2023, p. 627-639.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Download Statistics

No data available