On the Evolution Equation for Modelling the Covid-19 Pandemic

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dc.contributor.authorBlackledge, J
dc.date.accessioned2022-08-03T20:51:21Z
dc.date.available2022-08-03T20:51:21Z
dc.date.issued2021
dc.description.abstractThe paper introduces and discusses the evolution equation, and, based exclusively on this equation, considers random walk models for the time series available on the daily confirmed Covid-19 cases for different countries. It is shown that a conventional random walk model is not consistent with the current global pandemic time series data, which exhibits non-ergodic properties. A self-affine random walk field model is investigated, derived from the evolutionary equation for a specified memory function which provides the non-ergodic fields evident in the available Covid-19 data. This is based on using a spectral scaling relationship of the type 1/ωα where ω is the angular frequency and α ∈ (0, 1) conforms to the absolute values of a normalised zero mean Gaussian distribution. It is shown that α is a primary parameter for evaluating the global status of the pandemic in the sense that the pandemic will become extinguished as α → 0 for all countries. For this reason, and based on the data currently available, a study is made of the variations in α for 100 randomly selected countries. Finally, in the context of the Bio-dynamic Hypothesis, a parametric model is considered for simulating the three-dimensional structure of a spike protein which may be of value in the development of a vaccine.en_US
dc.identifier.citation2021J. M. Blackledge10.1007/978-981-16-2450-6_4Infosys Science Foundation Seriesen_US
dc.identifier.issn23644036
dc.identifier.uri10.1007/978-981-16-2450-6_4
dc.identifier.urihttp://hdl.handle.net/10566/7668
dc.language.isoenen_US
dc.subjectEinstein’s Evolution equationen_US
dc.subjectSelf-Affine random walk fieldsen_US
dc.subjectPandemic time series analysisen_US
dc.subjectBio-dynamics hypothesisen_US
dc.subjectFractal geometry of spike proteinsen_US
dc.titleOn the Evolution Equation for Modelling the Covid-19 Pandemicen_US
dc.typeArticleen_US

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