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The current global health emergency triggered by the pandemic COVID-19 is oneof the greatest challenges mankind face in this generation. Computationalsimulations have played an important role to predict the development of thecurrent pandemic. Such simulations enable early indications on the futureprojections of the pandemic and is useful to estimate the efficiency of controlaction in the battle against the SARS-CoV-2 virus. The SEIR model is awell-known method used in computational simulations of infectious viraldiseases and it has been widely used to model other epidemics such as Ebola,SARS, MERS, and influenza A. This paper presents a modified SEIRS model withadditional exit conditions in the form of death rates and resusceptibility,where we can tune the exit conditions in the model to extend prediction on thecurrent projections of the pandemic into three possible outcomes; death,recovery, and recovery with a possibility of resusceptibility. The model alsoconsiders specific information such as ageing factor of the population, timedelay on the development of the pandemic due to control action measures, aswell as resusceptibility with temporal immune response. Owing to hugevariations in clinical symptoms exhibited by COVID-19, the proposed model aimsto reflect better on the current scenario and case data reported, such that thespread of the disease and the efficiency of the control action taken can bebetter understood. The model is verified using two case studies forverification and prediction studies, based on the real-world data in SouthKorea and Northern Ireland, respectively.

physica d nonlinear phenomena

COVID-19: Development of a Robust Mathematical Model and Simulation  Package with Consideration for Ageing Population and Time Delay for Control  Action and Resusceptibility