COVID-19: Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility

Abstract

The current global health emergency triggered by the pandemic COVID-19 is one of the greatest challenges we face in this generation. Computational simulations have played an important role to predict the development of the current pandemic. Such simulations enable early indications on the future projections of the pandemic and is useful to estimate the efficiency of control action in the battle against the SARS-CoV-2 virus. The SEIR model is a well-known method used in computational simulations of infectious viral diseases 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 with additional exit conditions in the form of death rates and resusceptibility, where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes; death, recovery, and recovery with a possibility of resusceptibility. The model also considers specific information such as ageing factor of the population, time delay on the development of the pandemic due to control action measures, as well as resusceptibility with temporal immune response. Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better on the current scenario and case data reported, such that the spread of the disease and the efficiency of the control action taken can be better understood. The model is verified using two case studies based on the real-world data in South Korea and Northern Ireland.

Beyond just “flattening the curve”: Optimal control of epidemics with purely non-pharmaceutical interventions

“…In the case of the currently spreading COVID-19 pandemic caused by the new SARS-CoV2 coronavirus [1,2], the fundamental concern of the mitigation measures is not to exceed the available number of intensive care unit (ICU) beds, in particular for respiratory support or extracorporeal membrane oxygenation, in order to prevent actually avoidable deaths [3]. Since the outbreak of the epidemic, a large number of simulation studies have been conducted using mathematical models to assess the efficacy of different NPIs and to estimate the corresponding demands on the health care system [4][5][6][7][8][9][10][11][12]. Moreover, mathematical models are employed to deduce important epidemiological parameters [13][14][15] and to evaluate the effect of particular measures from empirical data [16,17].…”

Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control

“…Nowadays, the dynamics of COVID-19 models have been growing interest in the research community and may mathematical models are designed for the better interest of people around the world, such as the model of eight classes based on susceptible, infected, diagnosed, ailing, recognized, threatened, healed and extinct (SIDARTHE) [6], five classes based on SEIAR represented with 5 number of ordinary differential equations [7], a new θ -SEIHRD model represented with nine classes [8], modified SEIRS model system with five classes [9], four class modified SIR model [10], SAIR system based COVID-19 model for complex networks [11]. Beside, these variety of COVID-19 model are introduced by the researchers [8,[12][13][14][15][16][17][18][19][20][21][22].…”

“…The said information is the motivational affinities to investigate in AI base numerical computing solver for the COVID-19 model. As per our literature survey no one yet implemented AI based computational procedure through Levenberg-Marquardt artificial neural networks (LMANNs) to solve initial value problems (IVBs) of nonlinear systems of ordinary differential equations (ODEs) represented COVID dynamics as given in (1)(2)(3)(4)(5)(6)(7)(8)(9). We present the design of intelligent computing paradigm through LMANNs for numerical treatment of Covid-19 based SEIPAHRF model for five different cities of China and Pakistan including Wuhan, Karachi, Lahore, Rawalpindi and Faisalabad.…”

On Confinement and Quarantine Concerns on an SEIAR Epidemic Model with Simulated Parameterizations for the COVID-19 Pandemic

“…Those above issues have been important in the last months with regard to the evolution of the COVID-19 pandemic around the world (See References [27,29,[37][38][39][40][41][42][43][44][45][46][47][48][49][50][51]). In particular, an "ad hoc" SEIR model parameterization related to the COVID-19 pandemic is investigated in Reference [37] including delayed re-susceptibility caused by the infection. Additionally, a kind of autoregressive model average model (ARMA), known as an ARIMA model for prediction of COVID-19, is presented and simulated in Reference [38] for the data of several countries.…”