The current global health emergency triggered by the COVID-19 pandemic is one of the greatest challenges mankind 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 . It has been widely used to model other epidemics such as Ebola, MERS (Middle East Respiratory Syndrome) , and influenza . Ageneral SEIR model represents an epidemic using four compartments in cascade; susceptible (S), exposed (E), infected (I), and recovery/removed (R), where the model assumes that the epidemic exits with the infected population either recovered or removed due to immunity to the virus. In this work, we present a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibiliy, where we tuned the exit conditions in the model to extend predictions on the projection of the current pandemic into three  outcomes; death, recovery, and recovery with a possibility of resusceptibility. Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better to the current scenario and case data reported. 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 resusceptiblity with temporary immune response.  The model is verified with a case study using the real-world data in South Korea  .
The current global health emergency triggered by the COVID-19 pandemic is one of the greatest challenges mankind 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 resusceptibiliy, 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 resusceptiblity with temporary 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 for verification and prediction studies, based on the real-world data in South Korea and Northern Ireland, respectively .
R
. It
and it
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MERS (Middle East Respiratory Syndrome)
SARS, MERS
R
. Ageneral SEIR model represents an epidemic using four compartments in cascade; susceptible (S), exposed (E), infected (I), and recovery/removed (R), where the model assumes that the epidemic exits with the infected population either recovered or removed due to immunity to the virus. In this work, we present
A. This paper presents
R
tuned
can tune
R
predictions on the projection of the current
prediction on the current projections of the
A
possible
D
Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better to the current scenario and case data reported.
A
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.
R
with a case study using
using two case studies for verification and prediction studies, based on
A
and Northern Ireland, respectively