SEIAQRDT model for the spread of novel coronavirus (COVID-19): A case study in India
Date
2020-11
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Abstract
COVID-19 is a global pandemic declared by WHO. This pandemic requires the execution of planned control strate
gies, incorporating quarantine, self-isolation, and tracing of asymptomatic cases. Mathematical modeling is one of the
prominent techniques for predicting and controlling the spread of COVID-19. The predictions of earlier proposed
epidemiological models (e.g. SIR, SEIR, SIRD, SEIRD, etc.) are not much accurate due to lack of consideration for
transmission of the epidemic during the latent period. Moreover, it is important to classify infected individuals to
control this pandemic. Therefore, a new mathematical model is proposed to incorporate infected individuals based on
whether they have symptoms or not. This model forecasts the number of cases more accurately, which may help in
better planning of control strategies. The model consists of eight compartments: susceptible (S), exposed (E), infected
(I), asymptomatic (A), quarantined (Q), recovered (R), deaths (D), and insusceptible (T), accumulatively named as
SEIAQRDT. This model is employed to predict the pandemic results for India and its majorly affected states. The
estimated number of cases using the SEIAQRDT model is compared with SIRD, SEIR, and LSTM models. The
relative error square analysis is used to verify the accuracy of the proposed model. The simulation is done on real
datasets and results show the effectiveness of the proposed approach. These results may help the government and
individuals to make the planning in this pandemic situation.