Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Mathematical Modelling in Prediction of Novel CoronaVirus (COVID-19) Transmission Dynamics

Version 1 : Received: 29 September 2020 / Approved: 30 September 2020 / Online: 30 September 2020 (15:08:34 CEST)

How to cite: Uddin, M.S.; Nasseef, M.T.; Mahmud, M.; AlArjani, A. Mathematical Modelling in Prediction of Novel CoronaVirus (COVID-19) Transmission Dynamics. Preprints 2020, 2020090757 (doi: 10.20944/preprints202009.0757.v1). Uddin, M.S.; Nasseef, M.T.; Mahmud, M.; AlArjani, A. Mathematical Modelling in Prediction of Novel CoronaVirus (COVID-19) Transmission Dynamics. Preprints 2020, 2020090757 (doi: 10.20944/preprints202009.0757.v1).

Abstract

Human civilizations are under enormous threats due to the outbreak of novel coronavirus (COVID-19) originated from Wuhan, China. The asymptomatic carriers are the potential spreads of this novel virus. Since, guaranteed antiviral treatments have not been available in the market so far, it is really challenging to fight against this contagious disease. To save the living mankind, it is urgent to know more about how the virus transmits itself from one to another quite rapidly and how we can predict future infections. Scientists and Researchers are working hard in investigating to understand its high infection rate and transmission process. One possible way to know is to use our existing COVID-19 infection data and prepare a useful model to predict the future trend. Mathematical modelling is very useful to understand the basic principle of COVID-19 transmission and provide necessary guidelines for future prediction. Here, we have reviewed 9 distinct commonly used models based on Mathematical implementations for COVID-19 transmission and dig into the deep head to head comparison of each model. Finally, we have discussed interesting key behaviour of each model, relevant upcoming important issues, challenges and future directions.

Subject Areas

Coronavirus; ODE; SIR; SEIR; transmission dynamic; prediction; SARS-CoV-2

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