Abstract
This paper presents a new modified SIR model which incorporates appropriate delay parameters leading to a more precise prediction of COVID-19 real time data. The efficacy of the newly developed SIR model is proven by comparing its predictions to real data obtained from four counties namely Germany, Italy, Kuwait, and Oman. Two included delay periods for incubation and recovery within the SIR model produce a sensible and more accurate representation of the real time data. In the absence of the two-delay period () the dynamical behavior of the model will not correspond to today’s picture and lag the detection of the epidemic peak. The reproductive number R0 is defined for the model for values of recovery time delay of the infective case. The effect of recovery time may produce second wave, and/or an oscillation which could destabilize the behavior of the system and a periodic oscillation can arise due to Hopf bifurcation phenomenon.
Highlights
The announcement of the global pandemic outbreak of coronavirus COVID-19 by the world health organization has become one of human’s most concern due to its enormous infectious diseases, both in terms of medical, and economics
Simulations for dynamical system the classical SIR model (1 - 3) and the proposed time delayed SIR model (4 - 6) are compared to real data collected by the official site of World Health Organization (WHO) [19]
The delay periods correspond to the duration of the incubational and recovery periods as it appears in COVID-19
Summary
The announcement of the global pandemic outbreak of coronavirus COVID-19 by the world health organization has become one of human’s most concern due to its enormous infectious diseases, both in terms of medical, and economics. Mathematical modelling is an essential tool to understand the mechanism of spread of a disease such as COVID-19 in the human population These models generate insights into the transmission dynamics of infectious diseases and assist health officials and policymakers to control its extensive spread. The benefit of accurately estimating the recovery/infectious rates is to predict a possible slowdown or growth of the infection numbers and allow public health policymakers to determine which containment measures are more effective decisions to take by the Government in combating the spread of the COVID-19 pandemic
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