Riyadh at night. The SIR model uses three differential equations to describe the dynamic flow of people. Image Credit: Agency

Dubai: A research project using data-driven estimates to create models that show the coronavirus life-cycle in specific countries — has estimated “end dates” in specific countries, including Saudi Arabia and Qatar, Al Arabiya reported on Monday.

The research, done by the Singapore University of Technology and Design, uses the SIR (susceptible-infected-recovered) model, which describes the spread of infectious diseases and data of coronavirus cases as of May 7.

In Saudi Arabia, the research predicts an “end date” of September 10, 2020.

As for Qatar, the project predicts the coronavirus outbreak will end on September 14, 2020.

The SIR model uses three differential equations to describe the dynamic flow of people between three categories: ‘S’ for the number of people ‘susceptible’ to infection, ‘I’ for the number of infectious people, and ‘R’ for the number of removed people (either recovered or died) in the population.

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‘The SIR model incorporates two main parameters, beta and gamma. Gamma is the number of days a person is contagious and is a property of the virus. Beta is the average number of people infected due to coming in contact with a previously infected person and is related not only to the interaction patterns of people in a society (which social distancing can influence) but also the infection process properties of the virus.


The model shows a bell-shape curve where the left most end of the curve’s tail represents the first confirmed case of coronavirus in a country, the right most end of the curve’s tail represents the last predicted case of infection, the inflection point or the peak in the bell-shape curve represents the highest number of cases after which the rate of infection begins to slow down, and the area under the entire curve which represents the total predicted number of people who will have contracted the virus.

The research paper stressed that the predictions are uncertain and subject to change depending on real-world developments such as government policies, testing protocols and human behaviours.