Coronavirus ‘end date’ in UAE, Saudi Arabia, Qatar predicted by Singapore researchers
A research project by Singapore University of Technology and Design used data-based estimations to create models which show the coronavirus life-cycle in specific countries, and estimated “end dates” in specific countries, including the UAE, Saudi Arabia and Qatar.
The research uses the SIR (susceptible-infected-recovered) model which describes the spread of infectious diseases and data of coronavirus cases as of May 7.
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.
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 predicts that the coronavirus “end date” in the UAE will be on September 3, 2020.
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 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 behaviors.