COVID 19: Scientists data tried to answer a question on everyone's mind: When will the coronavirus pandemic end?
To predict the answer they used a mathematical model also known as SIR from the initials susceptible, infected, and recovered. The mathematical model is constructed to calculate the spread and remediation of diseases.
Researchers from the Singapore University of Technology and Design (SUTD) fed the model with data that feature from around the world. Confirmed infections, tests performed and deaths recorded, to estimate the life cycle of COVID 19.
Globally, then, their system predicts that pandemic will end in December (See results for each country separately – Greece not included). However, the expiration date varies greatly between states, with for example AI stating that it will expire in June on Australia or October in Italy.
The researchers say that both the model and the data can not accurately capture the complexity and dynamic nature of the disease. However, they still believe that predictions can support precautionary planning for when the lockdown may end.
History shows that the development of pandemics it is not completely accidental. They follow a life cycle that begins with the outbreak of the disease, accelerates to the peak of infections and then slows down until it disappears. All these stages are influenced by the government policies and individual actions of each country.
This convinced the SUTD team that data science could predict life cycle of the corona. But first, they had to collect a lot of data.
This information was found in a dataset for COVID 19 that was gathered by Our World in Data, a research organization investigating the world's biggest problems.
The dataset consists of confirmed cases and deaths collected by the European Center for Disease Prevention and Control, as well as statistical data examinations published in official reports.
The team then fed their data to their mathematical model so that they could predict how many people would be infected in the future. The numbers appear in bar graphs that eventually form a bell curve, indicating the start, acceleration, turning point and estimated end date of the pandemic.
Researchers stress that their predictions need to be treated with caution. Excessive optimism could prematurely loosen individual discipline and government restrictions, extending the pandemic.
They warn that the accuracy of their model depends on the quality of the data, which is often unreliable and reported differently around the world.
Also, the model cannot predict the results of political decisions. For example, Singapore's move to extend the blockade could curve the curve sooner than expected, while the early easing of social distancing in the US could delay the end of the pandemic.
