The evolution and spread of a pandemic is not completely random. All pandemics, COVID-19 included, follow a standard life cycle that consists of set phases. Starting with the initial outbreak, pandemics can be tracked through their acceleration phase, inflection point, deacceleration phase and ultimate ending. These phases can vary by pandemic and by country, as government and policy changes heavily influence how the disease evolves, yet they can provide a basis for an estimation of how the pandemic will progress.
Now, by utilizing the standard life cycle of a pandemic, researchers from Singapore University of Technology and Design (SUTD) have created a SIR (susceptible-infected-recovered) based-mathematical model that predicts future infections of COVID-19 based on data from current confirmed cases and deaths.
The “predictive-monitoring” model is updated daily with new data taken from the Our World in Data COVID-19 dataset. This dataset contains all of the data regarding confirmed cases and deaths that have been collected by the European Centre for Disease Prevention and Control (Solna, Sweden).
Inputted data is visualized in a bar chart, and an overlying bell-shaped curve displays the predicted trajectory of the disease, allowing users to easily identify the main phases of the pandemic, including the inflection point – or peak of the curve – and the acceleration and deacceleration phases.
As of 30 April 2020, the model predicted a 100% end to the pandemic on the worldwide scale of around 4 December. This differs for specific countries, with Singapore predicted to be 100% free of the virus around 28 June, the UK to be 100% free around 27 August and the USA to be 100% free around 20 September.
The predictions are open to change, and the dates provided are far from definite. The SUTD team stress that the model and data are “inaccurate to the complex, evolving and heterogeneous realities of different countries” and that “predictions are uncertain by nature.” Any predictions should be read in combination with the current events occurring in the real world and the governmental policy changes that are influencing the spread of the virus. The strengthening of restrictions in Singapore in April may have bended the curve earlier than initially predicted, and the relaxation of social distancing rules in Germany or protests against lockdown in the USA may delay the eventual end point as infection rates begin to build once again.
In a report written to accompany the site, lab leader Jianxi Luo warns against the dangers of over-optimism based on the predicted dates, as any relaxation of social distancing rules could lead to a turnaround of the infection and a prolonging of the pandemic.
“Although prediction based on science and data is aimed to be objective, it is uncertain by nature. One thing that is certain is that the model, data and prediction are inaccurate and insufficient to fully represent the complex, evolving, and heterogeneous realities of our world,” wrote Luo.
By creating the site, the team hope to reduce the anxiety felt by many about the uncertainty of the future of the pandemic, putting an end in sight and to make people more “future-informed”.