The study, published in the Journal of the Royal Society Interface and led by researchers from the University of Cambridge, is based on time series models developed using classical statistical methods. The models produce nowcasts and forecasts of the daily number of new cases and deaths that have already proved successful in predicting new COVID-19 waves and spikes in Germany, Florida and several states in India.
The study is co-authored by Andrew Harvey and Paul Kattuman, whose time series model reflecting epidemic trajectories, known as the Harvey-Kattuman model, was introduced last year in a paper published in Harvard Data Science Review.
“The basic R rate quickly wanes in usefulness as soon as a pandemic begins,” said Kattuman, from Cambridge Judge Business School. “The basic R rate looks at the number of infections expected to result from a single infectious person in a completely susceptible population, and this changes as immunity builds up and measures such as social distancing are imposed.”
In later stages of a pandemic, the researchers conclude that use of the effective R rate which takes these factors into account is also not the best route: the focus should be not on contagiousness, but rather on the growth rate of new cases and deaths, examined alongside their predicted time path so a trajectory can be forecasted.
“These are the numbers that really help guide policymakers in making the crucial decisions that will hopefully save lives and prevent overcrowded hospitals as a pandemic plays out – which, as we have seen with COVID-19, can occur over months and even years,” said Kattuman. “The data generated through this time-series model has already proved accurate and effective in countries around the world.”
Image: Covid-19 vaccine
Credit: Image by torstensimon from Pixabay
Reproduced courtesy of the University of Cambridge