Real-time COVID-19 forecasting: challenges and opportunities of model performance and translation
Kristen Nixon, Sonia Jindal, Felix Parker, Maximilian Marshall, Nicholas G Reich, Kimia Ghobadi, Elizabeth C. Lee, Shaun Truelove, Lauren Gardner
Abstract
The COVID-19 pandemic brought mathematical modelling into the spotlight, as scientists rushed to use data to understand transmission patterns and disease severity, and to anticipate future epidemic outcomes. However, the use of COVID-19 modelling has been criticised, in part because of a few particularly erroneous projections at the start of the pandemic.1 More than 2 years into the pandemic, models continue to face serious obstacles as tools for informing outbreak response.1 Population-level health outcomes are difficult to predict accurately, especially cases and hospitalisations,2 as discussed in the International Institute of Forecasters blog.