The onset of the COVID-19 pandemic has seen massive disruption to long-established patterns of population distribution and movement, particularly impacting travel to education and employment, including student populations moving between term-time and home addresses. At the same time, there has been intense interest in monitoring local disease incidence rates and public health responses, which demand accurate denominator counts. New data such as Google’s Community Mobility Reports1 provide powerful insights on the broad patterns of change, but the diversity of response strategies and ongoing evolution of the pandemic make it necessary to be able to repeatedly reconstruct existing population denominator models to reflect new conditions.
The Population 24/7 modelling framework2 has previously been used in the UK and elsewhere to develop population distributions on a fine spatial grid for specific types and times of day. The approach integrates official statistics and administrative data to inform volume-preserving redistribution of population totals over space, based on expected temporal activity profiles. Early in the pandemic, huge disruption occurred to established patterns, resulting in interest from many agencies in more accurately depicting changing population distributions at high spatial resolution. This paper demonstrates how the Population 24/7 framework has been readily adapted using a combination of official mid-year population estimates and Google mobility data to reflect, for example, decreases in workplace and school attendance and particularly the relocation of around 2 million university students. The approach has potential to support rapid recalculation of changing population distributions under alternative pandemic scenarios.
1. https://www.google.com/covid19/mobility/
2. https://www.tandfonline.com/doi/full/10.1080/00045608.2015.1022089