Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England
Open Access
- 8 March 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Scientific Reports
- Vol. 11 (1), 1-11
- https://doi.org/10.1038/s41598-021-83780-2
Abstract
COVID-19 caseloads in England have passed through a first peak, and at the time of this analysis appeared to be gradually increasing, potentially signalling the emergence of a second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths at small-area resolution, identify localised areas in space–time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at high spatial resolution in coming weeks. We applied a Bayesian hierarchical space–time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England [Middle Layer Super Output Area (MSOA), 6791 units] and by week (using observed data from week 5 to 34 of 2020), including key determinants, the modelled transmission dynamics and spatial–temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA. Reductions in population mobility during the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates. While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have significantly contributed to the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced.Keywords
This publication has 37 references indexed in Scilit:
- A shared neighbor conditional autoregressive model for small area spatial dataEnvironmetrics, 2015
- Evaluation of the T-stress for multiple cracks in an elastic half-plane using singular integral equation and Green’s function methodApplied Mathematics and Computation, 2014
- Bayesian hierarchical modeling of the dynamics of spatio-temporal influenza season outbreaksSpatial and Spatio-temporal Epidemiology, 2010
- The Effects of Weather and Climate on the Seasonality of Influenza: What We Know and What We Need to KnowGeography Compass, 2010
- Spatial dynamics of the 1918 influenza pandemic in England, Wales and the United StatesJournal of The Royal Society Interface, 2010
- The effect of public health measures on the 1918 influenza pandemic in U.S. citiesProceedings of the National Academy of Sciences of the United States of America, 2007
- A comparison of Bayesian spatial models for disease mappingStatistical Methods in Medical Research, 2005
- Local Spatial Autocorrelation Statistics: Distributional Issues and an ApplicationGeographical Analysis, 1995
- Inference from Iterative Simulation Using Multiple SequencesStatistical Science, 1992
- Bayesian image restoration, with two applications in spatial statisticsAnnals of the Institute of Statistical Mathematics, 1991