Literature DB >> 33686125

Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England.

B Sartorius1,2,3, A B Lawson4, R L Pullan5.   

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.

Entities:  

Year:  2021        PMID: 33686125     DOI: 10.1038/s41598-021-83780-2

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  10 in total

1.  The local burden of disease during the first wave of the COVID-19 epidemic in England: estimation using different data sources from changing surveillance practices.

Authors:  Emily S Nightingale; Sam Abbott; Timothy W Russell; Rachel Lowe; Graham F Medley; Oliver J Brady
Journal:  BMC Public Health       Date:  2022-04-11       Impact factor: 4.135

2.  Bayesian spatial modeling of COVID-19 case-fatality rate inequalities.

Authors:  Gina Polo; Diego Soler-Tovar; Luis Carlos Villamil Jimenez; Efraín Benavides-Ortiz; Carlos Mera Acosta
Journal:  Spat Spatiotemporal Epidemiol       Date:  2022-03-25

3.  Discovering spatiotemporal patterns of COVID-19 pandemic in South Korea.

Authors:  Sungchan Kim; Minseok Kim; Sunmi Lee; Young Ju Lee
Journal:  Sci Rep       Date:  2021-12-28       Impact factor: 4.379

4.  Deep recurrent reinforced learning model to compare the efficacy of targeted local versus national measures on the spread of COVID-19 in the UK.

Authors:  Tim Dong; Umberto Benedetto; Shubhra Sinha; Daniel Fudulu; Arnaldo Dimagli; Jeremy Chan; Massimo Caputo; Gianni Angelini
Journal:  BMJ Open       Date:  2022-02-21       Impact factor: 2.692

5.  Evaluating COVID-19 control measures in mass gathering events with vaccine inequalities.

Authors:  Ali M Al-Shaery; Bilal Hejase; Abdessamad Tridane; Norah S Farooqi; Hamad Al Jassmi
Journal:  Sci Rep       Date:  2022-03-07       Impact factor: 4.379

6.  Analysis on the characteristics of spatio-temporal evolution and aggregation trend of early COVID-19 in mainland China.

Authors:  Shengxian Bi; Siyu Bie; Xijian Hu; Huiguo Zhang
Journal:  Sci Rep       Date:  2022-03-14       Impact factor: 4.379

Review 7.  Dynamics of SARS-CoV-2 spreading under the influence of environmental factors and strategies to tackle the pandemic: A systematic review.

Authors:  Zunaira Asif; Zhi Chen; Saverio Stranges; Xin Zhao; Rehan Sadiq; Francisco Olea-Popelka; Changhui Peng; Fariborz Haghighat; Tong Yu
Journal:  Sustain Cities Soc       Date:  2022-03-16       Impact factor: 10.696

8.  A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates.

Authors:  Peter Congdon
Journal:  J Geogr Syst       Date:  2022-04-26

9.  Exploring the effect of PM2.5 and temperature on COVID-19 transmission in Seoul, South Korea.

Authors:  Youngbin Lym; Ki-Jung Kim
Journal:  Environ Res       Date:  2021-07-31       Impact factor: 6.498

10.  The Geographical Distribution and Influencing Factors of COVID-19 in China.

Authors:  Weiwei Li; Ping Zhang; Kaixu Zhao; Sidong Zhao
Journal:  Trop Med Infect Dis       Date:  2022-03-06
  10 in total

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