Spatio-temporal small area surveillance of the COVID-19 pandemic

被引:3
作者
Martinez-Beneito, Miguel A. [1 ,2 ]
Mateu, Jorge [3 ]
Botella-Rocamora, Paloma [2 ,4 ]
机构
[1] Univ Valencia, Dept Stat & Operat Res, Burjassot, Valencia, Spain
[2] UV FISABIO, Unitat Mixta recerca metodes estadist dades Biome, Valencia, Spain
[3] Univ Jaume I Castellon, Dept Math, Castellon de La Plana, Spain
[4] Conselleria Sanitat & Universal Salut Publ, Subdirecc Gen Epidemiol Vigilancia Salud & Sanidad, Valencia, Spain
关键词
COVID-19; Disease mapping; Instantaneous reproduction number; Spatio-temporal modelling;
D O I
10.1016/j.spasta.2021.100551
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The emergence of COVID-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with small units of analysis, are a priority in this context. These models provide geographically detailed and temporally updated overviews of the current state of the pandemic, making public health interventions more effective. These models also allow estimating epidemiological indicators highly demanded for COVID-19 surveillance, such as the instantaneous reproduction number R-t, even for small areas. In this paper, we propose a new spatio-temporal spline model particularly suited for COVID-19 surveillance, which allows estimating and monitoring R-t for small areas. We illustrate our proposal on the study of the disease pandemic in two Spanish regions. As a result, we show how tourism flows have shaped the spatial distribution of the disease in these regions. In these case studies, we also develop new epidemiological tools to be used by regional public health services for small area surveillance. (c) 2021 The Author(s). Published by Elsevier B.V.
引用
收藏
页数:13
相关论文
共 23 条
  • [1] [Anonymous], Centro Nacional de Epidemiologia
  • [2] [Anonymous], 2011, WILEY SERIES PROBABI
  • [3] [Anonymous], 2003, Small area estimation, DOI DOI 10.1002/0471722189
  • [4] BAYESIAN IMAGE-RESTORATION, WITH 2 APPLICATIONS IN SPATIAL STATISTICS
    BESAG, J
    YORK, J
    MOLLIE, A
    [J]. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 1991, 43 (01) : 1 - 20
  • [5] Consejo Interterritorial Sistema Nacional de Salud, COORDINATED RESPONSE
  • [6] Cori A, 2020, EPIESTIM ESTIMATE TI
  • [7] A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics
    Cori, Anne
    Ferguson, Neil M.
    Fraser, Christophe
    Cauchemez, Simon
    [J]. AMERICAN JOURNAL OF EPIDEMIOLOGY, 2013, 178 (09) : 1505 - 1512
  • [8] Transmission dynamics of the COVID-19 outbreak and effectiveness of government interventions: A data-driven analysis
    Fang, Yaqing
    Nie, Yiting
    Penny, Marshare
    [J]. JOURNAL OF MEDICAL VIROLOGY, 2020, 92 (06) : 645 - 659
  • [9] Gelman A., 2013, Bayesian data analysis, V3rd, DOI DOI 10.1201/B16018
  • [10] Lessons learnt from easing COVID-19 restrictions: an analysis of countries and regions in Asia Pacific and Europe
    Han, Emeline
    Tan, Melisa Mei Jin
    Turk, Eva
    Sridhar, Devi
    Leung, Gabriel M.
    Shibuya, Kenji
    Asgari, Nima
    Oh, Juhwan
    Garcia-Basteiro, Alberto L.
    Hanefeld, Johanna
    Cook, Alex R.
    Hsu, Li Yang
    Teo, Yik Ying
    Heymann, David
    Clark, Helen
    McKee, Martin
    Legido-Quigley, Helena
    [J]. LANCET, 2020, 396 (10261) : 1525 - 1534