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
  • [11] Assessment of basic reproduction number (R0), spatial and temporal epidemiological determinants, and genetic characterization of SARS-CoV-2 in Bangladesh
    Islam, Ariful
    Abu Sayeed, Md
    Rahman, Md Kaisar
    Zamil, Shafayat
    Abedin, Josefina
    Saha, Otun
    Hassan, Mohammad Mahmudul
    [J]. INFECTION GENETICS AND EVOLUTION, 2021, 92
  • [12] Leroux B.G., 1999, Statistical models in epidemiology, the environment and clinical trials
  • [13] An autoregressive approach to spatio-temporal disease mapping
    Martinez-Beneito, M. A.
    Lopez-Quilez, A.
    Botella-Rocamora, P.
    [J]. STATISTICS IN MEDICINE, 2008, 27 (15) : 2874 - 2889
  • [14] Martinez-Beneito Miguel A., 2019, Disease Mapping: From Foundations to Multidimensional Modeling
  • [15] Milligan G., 2014, VACCINOLOGY ESSENTIA
  • [16] Nishiura H., 2009, Mathematical and Statistical Estimation Approaches in Epidemiology, P103, DOI 10.1007/978-90-481-2313-1
  • [17] Serial interval of novel coronavirus (COVID-19) infections
    Nishiura, Hiroshi
    Linton, Natalie M.
    Akhmetzhanov, Andrei R.
    [J]. INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES, 2020, 93 : 284 - 286
  • [18] Spatial dynamics and the basic reproduction number of the 1991-1997 Cholera epidemic in Peru
    Smirnova, Alexandra
    Sterrett, Natalie
    Mujica, Oscar J.
    Munayco, Cesar
    Suarez, Luis
    Viboud, Cecile
    Chowell, Gerardo
    [J]. PLOS NEGLECTED TROPICAL DISEASES, 2020, 14 (07): : 1 - 22
  • [19] COVID19-world: a shiny application to perform comprehensive country-specific data visualization for SARS-CoV-2 epidemic
    Tebe, Cristian
    Valls, Joan
    Satorra, Pau
    Tobias, Aurelio
    [J]. BMC MEDICAL RESEARCH METHODOLOGY, 2020, 20 (01)
  • [20] STANOVA: a smoothed-ANOVA-based model for spatio-temporal disease mapping
    Torres-Aviles, Francisco
    Martinez-Beneito, Miguel A.
    [J]. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2015, 29 (01) : 131 - 141