IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i12p6285-d572467.html
   My bibliography  Save this article

Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland

Author

Listed:
  • Jamie M. Madden

    (Centre for Veterinary Epidemiology and Risk Analysis (CVERA), School of Veterinary Medicine, University College Dublin, D04 W6F6 Dublin, Ireland)

  • Simon More

    (Centre for Veterinary Epidemiology and Risk Analysis (CVERA), School of Veterinary Medicine, University College Dublin, D04 W6F6 Dublin, Ireland)

  • Conor Teljeur

    (Health Technology Assessment Directorate, Health Information and Quality Authority, D07 E98Y Dublin, Ireland)

  • Justin Gleeson

    (National Institute for Regional and Spatial Analysis, National University of Ireland Maynooth, W23 F2H6 Kildare, Ireland)

  • Cathal Walsh

    (Health Research Institute and MACSI, University of Limerick, V94 T9PX Limerick, Ireland)

  • Guy McGrath

    (Centre for Veterinary Epidemiology and Risk Analysis (CVERA), School of Veterinary Medicine, University College Dublin, D04 W6F6 Dublin, Ireland)

Abstract

Like most countries worldwide, the coronavirus disease (COVID-19) has adversely affected Ireland. The aim of this study was to (i) investigate the spatio-temporal trend of COVID-19 incidence; (ii) describe mobility trends as measured by aggregated mobile phone records; and (iii) investigate the association between deprivation index, population density and COVID-19 cases while accounting for spatial and temporal correlation. Standardised incidence ratios of cases were calculated and mapped at a high spatial resolution (electoral division level) over time. Trends in the percentage change in mobility compared to a pre-COVID-19 period were plotted to investigate the impact of lockdown restrictions. We implemented a hierarchical Bayesian spatio-temporal model (Besag, York and Mollié (BYM)), commonly used for disease mapping, to investigate the association between covariates and the number of cases. There have been three distinct “waves” of COVID-19 cases in Ireland to date. Lockdown restrictions led to a substantial reduction in human movement, particularly during the 1st and 3rd wave. Despite adjustment for population density (incidence ratio (IR) = 1.985 (1.915–2.058)) and the average number of persons per room (IR = 10.411 (5.264–22.533)), we found an association between deprivation index and COVID-19 incidence (IR = 1.210 (CI: 1.077–1.357) for the most deprived quintile compared to the least deprived). There is a large range of spatial heterogeneity in COVID-19 cases in Ireland. The methods presented can be used to explore locally intensive surveillance with the possibility of localised lockdown measures to curb the transmission of infection, while keeping other, low-incidence areas open. Our results suggest that prioritising densely populated deprived areas (that are at increased risk of comorbidities) during vaccination rollout may capture people that are at risk of infection and, potentially, also those at increased risk of hospitalisation.

Suggested Citation

  • Jamie M. Madden & Simon More & Conor Teljeur & Justin Gleeson & Cathal Walsh & Guy McGrath, 2021. "Population Mobility Trends, Deprivation Index and the Spatio-Temporal Spread of Coronavirus Disease 2019 in Ireland," IJERPH, MDPI, vol. 18(12), pages 1-16, June.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6285-:d:572467
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/12/6285/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/12/6285/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicodemo, Catia & Barzin, Samira & Lasserson, Daniel S. & Moscone, Francesco & Redding, Stuart & Shaikh, Mujaheed & Cavalli, Nicolò, 2020. "Measuring Geographical Disparities in England at the Time of COVID-19: Results Using a Composite Indicator of Population Vulnerability," IZA Discussion Papers 13757, Institute of Labor Economics (IZA).
    2. CSAJI, Balazs Cs. & BROWET, Arnaud & TRAAG, V.A. & DELVENNE, Jean-Charles, 2013. "Exploring the mobility of mobile phone users," LIDAM Reprints CORE 2508, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Csáji, Balázs Cs. & Browet, Arnaud & Traag, V.A. & Delvenne, Jean-Charles & Huens, Etienne & Van Dooren, Paul & Smoreda, Zbigniew & Blondel, Vincent D., 2013. "Exploring the mobility of mobile phone users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1459-1473.
    4. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    5. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    6. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    7. Naeimehossadat Asmarian & Seyyed Mohammad Taghi Ayatollahi & Zahra Sharafi & Najaf Zare, 2019. "Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran," IJERPH, MDPI, vol. 16(22), pages 1-13, November.
    8. Ali Arab, 2015. "Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros," IJERPH, MDPI, vol. 12(9), pages 1-13, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Johnson, Blair T. & Sisti, Anthony & Bernstein, Mary & Chen, Kun & Hennessy, Emily A. & Acabchuk, Rebecca L. & Matos, Michaela, 2021. "Community-level factors and incidence of gun violence in the United States, 2014–2017," Social Science & Medicine, Elsevier, vol. 280(C).
    2. William Gonzalez Daza & Renata L. Muylaert & Thadeu Sobral-Souza & Victor Lemes Landeiro, 2023. "Malaria Risk Drivers in the Brazilian Amazon: Land Use—Land Cover Interactions and Biological Diversity," IJERPH, MDPI, vol. 20(15), pages 1-16, August.
    3. Getayeneh Antehunegn Tesema & Zemenu Tadesse Tessema & Stephane Heritier & Rob G. Stirling & Arul Earnest, 2023. "A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research," IJERPH, MDPI, vol. 20(7), pages 1-24, March.
    4. Chien-Chou Chen & Guo-Jun Lo & Ta-Chien Chan, 2022. "Spatial Analysis on Supply and Demand of Adult Surgical Masks in Taipei Metropolitan Areas in the Early Phase of the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(11), pages 1-12, May.
    5. Ropo E. Ogunsakin & Themba G. Ginindza, 2022. "Bayesian Spatial Modeling of Diabetes and Hypertension: Results from the South Africa General Household Survey," IJERPH, MDPI, vol. 19(15), pages 1-17, July.
    6. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    7. Mabel Morales-Otero & Vicente Núñez-Antón, 2021. "Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates," Mathematics, MDPI, vol. 9(3), pages 1-33, January.
    8. Naeimehossadat Asmarian & Seyyed Mohammad Taghi Ayatollahi & Zahra Sharafi & Najaf Zare, 2019. "Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran," IJERPH, MDPI, vol. 16(22), pages 1-13, November.
    9. Álvaro Briz‐Redón & Jorge Mateu & Francisco Montes, 2022. "Identifying crime generators and spatially overlapping high‐risk areas through a nonlinear model: A comparison between three cities of the Valencian region (Spain)," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(1), pages 97-120, February.
    10. Jinjie Chen & Joon Jin Song & James D. Stamey, 2022. "A Bayesian Hierarchical Spatial Model to Correct for Misreporting in Count Data: Application to State-Level COVID-19 Data in the United States," IJERPH, MDPI, vol. 19(6), pages 1-15, March.
    11. Wang, Craig & Furrer, Reinhard, 2021. "Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    12. Márcio Poletti Laurini, 2017. "A continuous spatio-temporal model for house prices in the USA," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 235-269, January.
    13. Cho, Daegon & Hwang, Youngdeok & Park, Jongwon, 2018. "More buzz, more vibes: Impact of social media on concert distribution," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 103-113.
    14. Andre Python & Andreas Bender & Marta Blangiardo & Janine B. Illian & Ying Lin & Baoli Liu & Tim C.D. Lucas & Siwei Tan & Yingying Wen & Davit Svanidze & Jianwei Yin, 2022. "A downscaling approach to compare COVID‐19 count data from databases aggregated at different spatial scales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 202-218, January.
    15. Shreosi Sanyal & Thierry Rochereau & Cara Nichole Maesano & Laure Com-Ruelle & Isabella Annesi-Maesano, 2018. "Long-Term Effect of Outdoor Air Pollution on Mortality and Morbidity: A 12-Year Follow-Up Study for Metropolitan France," IJERPH, MDPI, vol. 15(11), pages 1-8, November.
    16. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    17. Vanessa Santos-Sánchez & Juan Antonio Córdoba-Doña & Javier García-Pérez & Antonio Escolar-Pujolar & Lucia Pozzi & Rebeca Ramis, 2020. "Cancer Mortality and Deprivation in the Proximity of Polluting Industrial Facilities in an Industrial Region of Spain," IJERPH, MDPI, vol. 17(6), pages 1-15, March.
    18. Massimo Bilancia & Giacomo Demarinis, 2014. "Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 71-94, March.
    19. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    20. Jonathan Wakefield & Taylor Okonek & Jon Pedersen, 2020. "Small Area Estimation for Disease Prevalence Mapping," International Statistical Review, International Statistical Institute, vol. 88(2), pages 398-418, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:12:p:6285-:d:572467. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.