IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i13p1538-d586541.html
   My bibliography  Save this article

A Review of Spatiotemporal Models for Count Data in R Packages. A Case Study of COVID-19 Data

Author

Listed:
  • Maria Victoria Ibañez

    (Department of Mathematics-IMAC, Universitat Jaume I, Avda. del Riu Sec s/n., 12071 Castelló de la Plana, Castellón, Spain
    All authors contributed equally to this work.)

  • Marina Martínez-Garcia

    (Department of Mathematics-IMAC, Universitat Jaume I, Avda. del Riu Sec s/n., 12071 Castelló de la Plana, Castellón, Spain
    All authors contributed equally to this work.)

  • Amelia Simó

    (Department of Mathematics-IMAC, Universitat Jaume I, Avda. del Riu Sec s/n., 12071 Castelló de la Plana, Castellón, Spain
    All authors contributed equally to this work.)

Abstract

Spatiotemporal models for count data are required in a wide range of scientific fields, and they have become particularly crucial today because of their ability to analyze COVID-19-related data. The main objective of this paper is to present a review describing the most important approaches, and we monitor their performance under the same dataset. For this review, we focus on the three R-packages that can be used for this purpose, and the different models assessed are representative of the two most widespread methodologies used to analyze spatiotemporal count data: the classical approach and the Bayesian point of view. A COVID-19-related case study is analyzed as an illustration of these different methodologies. Because of the current urgent need for monitoring and predicting data in the COVID-19 pandemic, this case study is, in itself, of particular importance and can be considered the secondary objective of this work. Satisfactory and promising results have been obtained in this second goal. With respect to the main objective, it has been seen that, although the three models provide similar results in our case study, their different properties and flexibility allow us to choose the model depending on the application at hand.

Suggested Citation

  • Maria Victoria Ibañez & Marina Martínez-Garcia & Amelia Simó, 2021. "A Review of Spatiotemporal Models for Count Data in R Packages. A Case Study of COVID-19 Data," Mathematics, MDPI, vol. 9(13), pages 1-23, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:13:p:1538-:d:586541
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/13/1538/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/13/1538/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    2. Bent Jørgensen & Célestin Kokonendji, 2016. "Discrete dispersion models and their Tweedie asymptotics," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(1), pages 43-78, January.
    3. Miguel A. Martinez-Beneito, 2013. "A general modelling framework for multivariate disease mapping," Biometrika, Biometrika Trust, vol. 100(3), pages 539-553.
    4. Bent Jørgensen & Sven Jesper Knudsen, 2004. "Parameter Orthogonality and Bias Adjustment for Estimating Functions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(1), pages 93-114, March.
    5. Cici Bauer & Jon Wakefield, 2018. "Stratified space–time infectious disease modelling, with an application to hand, foot and mouth disease in China," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1379-1398, November.
    6. Marcel Goic & Mirko S Bozanic-Leal & Magdalena Badal & Leonardo J Basso, 2021. "COVID-19: Short-term forecast of ICU beds in times of crisis," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-24, January.
    7. 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.
    8. Wei Wei & Leonhard Held, 2014. "Calibration tests for count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 787-805, December.
    9. Perone, G., 2020. "Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/18, HEDG, c/o Department of Economics, University of York.
    10. J. Paul Elhorst, 2014. "Dynamic Spatial Panels: Models, Methods and Inferences," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 95-119, Springer.
    11. Alastair Rushworth & Duncan Lee & Christophe Sarran, 2017. "An adaptive spatiotemporal smoothing model for estimating trends and step changes in disease risk," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 141-157, January.
    12. Wagner Hugo Bonat & Bent Jørgensen, 2016. "Multivariate covariance generalized linear models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 649-675, November.
    13. J. Besag & D. Higdon, 1999. "Bayesian analysis of agricultural field experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 691-746.
    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. W. H. Bonat & J. Olivero & M. Grande-Vega & M. A. Farfán & J. E. Fa, 2017. "Modelling the Covariance Structure in Marginal Multivariate Count Models: Hunting in Bioko Island," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 446-464, December.
    2. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    3. Lambert, Philippe, 2021. "Fast Bayesian inference using Laplace approximations in nonparametric double additive location-scale models with right- and interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    4. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    5. Ricardo Rasmussen Petterle & Wagner Hugo Bonat & Cassius Tadeu Scarpin, 2019. "Quasi-beta Longitudinal Regression Model Applied to Water Quality Index Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 346-368, June.
    6. Soltani, Ali & Roohani Qadikolaei, Mohsen, 2024. "Space-time analysis of accident frequency and the role of built environment in mitigation," Transport Policy, Elsevier, vol. 150(C), pages 189-205.
    7. Samantha Ofili & Lucy Thompson & Philip Wilson & Louise Marryat & Graham Connelly & Marion Henderson & Sarah J. E. Barry, 2022. "Mapping Geographic Trends in Early Childhood Social, Emotional, and Behavioural Difficulties in Glasgow: 2010–2017," IJERPH, MDPI, vol. 19(18), pages 1-14, September.
    8. Kokonendji, Célestin C. & Puig, Pedro, 2018. "Fisher dispersion index for multivariate count distributions: A review and a new proposal," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 180-193.
    9. Schmidt, Paul & Mühlau, Mark & Schmid, Volker, 2017. "Fitting large-scale structured additive regression models using Krylov subspace methods," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 59-75.
    10. Nikoline N. Knudsen & Jörg Schullehner & Birgitte Hansen & Lisbeth F. Jørgensen & Søren M. Kristiansen & Denitza D. Voutchkova & Thomas A. Gerds & Per K. Andersen & Kristine Bihrmann & Morten Grønbæk , 2017. "Lithium in Drinking Water and Incidence of Suicide: A Nationwide Individual-Level Cohort Study with 22 Years of Follow-Up," IJERPH, MDPI, vol. 14(6), pages 1-13, June.
    11. Leonardo Padilla & Bernado Lagos‐Álvarez & Jorge Mateu & Emilio Porcu, 2020. "Space‐time autoregressive estimation and prediction with missing data based on Kalman filtering," Environmetrics, John Wiley & Sons, Ltd., vol. 31(7), November.
    12. Tang, Yunfeng & Zhang, Xuan & Lu, Shibao & Taghizadeh-Hesary, Farhad, 2023. "Digital finance and air pollution in China: Evolution characteristics, impact mechanism and regional differences," Resources Policy, Elsevier, vol. 86(PA).
    13. Scott, Ryan P. & Scott, Tyler A., 2019. "Investing in collaboration for safety: Assessing grants to states for oil and gas distribution pipeline safety program enhancement," Energy Policy, Elsevier, vol. 124(C), pages 332-345.
    14. Zheng, Xinye & Li, Fanghua & Song, Shunfeng & Yu, Yihua, 2013. "Central government's infrastructure investment across Chinese regions: A dynamic spatial panel data approach," China Economic Review, Elsevier, vol. 27(C), pages 264-276.
    15. Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
    16. Aldo Carranza & Marcel Goic & Eduardo Lara & Marcelo Olivares & Gabriel Y. Weintraub & Julio Covarrubia & Cristian Escobedo & Natalia Jara & Leonardo J. Basso, 2022. "The Social Divide of Social Distancing: Shelter-in-Place Behavior in Santiago During the Covid-19 Pandemic," Management Science, INFORMS, vol. 68(3), pages 2016-2027, March.
    17. 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.
    18. Jonathan Berrisch & Florian Ziel, 2023. "Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices," Papers 2303.10019, arXiv.org, revised Feb 2024.
    19. Gardt Manuel & Broekel Tom & Gareis Philipp & Litmeyer Marie-Louise, 2018. "Einfluss von Windenergieanlagen auf die Entwicklung des Tourismus in Hessen," ZFW – Advances in Economic Geography, De Gruyter, vol. 62(1), pages 46-64, March.
    20. Wang, Xiong & Wang, Xiao & Ren, Xiaohang & Wen, Fenghua, 2022. "Can digital financial inclusion affect CO2 emissions of China at the prefecture level? Evidence from a spatial econometric approach," Energy Economics, Elsevier, vol. 109(C).

    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:jmathe:v:9:y:2021:i:13:p:1538-:d:586541. 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.