IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v90y2023ics0038012123002446.html
   My bibliography  Save this article

Linear and nonlinear effects explaining the risk of Covid-19 infection: an empirical analysis on real data from the USA

Author

Listed:
  • Giordano, Francesco
  • Milito, Sara
  • Parrella, Maria Lucia

Abstract

Using data from 3142 counties in the United States and a fully nonparametric variable selection approach for high-dimensional models, we identify predictor variables (among social, behavioral, economic, political, regulatory, demographic, and health characteristics) and discriminate against them between linear and nonlinear, depending on their effect on the risk of Severe Acute Respiratory Syndrome Coronavirus 2 infection. The data refer to the period from January to December 2020. We use a nonparametric and non-additive screening selection approach, the Derivative Empirical Likelihood Sure Independent Screening (DELSIS), in combination with a subsample technique. The results show that the relevant variables are different between counties with “large” and “small” populations. Furthermore, predictors such as mask wearing, age levels, ethnicity and poor health conditions are the main relevant variables for predicting the risk of infection, but with some differences over time.

Suggested Citation

  • Giordano, Francesco & Milito, Sara & Parrella, Maria Lucia, 2023. "Linear and nonlinear effects explaining the risk of Covid-19 infection: an empirical analysis on real data from the USA," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:soceps:v:90:y:2023:i:c:s0038012123002446
    DOI: 10.1016/j.seps.2023.101732
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012123002446
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2023.101732?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lian, Heng & Du, Pang & Li, YuanZhang & Liang, Hua, 2014. "Partially linear structure identification in generalized additive models with NP-dimensionality," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 197-208.
    2. Nicolai Meinshausen & Peter Bühlmann, 2010. "Stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 417-473, September.
    3. Baranowski, Rafal & Chen, Yining & Fryzlewicz, Piotr, 2020. "Ranking-based variable selection for high-dimensional data," LSE Research Online Documents on Economics 90233, London School of Economics and Political Science, LSE Library.
    4. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    5. Abolfazl Mollalo & Kiara M. Rivera & Behzad Vahedi, 2020. "Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    6. Yueying Wang & Myungjin Kim & Shan Yu & Xinyi Li & Guannan Wang & Li Wang, 2022. "Nonparametric estimation and inference for spatiotemporal epidemic models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(3), pages 683-705, July.
    7. Francesco Giordano & Sara Milito & Maria Lucia Parrella, 2021. "A Model-Free Screening Selection Approach by Local Derivative Estimation," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 243-250, Springer.
    8. F. Giordano & S. Milito & M. L. Parrella, 2022. "A nonparametric procedure for linear and nonlinear variable screening," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(4), pages 859-894, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Paul, Jomon A. & Wang, Xinfang & Bagchi, Aniruddha, 2024. "Lives or livelihoods: A configurational perspective of COVID-19 policies," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).

    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. Tan, Xin Lu, 2019. "Optimal estimation of slope vector in high-dimensional linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 179-204.
    2. Soyeon Kim & Veerabhadran Baladandayuthapani & J. Jack Lee, 2017. "Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 217-245, June.
    3. Latouche, Pierre & Mattei, Pierre-Alexandre & Bouveyron, Charles & Chiquet, Julien, 2016. "Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 177-190.
    4. Joel L. Horowitz, 2015. "Variable selection and estimation in high‐dimensional models," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 48(2), pages 389-407, May.
    5. Jessie Jeng, X., 2016. "Detecting weak signals in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 234-246.
    6. Joel L. Horowitz, 2015. "Variable selection and estimation in high-dimensional models," Canadian Journal of Economics, Canadian Economics Association, vol. 48(2), pages 389-407, May.
    7. Joel L. Horowitz, 2015. "Variable selection and estimation in high-dimensional models," CeMMAP working papers CWP35/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Gillard, Jonathan & Zhigljavsky, Anatoly, 2018. "Optimal estimation of direction in regression models with large number of parameters," Applied Mathematics and Computation, Elsevier, vol. 318(C), pages 281-289.
    9. Meng An & Haixiang Zhang, 2023. "High-Dimensional Mediation Analysis for Time-to-Event Outcomes with Additive Hazards Model," Mathematics, MDPI, vol. 11(24), pages 1-11, December.
    10. Tomohiro Ando & Ruey S. Tsay, 2009. "Model selection for generalized linear models with factor‐augmented predictors," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 207-235, May.
    11. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    12. Wei, Jie & Chen, Hui, 2020. "Determining the number of factors in approximate factor models by twice K-fold cross validation," Economics Letters, Elsevier, vol. 191(C).
    13. Jing Zhang & Qihua Wang & Xuan Wang, 2022. "Surrogate-variable-based model-free feature screening for survival data under the general censoring mechanism," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 379-397, April.
    14. Sauvenier, Mathieu & Van Bellegem, Sébastien, 2023. "Direction Identification and Minimax Estimation by Generalized Eigenvalue Problem in High Dimensional Sparse Regression," LIDAM Discussion Papers CORE 2023005, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    15. Jie-Huei Wang & Cheng-Yu Liu & You-Ruei Min & Zih-Han Wu & Po-Lin Hou, 2024. "Cancer Diagnosis by Gene-Environment Interactions via Combination of SMOTE-Tomek and Overlapped Group Screening Approaches with Application to Imbalanced TCGA Clinical and Genomic Data," Mathematics, MDPI, vol. 12(14), pages 1-24, July.
    16. Zhaoyu Xing & Yang Wan & Juan Wen & Wei Zhong, 2024. "GOLFS: feature selection via combining both global and local information for high dimensional clustering," Computational Statistics, Springer, vol. 39(5), pages 2651-2675, July.
    17. Ahmed Ismaïl & Hartikainen Anna-Liisa & Järvelin Marjo-Riitta & Richardson Sylvia, 2011. "False Discovery Rate Estimation for Stability Selection: Application to Genome-Wide Association Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-20, November.
    18. Emre Demirkaya & Yang Feng & Pallavi Basu & Jinchi Lv, 2022. "Large-scale model selection in misspecified generalized linear models [Information theory and an extension of the maximum likelihood principle]," Biometrika, Biometrika Trust, vol. 109(1), pages 123-136.
    19. Shan Luo & Zehua Chen, 2014. "Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1229-1240, September.
    20. Shi Chen & Wolfgang Karl Hardle & Brenda L'opez Cabrera, 2020. "Regularization Approach for Network Modeling of German Power Derivative Market," Papers 2009.09739, arXiv.org.

    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:eee:soceps:v:90:y:2023:i:c:s0038012123002446. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    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.