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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
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