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

IF 6.2 2区经济学 Q1 ECONOMICS

Socio-economic Planning Sciences Pub Date : 2023-10-01 DOI:10.1016/j.seps.2023.101732

Francesco Giordano, Sara Milito, Maria Lucia Parrella

引用次数: 0

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.

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解释新冠肺炎感染风险的线性和非线性效应——基于美国真实数据的实证分析

使用来自美国3142个县的数据和高维模型的完全非参数变量选择方法，我们确定了预测变量（在社会、行为、经济、政治、监管、人口统计和健康特征中），并在线性和非线性之间进行区分，这取决于它们对严重急性呼吸综合征冠状病毒2型感染风险的影响。数据指的是2020年1月至12月期间的数据。我们使用非参数和非加性筛选选择方法，即导数经验似然可靠独立筛选（DELSIS），并结合子样本技术。结果表明，人口“大”和“小”的县之间的相关变量不同。此外，戴口罩、年龄水平、种族和不良健康状况等预测因素是预测感染风险的主要相关变量，但随着时间的推移会有一些差异。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Socio-economic Planning Sciences OPERATIONS RESEARCH & MANAGEMENT SCIENCE-

CiteScore

9.40

自引率

13.10%

发文量

294

审稿时长

58 days

期刊介绍： Studies directed toward the more effective utilization of existing resources, e.g. mathematical programming models of health care delivery systems with relevance to more effective program design; systems analysis of fire outbreaks and its relevance to the location of fire stations; statistical analysis of the efficiency of a developing country economy or industry. Studies relating to the interaction of various segments of society and technology, e.g. the effects of government health policies on the utilization and design of hospital facilities; the relationship between housing density and the demands on public transportation or other service facilities: patterns and implications of urban development and air or water pollution. Studies devoted to the anticipations of and response to future needs for social, health and other human services, e.g. the relationship between industrial growth and the development of educational resources in affected areas; investigation of future demands for material and child health resources in a developing country; design of effective recycling in an urban setting.