Using Panel Data Analysis to Evaluate How Individual Non-Pharmaceutical Interventions Affected Traffic in the US during the First Three Months of the COVID Pandemic

被引:2
作者
Motuba, Diomo [1 ]
Khan, Muhammad Asif [2 ]
Mirzazadeh, Babak [3 ]
Habib, Muhammad Faisal [4 ]
机构
[1] North Dakota State Univ, Upper Great Plains Transportat Inst, Dept Transportat & Logist, Fargo, ND 58108 USA
[2] Natl Univ Sci & Technol NUST, Sch Civil & Environm Engn, Sect H 12, Islamabad 44000, Pakistan
[3] KLD Engn PC, 1601 Vet Mem Highway, Suite 340, Islandia, NY 11749 USA
[4] North Dakota State Univ NDSU, Dept Transportat & Logist, Fargo, ND 58102 USA
来源
COVID | 2022年 / 2卷 / 09期
关键词
interventions policies; COVID-19; restrictions; vehicle trip reduction; heterogeneity; panel data analysis; SCHOOL CLOSURE; INFLUENZA; TRANSMISSION; CHILDREN;
D O I
10.3390/covid2090086
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
In response to the COVID-19 pandemic, restrictive non-pharmaceutical policy interventions (NPIs), with the goals of reducing interactions and travel for people in different households, were introduced. In the U.S., each state had jurisdiction over the NPI policy imposed, resulting in myriad policy decisions. The aggregate impacts of these decisions are known; however, the individual impacts are not fully understood. We disaggregated the NPIs imposed during the first three months of the epidemic (1 March and 7 June 2020) using panel data regression analysis. Vehicular travel reduction as a proxy for NPI impacts on traffic was regressed against stay-at-home orders, business closures, school closures, and gathering bans. The results show that school closures and full closures of non-essential businesses were correlated with the largest impacts in reducing vehicle trips compared to when they are not in place. Stay-at-home orders had about half the impact of school closures compared to when they were not in place. Gathering bans had the least impact. In the U.S., decisions that target businesses were the most effective in reducing vehicle traffic. There was heterogeneity in how people responded to these restrictions. This study can be used in epidemiology models and inform decision-makers on policies that work best.
引用
收藏
页码:1193 / 1206
页数:14
相关论文
共 42 条
[1]   Delaying the COVID-19 epidemic in Australia: evaluating the effectiveness of international travel bans [J].
Adekunle, Adeshina ;
Meehan, Michael ;
Rojas-Alvarez, Diana ;
Trauer, James ;
McBryde, Emma .
AUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH, 2020, 44 (04) :257-259
[2]   Analysing urban traflc volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease [J].
Aletta, Francesco ;
Brinchi, Stefano ;
Carrese, Stefano ;
Gemma, Andrea ;
Guattari, Claudia ;
Mannini, Livia ;
Patella, Sergio Maria .
NOISE MAPPING, 2020, 7 (01) :114-122
[3]  
[Anonymous], Pandemic Influenza Plan: 2017 Update
[4]   COVID-19 Outbreak in Colombia: An Analysis of Its Impacts on Transport Systems [J].
Arellana, Julian ;
Marquez, Luis ;
Cantillo, Victor .
JOURNAL OF ADVANCED TRANSPORTATION, 2020, 2020 :1DUMMMY
[5]  
Balloptpedia, Lawsuits about State Actions and Policies in Response to the Coronavirus (COVID-19) Pandemic
[6]   Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave [J].
Banholzer, Nicolas ;
van Weenen, Eva ;
Lison, Adrian ;
Cenedese, Alberto ;
Seeliger, Arne ;
Kratzwald, Bernhard ;
Tschernutter, Daniel ;
Salles, Joan Puig ;
Bottrighi, Pierluigi ;
Lehtinen, Sonja ;
Feuerriegel, Stefan ;
Vach, Werner .
PLOS ONE, 2021, 16 (06)
[7]  
Belsley D.A., 2004, REGRESSION DIAGNOSTI
[8]   Economic and social consequences of human mobility restrictions under COVID-19 [J].
Bonaccorsi, Giovanni ;
Pierri, Francesco ;
Cinelli, Matteo ;
Flori, Andrea ;
Galeazzi, Alessandro ;
Porcelli, Francesco ;
Schmidt, Ana Lucia ;
Valensise, Carlo Michele ;
Scala, Antonio ;
Quattrociocchi, Walter ;
Pammolli, Fabio .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (27) :15530-15535
[9]  
Chatterjee S, 2015, Regression Analysis by Example
[10]  
Chinazzi M, 2020, SCIENCE, V368, P395, DOI [10.1126/science.aba9757, 10.1101/2020.02.09.20021261]