Impact of Different Transportation Modes on the Transmission of COVID-19: Correlation and Strategies from a Case Study in Wuhan, China

Danwen Bao, Liping Yin (), Shijia Tian, Jialin Lv, Yanjun Wang, Jian Wang and Chaohao Liao
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Danwen Bao: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Liping Yin: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Shijia Tian: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Jialin Lv: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Yanjun Wang: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Jian Wang: School of Transportation, Southeast University, Nanjing 211189, China
Chaohao Liao: Air Traffic Management Bureau of Central South of China, Guangzhou 510422, China

IJERPH, 2022, vol. 19, issue 23, 1-22

Abstract: Transportation is the main carrier of population movement, so it is significant to clarify how different transportation modes influence epidemic transmission. This paper verified the relationship between different levels of facilities and epidemic transmission by use of the K-means clustering method and the Mann–Whitney U test. Next, quantile regression and negative binomial regression were adopted to evaluate the relationship between transportation modes and transmission patterns. Finally, this paper proposed a control efficiency indicator to assess the differentiated strategies. The results indicated that the epidemic appeared 2–3 days earlier in cities with strong hubs, and the diagnoses were nearly fourfold than in other cities. In addition, air and road transportation were strongly associated with transmission speed, while railway and road transportation were more correlated with severity. A prevention strategy that considered transportation facility levels resulted in a reduction of the diagnoses of about 6%, for the same cost. The results of different strategies may provide valuable insights for cities to develop more efficient control measures and an orderly restoration of public transportation during the steady phase of the epidemic.

Keywords: transportation modes; transmission pattern; quantile regression; negative binomial regression; control efficiency (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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