Estimation of Disease Transmission in Multimodal Transportation Networks

• Published in: Journal of advanced transportation Vol. 2020; no. 2020; pp. 1 - 16
• Main Author: Zheng, Yu
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 31-08-2020; Hindawi; John Wiley & Sons, Inc; Hindawi Limited; Hindawi-Wiley
• Subjects: Big Data; Coronaviruses; COVID-19; Development and progression; Disease control; Disease transmission; Economic models; Empirical analysis; Epidemics; Epidemiology; Health risks; High speed rail; High speed trains; Infectious diseases; Logit models; Mathematical models; Medical research; Medicine, Experimental; Model testing; Multimodal transportation systems; Propagation; Public health; Risk analysis; Risk factors; Sensitivity analysis; Traffic models; Transportation; Transportation industry; Transportation models; Transportation networks; Travel; Travel modes; Viruses; Hong Kong; China; Beijing China; Macao; Taiwan; Hong Kong China; Inner Mongolia China; Tibet
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2020/8898923

Mathematical models are important methods in estimating epidemiological patterns of diseases and predicting the consequences of the spread of diseases. Investigation of risk factors of transportation modes and control of transportation exposures will help prevent disease transmission in the transportation system and protect people’s health. In this paper, a multimodal traffic distribution model is established to estimate the spreading of virus. The analysis is based on the empirical evidence learned from the real transportation network which connects Wuhan with other cities. We consider five mainstream travel modes, namely, auto mode, high-speed railway mode, common railway mode, coach mode, and flight mode. Logit model of economics is used to predict the distribution of trips and the corresponding diseases. The effectiveness of the model is verified with big data of the distribution of COVID-19 virus. We also conduct model-based tests to analyze the role of lockdown on different travel modes. Furthermore, sensitivity analysis is implemented, the results of which assist in policy-making for containing infection transmission through traffic.