The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi’an, China

• Published in: PloS one Vol. 19; no. 1; p. e0295950
• Main Authors: Xiang, Wang, Wang, Zezhi, Pan, Xin, Liu, Xiaobing, Yan, Xuedong, Chen, Li
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.01.2024; Public Library of Science (PLoS)
• Subjects: Algorithms; Asymptomatic; Balancing; Case studies; China; Cities; Coronaviruses; Correlation coefficient; Correlation coefficients; COVID-19; Development and progression; Disease control; Disease transmission; Earth Sciences; Economic development; Economics; Engineering and Technology; Epidemics; Evaluation; Infections; Literature reviews; Machine learning; Management; Medicine and Health Sciences; Multiple objective analysis; Optimization; Pandemics; Physical Sciences; Prevention; Propagation; Regression analysis; Social Sciences; Society; Tourism; Tourists; Traffic; Traffic control; Traffic engineering; Viruses; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0295950

Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi’an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development.