ABSTRACT
At the beginning of 2020, with the rapid spread of COVID-19 around the world, the passenger flow of subway has suffered from a serious impact. Based on the subway passenger flow data in Chicago, this article analyzes the impact of COVID-19 on rail transit passenger flow. ArcGIS is used to visualize the spatial-temporal distribution of the passenger flow of different stations during different time periods. Based on the fluctuation characteristics of passenger flow before and after the outbreak of COVID-19, one of the deep learning methods, the LSTM (Long-Short Term Memory) neural network model, is constructed to predict the passenger flow of each station in the scenario of no virus. The decline of passenger flow is calculated for each station. Stepwise regression model is constructed to determine factors that explain the decline in passenger flow, and significant factors are obtained: the original passenger flow, number of houses and jobs within 800m buffer zone, number of bus stops within 800m buffer zone, whether the station is a transfer station, distance from the station to the city center, and the number of low-income people. The results of the study show that after the outbreak of COVID-19, the passenger flow of the subway in Chicago experience a 'cliff-like' decline in the short term. The passenger flow in most areas dropped by more than 80%, and the passenger flow of some severely impacted stations dropped by more than 90%. Characteristics of the station and built environment factors of different stations influence the decline of passenger flow. © 2021 IEEE.
ABSTRACT
The objective of our study was to explore the influence of the current vaccination program and other relevant government factors to explain the variation in COVID-19 mortality in the world. The study involves a cross-sectional survey of COVID-19 related and government factors from 161 countries. We retrieved and processed publically available coronavirus pandemic data (July 17, 2021) from several online databases, excluding countries' data violating correlation and regression analysis assumptions. In addition, partial correlations studies and multivariate analysis were performed to explore the influence current vaccination program and other relevant government factors on the relationship between the explanatory variable and the total deaths due to COVID-19. The partial-correlation studies revealed that controlling for a complete dosage of COVID-19 vaccine per 100 people in the population had a significant (P<0.001) impact on the strength of the relationship between some explanatory variables and the response variable (total COVID-19 mortality). Furthermore, the Stepwise Linear Regression (SLR) model shows that the covariates, namely total_cases, hospital patients per million, hospital beds per thousand, male smokers, and people fully vaccinated per hundred, added significantly (P<0.001) to the prediction of the response variable. Our SLR model validation study revealed that the observed total COVID-19 mortality was highly correlated with the predicted total COVID-19 mortality in various countries (r = 0.977, P<0.001). Our Stepwise Linear Regression model performs significantly better with an R-squared value of 0.958 and adjusted R-squared value of 0.956 than other related regression models built to predict COVID-19 mortality. Based on our current findings, we conclude that governments with better hospital infrastructure and people with complete dosages of the COVID-19 vaccine will have minimal COVID-19 fatalities. © 2022 The Authors.