COVID-19, commuting flows, and air quality

Risk compensation after COVID-19 vaccination: Evidence from vaccine rollout by exact birth date in South Korea

We utilize the phased rollout of COVID-19 vaccines by exact birth date in South Korea as a natural experiment for testing risk compensation. People may resume face-to-face social activities following vaccination because they perceive lower risk of infection. Applying a regression discontinuity design based on birth date cutoffs for vaccine eligibility, we find no evidence of risk-compensating behaviors, as measured by large, high-frequency data from credit card and airline companies as well as survey data. We find some evidence of self-selection into vaccine take-up based on perception toward vaccine effectiveness and side effects, but the treatment effects do not differ between compliers and never-takers.

Non-commuting intentions during COVID-19 in Nanjing, China: A hybrid latent class modeling approach

Non-commuting travel is essential for people to meet daily demands and regulate mental health, which is greatly disrupted due to the COVID-19 pandemic. To explore non-commuting intentions during COVID-19 across different groups of residents, this paper uses online survey data in Nanjing and constructs a hybrid latent class choice model that combines sociodemographic characteristics and psychological factors. Results showed that the respondents can be divided into two groups: the "cautious" group versus the "fearless" group. The "cautious" group with lower willingness to travel tend to be older, higher-income, higher-educated, female and full-time employees. Furthermore, the "cautious" group with higher perceived susceptibility is more obedient to government policies. In contrast, the "fearless" group is significantly affected by perceived severity and is more inclined to turn to personal protection against the pandemic. These results suggested that non-commuting trips were influenced not only by individual characteristics but also by psychological factors. Finally, the paper provides implications for the government to formulate COVID-19 management measures for the heterogeneity of different groups.

Change in the air due to the coronavirus outbreak in four major cities of India: What do the statistics say?

The onset of the novel Coronavirus (COVID-19) has impacted all sectors of society. To avoid the rapid spread of this virus, the Government of India imposed a nationwide lockdown in four phases. Lockdown, due to COVID-19 pandemic, resulted a decline in pollution in India in general and in dense cities in particular. Data on key air quality indicators were collected, imputed, and compiled for the period 1st August 2018 to 31st May 2020 for India's four megacities, namely Delhi, Mumbai, Kolkata, and Hyderabad. Autoregressive integrated moving average (ARIMA) model and machine learning technique e.g. Artificial Neural Network (ANN) with the inclusion of lockdown dummy in both the models have been applied to examine the impact of anthropogenic activity on air quality parameters. The number of indicators having significant lockdown dummy are six (PM2.5, PM10, NOx, CO, benzene, and AQI), five (PM2.5, PM10, NOx, SO2 and benzene), five (PM10, NOx, CO, benzene and AQI) and three (PM2.5, PM10, and AQI) for Delhi, Kolkata, Mumbai and Hyderabad respectively. It was also observed that the prediction accuracy significantly improved when a lockdown dummy was incorporated. The highest reduction in Mean Absolute Percentage Error (MAPE) is found for CO in Hyderabad (28.98%) followed by the NOx in Delhi (28.55%). Overall, it can be concluded that there is a significant decline in the value of air quality parameters in the lockdown period as compared to the same time phase in the previous year. Insights from the COVID-19 pandemic will help to achieve significant improvement in ambient air quality while keeping economic growth in mind.

Investigation of factors associated with mental health during the early part of the COVID-19 pandemic in South Korea based on machine learning algorithms: A cohort study

Objective

The coronavirus disease 2019 (COVID-19) pandemic is among the most critical public health problems worldwide in the last three years. We tried to investigate changes in factors between pre- and early stages of the COVID-19 pandemic.

Methods

The data of 457,309 participants from the 2019 and 2020 Community Health Survey were examined. Four mental health-related variables were selected for examination as a dependent variable (patient health questionnaire-9, depression, stress, and sleep time). Other variables without the aforementioned four variables were split into three groups based on the coefficient values of lasso and ridge regression models. The importance of each variable was calculated and compared using feature importance values obtained from three machine learning algorithms.

Results

Psychiatric and sociodemographic variables were identified, both during the pre- and early pandemic periods. In contrast, during the early pandemic period, average sleep time variables ranked the highest with the dependent variables regarding the experience of depression. The difference in sleep time before and after the pandemic was validated by the results of paired t-tests, which were statistically significant (p-value < 0.05).

Conclusions

Changes in the importance of mental health factors in the early pandemic period in South Korea were identified. For each mental health-dependent variable, average sleep time, experience of depression, and experience of accidents or addictions were found to be the most important factors. House type and type of residence were also found in regions with larger populations and a higher number of confirmed cases.

A Novel Method for Traffic Estimation and Air Quality Assessment in California

Motor vehicle traffic is recognized as one of the critical factors that causes air pollution; however, the relationship between traffic volume and air pollutant concentrations is unclear, especially at a local level. Traditional traffic volume monitoring systems collect traffic data through counting the number of vehicles, using either sensors or surveillance cameras, but they have clear limitations such as they can only monitor certain areas and specific occasions. To overcome such limitations, we introduce a method of monitoring traffic volume in the local area by collecting estimated travel times for virtual trips in Google Maps. We began collecting the data in January 2020; ironically, the COVID-19 pandemic provided a natural experimental environment of showing unusual trends in traffic volume and unexpected changes in air pollutants. We demonstrate monthly traffic volumes in urban areas and analyze the correlation of traffic volume with typical traffic-related air pollutants using the proposed traffic volume monitoring method.

Analyzing the Effect of Social Distancing Policies on Traffic at Sinchon Station, South Korea, during the COVID-19 Pandemic in 2020 and 2021

The COVID-19 pandemic is recognized as one of the most serious global health problems, and many countries implemented lockdown measures to mitigate the effects of the crisis caused by this respiratory infectious disease. In this study, we investigated the relationship between social distancing policies and changes in traffic volume in Sinchon Station, South Korea. We used an official COVID-19 report provided by the Korea Disease Control and Prevention Agency (KCDA) and Seoul Metropolitan Government (SMG) to review social distancing policies, and the changes in traffic patterns before and during the COVID-19 pandemic between January 2020 and November 2021 were analyzed. Our study reveals that the changes in the overall traffic patterns from acceleration phases to deceleration phases of COVID-19 were related to the alert levels of social distancing policies implemented to tackle the situation resulting from the COVID-19 pandemic. Herein, we found that a significant decline in traffic volume took place from August to September 2020 (13.5−19.7%, weekday; 19.4−31.7%, weekend), from December 2020 to January 2021 (20.0%−26.6%, weekday; 26.8−34.0%, weekend), and from July to September 2021 (3.2−13.1%, weekday; 38.3−44.7%, weekend) when compared to the corresponding periods in 2019 (paired t-test; p < 0.001). The results of this study provide strong support for the effectiveness of Seoul’s preemptive measures, namely, the central government’s intensive social distancing campaign, in managing and reducing the impact of the pandemic situation based on the precise analysis of 10 types of facilities.

COVID-19 Impact on Public Health, Environment, Human Psychology, Global Socioeconomy, and Education

The end of the year 2019 was marked by the introduction of a third highly pathogenic coronavirus, after SARS-CoV (2003) and MERS-CoV (2012), in the human population which was officially declared a global pandemic by the World Health Organization (WHO) on March 11, 2020. Indeed, the pandemic of COVID-19 (Coronavirus Disease 19) has evolved at an unprecedented rate: after its emergence in Wuhan, the capital of the province of Hubei of the People's Republic of China, in December 2019, the total number of confirmed cases did not cease growing very quickly in the world. In this manuscript, we have provided an overview of the impact of COVID-19 on health, and we have proposed different nutrients suitable for infected patients to boost their immune systems. On the other hand, we have described the advantages and disadvantages of COVID-19 on the environment including the quality of water, air, waste management, and energy consumption, as well as the impact of this pandemic on human psychology, the educational system, and the global economy. In addition, we have tried to come up with some solutions to counter the negative repercussions of the pandemic.