Urban spatial epidemic simulation model: A case study of the second COVID-19 outbreak in Beijing, China.

The second COVID-19 outbreak in Beijing was controlled by non-pharmaceutical interventions, which avoided a second pandemic. Until mass vaccination achieves herd immunity, cities are at risk of similar outbreaks. It is vital to quantify and simulate Beijing's non-pharmaceutical interventions to find effective intervention policies for the second outbreak. Few models have achieved accurate intra-city spatio-temporal epidemic spread simulation, and most modeling studies focused on the initial pandemic. We built a dynamic module of infected case movement within the city, and established an urban spatially epidemic simulation model (USESM), using mobile phone signaling data to create scenarios to assess the impact of interventions. We found that: (1) USESM simulated the transmission process of the epidemic within Beijing; (2) USESM showed the epidemic curve and presented the spatial distribution of epidemic spread on a map; and (3) to balance resources, interventions, and economic development, nucleic acid testing intensity could be increased and restrictions on human mobility in non-epidemic areas eased.

Quantifying human mobility resilience to the COVID-19 pandemic: A case study of Beijing, China.

Human mobility, as a fundamental requirement of everyday life, has been most directly impacted during the COVID-19 pandemic. Existing studies have revealed its ensuing changes. However, its resilience, which is defined as people's ability to resist such impact and maintain their normal mobility, still remains unclear. Such resilience reveals people's response capabilities to the pandemic and quantifying it can help us better understand the interplay between them. Herein, we introduced an integrated framework to quantify the resilience of human mobility to COVID-19 based on its change process. Taking Beijing as a case study, the resilience of different mobility characteristics among different population groups, and under different waves of COVID-19, were compared. Overall, the mobility range and diversity were found to be less resilient than decisions on whether to move. Females consistently exhibited lower resilience than males; middle-aged people exhibited the lowest resilience under the first wave of COVID-19 while older adult's resilience became the lowest during the COVID-19 rebound. With the refinement of pandemic-control measures, human mobility resilience was enhanced. These findings reveal heterogeneities and variations in people's response capabilities to the pandemic, which can help formulate targeted and flexible policies, and thereby promote sustainable and resilient urban management.

The Influence of Teacher-Student Interaction on the Effects of Online Learning: Based on a Serial Mediating Model.

During the COVID-19 pandemic, online education has become an important approach to learning in the information era and an important research topic in the field of educational technology as well as that of education in general. Teacher-student interaction in online education is an important factor affecting students' learning performance. This study employed a questionnaire survey to explore the influence of teacher-student interaction on learning effects in online education as well as the mediating role of psychological atmosphere and learning engagement. The study involved 398 college students studying at Chinese universities as the research object. Participants filled out a self-report questionnaire. The study found that (1) the level of teacher-student interaction positively affected students' learning effects (r = 0.649, p < 0.01). (2) The psychological atmosphere mediated the positive effect of the level of teacher-student interaction on learning effects with mediating effect value of 0.1248. (3) Learning engagement mediated the positive effect of teacher-student interaction on learning effects with a mediating effect value of 0.1539. (4) The psychological atmosphere and learning engagement play a chain-mediating role in the mechanism of teacher-student interaction affecting students' learning effects; that is, teacher-student interaction promotes students' learning engagement by creating a good psychological atmosphere, which, in turn, influences learning effects. The mediating effect value was 0.0403. The results indicate that teacher-student interaction not only directly affects students' learning effects but also influences students' learning effects through the mediating effect of the psychological atmosphere and learning engagement.

Analysis of changes in population's cross-city travel patterns in the pre- and post-pandemic era: A case study of China.

The coronavirus disease (COVID-19) outbreak has immensely changed people's travel behaviour. The changes in travel behaviour have had a huge impact on different industries, such as consumption, entertainment, commerce, office, and education. This study investigates the impact of COVID-19 on population travel patterns from three aspects: total trips, travel recovery degree, and travel distance. The result indicates that COVID-19 has reduced the total number of cross-city trips and flexible non-work travel; in the post-pandemic era, cross-city travel is mainly short-distance (distance <100 km). This study has significant policymaking implications for governments in countries where the population shares a similar change in travel behaviour.

Urban spatial epidemic simulation model: A case study of the second COVID‐19 outbreak in Beijing, China

The second COVID‐19 outbreak in Beijing was controlled by non‐pharmaceutical interventions, which avoided a second pandemic. Until mass vaccination achieves herd immunity, cities are at risk of similar outbreaks. It is vital to quantify and simulate Beijing's non‐pharmaceutical interventions to find effective intervention policies for the second outbreak. Few models have achieved accurate intra‐city spatio‐temporal epidemic spread simulation, and most modeling studies focused on the initial pandemic. We built a dynamic module of infected case movement within the city, and established an urban spatially epidemic simulation model (USESM), using mobile phone signaling data to create scenarios to assess the impact of interventions. We found that: (1) USESM simulated the transmission process of the epidemic within Beijing;(2) USESM showed the epidemic curve and presented the spatial distribution of epidemic spread on a map;and (3) to balance resources, interventions, and economic development, nucleic acid testing intensity could be increased and restrictions on human mobility in non‐epidemic areas eased.

How did human dwelling and working intensity change over different stages of COVID-19 in Beijing?

The COVID-19 pandemic has changed human daily activities significantly. Understanding the nature, causes, and extent of these changes is essential to evaluate the pandemic's influence on commerce, transportation, employment, and environment, among others. However, existing studies mainly focus on changes to general human mobility patterns; few have investigated changes in specific human daily activities. Based on one-year longitudinal mobile phone positioning data for more than 31 million users in Beijing, we tracked intensity changes in two basic human daily activities, dwelling and working, over the stages of COVID-19. The results show that during COVID-19 outbreak, human working intensity decreased about 60% citywide, while dwelling intensity decreased about 40% in some work and education areas. After COVID-19 was under control, intensity in most regions has recovered, but that in schools, hotels, entertainment venues, and tourism areas has not. These intensity changes at regional scale are due to behavior changes at individual scale: about 43% of residents left Beijing before COVID-19, while only 16% have returned back; all commuters decreased their commuting times during COVID-19, while only 75% have reverted to normal. The findings reveal variations in human activities caused by COVID-19 that can support targeted urban management in the post-epidemic era.

Spread of COVID-19 in China: analysis from a city-based epidemic and mobility model.

Understanding the processes and mechanisms of the spatial spread of epidemics is essential for making reasonable judgments on the development trends of epidemics and for adopting effective containment measures. Using multi-agent network technology and big data on population migration, this paper constructed a city-based epidemic and mobility model (CEMM) to stimulate the spatiotemporal of COVID-19. Compared with traditional models, this model is characterized by an urban network perspective and emphasizes the important role of intercity population mobility and high-speed transportation networks. The results show that the model could simulate the inter-city spread of COVID-19 at the early stage in China with high precision. Through scenario simulation, the paper quantitatively evaluated the effect of control measures "city lockdown" and "decreasing population mobility" on containing the spatial spread of the COVID-19 epidemic. According to the simulation, the total number of infectious cases in China would have climbed to 138,824 on February 2020, or 4.46 times the real number, if neither of the measures had been implemented. Overall, the containment effect of the lockdown of cities in Hubei was greater than that of decreasing intercity population mobility, and the effect of city lockdowns was more sensitive to timing relative to decreasing population mobility.

Quantifying the Time-Lag Effects of Human Mobility on the COVID-19 Transmission: A Multi-City Study in China.

The first wave of the 2019 novel coronavirus (COVID-19) epidemic in China showed there was a lag between the reduction in human mobility and the decline in COVID-19 transmission and this lag was different in cities. A prolonged lag would cause public panic and reflect the inefficiency of control measures. This study aims to quantify this time-lag effect and reveal its influencing socio-demographic and environmental factors, which is helpful to policymaking in controlling COVID-19 and other potential infectious diseases in the future. We combined city-level mobility index and new case time series for 80 most affected cities in China from Jan 17 to Feb 29, 2020. Cross correlation analysis and spatial autoregressive model were used to estimate the lag length and determine influencing factors behind it, respectively. The results show that mobility is strongly correlated with COVID-19 transmission in most cities with lags of 10 days (interquartile range 8 - 11 days) and correlation coefficients of 0.68 ± 0.12. This time-lag is consistent with the incubation period plus time for reporting. Cities with a shorter lag appear to have a shorter epidemic duration. This lag is shorter in cities with larger volume of population flow from Wuhan, higher designated hospitals density and urban road density while economically advantaged cities tend to have longer time lags. These findings suggest that cities with compact urban structure should strictly adhere to human mobility restrictions, while economically prosperous cities should also strengthen other non-pharmaceutical interventions to control the spread of the virus.

COVID-19: Challenges to GIS with Big Data

The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more than 100,000 people infected and thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multi-source big data, rapid visualization of epidemic information, spatial tracking of confirmed cases, prediction of regional transmission, spatial segmentation of the epidemic risk and prevention level, balancing and management of the supply and demand of material resources, and social-emotional guidance and panic elimination, which provided solid spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against the widespread epidemic, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management. At the data level, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.