Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

Spatio-temporal effects of built environment on running activity based on a random forest approach in nanjing, China

Running activity is closely related to the urban built environment in terms of mental and physical health, and this relationship can change as a result of spatio-temporal changes. Most studies, however, do not account for this and assume a linear relationship exists between the built environment and running activity. This study, therefore, collected running data spanning 2019-2022, studied spatial distribution of four-year running activity, established built environment indicators, used a random forest approach to investigate the non-linear relationship between them, and evaluated spatio-temporal changes in the relationships over time. The findings suggested that running activities are spatially clustered and the degree of clustering varies over time, and nonlinear relationships and threshold effects between the built environment and running activity can be found through the random forest algorithm and partial dependence plots. Urban park green space, greenway, and the normalized difference vegetation index had the most significant effects on running activity. The effects of population, buildings, streets, road intersections, and points of interest on running activity changed during the Coronavirus disease 2019 pandemic. In 2022, however, these effects were consistent with those during the pre-pandemic period. Our findings fill research gaps by using spatio-temporal analysis and a non-linear approach; they can also provide a reference for urban planners in building running-suitable and healthy cities.

A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China

The outbreak of the Coronavirus Disease 2019 (COVID-19) has profoundly influenced daily life, necessitating the understanding of the relationship between the epidemic's progression and population dynamics. In this study, we present a data-driven framework that integrates GIS-based data mining technology and a Susceptible, Exposed, Infected and Recovered (SEIR) model. This approach helps delineate population dynamics at the grid and community scales and analyze the impacts of government policies, urban functional areas, and intercity flows on population dynamics during the pandemic. Xiamen Island was selected as a case study to validate the effectiveness of the data-driven framework. The results of the high/low cluster analysis provide 99% certainty (P < 0.01) that the population distribution between January 23 and March 16, 2020, was not random, a phenomenon referred to as high-value clustering. The SEIR model predicts that a ten-day delay in implementing a lockdown policy during an epidemic can lead to a significant increase in the number of individuals infected by the virus. Throughout the epidemic prevention and control period (January 23 to February 21, 2020), residential and transportation areas housed more residents. After the resumption of regular activities, the population was mainly concentrated in residential, industrial, and transportation, as well as road facility areas. Notably, the migration patterns into and out of Xiamen were primarily centered on neighboring cities both before and after the outbreak. However, migration indices from cities outside the affected province drastically decreased and approached zero following the COVID-19 outbreak. Our findings offer new insights into the interplay between the epidemic's development and population dynamics, which enhances the prevention and control of the coronavirus epidemic.

Assessment of the impact of reopening strategies on the spatial transmission risk of COVID-19 based on a data-driven transmission model

COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots.

Cross-regional analysis of the association between human mobility and COVID-19 infection in Southeast Asia during the transitional period of “living with COVID-19”

Background

In response to COVID-19, Southeast Asian (SEA) countries had imposed stringent lockdowns and restrictions to mitigate the pandemic ever since 2019. Because of a gradually boosting vaccination rate along with a strong demand for economic recovery, many governments have shifted the intervention strategy from restrictions to “Living with COVID-19” where people gradually resumed their normal activities since the second half of the year 2021. Noticeably, timelines for enacting the loosened strategy varied across Southeast Asian countries, which resulted in different patterns of human mobility across space and time. This thus presents an opportunity to study the relationship between mobility and the number of infection cases across regions, which could provide support for ongoing interventions in terms of effectiveness.

Objective

This study aimed to investigate the association between human mobility and COVID-19 infections across space and time during the transition period of shifting strategies from restrictions to normal living in Southeast Asia. Our research results have significant implications for evidence-based policymaking at the present of the COVID-19 pandemic and other public health issues.

Methods

We aggregated weekly average human mobility data derived from the Facebook origin and destination Movement dataset. and weekly average new cases of COVID-19 at the district level from 01-Jun-2021 to 26-Dec-2021 (a total of 30 weeks). We mapped the spatiotemporal dynamics of human mobility and COVID-19 cases across countries in SEA. We further adopted the Geographically and Temporally Weighted Regression model to identify the spatiotemporal variations of the association between human mobility and COVID-19 infections over 30 weeks. Our model also controls for socioeconomic status, vaccination, and stringency of intervention to better identify the impact of human mobility on COVID-19 spread.

Results

The percentage of districts that presented a statistically significant association between human mobility and COVID-19 infections generally decreased from 96.15% in week 1 to 90.38% in week 30, indicating a gradual disconnection between human mobility and COVID-19 spread. Over the study period, the average coefficients in 7 SEA countries increased, decreased, and finally kept stable. The association between human mobility and COVID-19 spread also presents spatial heterogeneity where higher coefficients were mainly concentrated in districts of Indonesia from week 1 to week 10 (ranging from 0.336 to 0.826), while lower coefficients were mainly located in districts of Vietnam (ranging from 0.044 to 0.130). From week 10 to week 25, higher coefficients were mainly observed in Singapore, Malaysia, Brunei, north Indonesia, and several districts of the Philippines. Despite the association showing a general weakening trend over time, significant positive coefficients were observed in Singapore, Malaysia, western Indonesia, and the Philippines, with the relatively highest coefficients observed in the Philippines in week 30 (ranging from 0.101 to 0.139).

Conclusions

The loosening interventions in response to COVID-19 in SEA countries during the second half of 2021 led to diverse changes in human mobility over time, which may result in the COVID-19 infection dynamics. This study investigated the association between mobility and infections at the regional level during the special transitional period. Our study has important implications for public policy interventions, especially at the later stage of a public health crisis.

Changes in mobility amid the COVID-19 pandemic in Sapporo City, Japan: An investigation through the relationship between spatiotemporal population density and urban facilities

By the end of 2021, the Omicron variant of coronavirus disease 2019 had become the dominant cause of a worldwide pandemic crisis. This demands a deeper analysis to support policy makers in creating interventions that not only protect people from the pandemic but also remedy its negative effects on the economy. Thus, this study investigated people's mobility changes through the relationship between spatiotemporal population density and urban facilities. Results showed that places related to daily services, restaurants, commercial areas, and offices experienced decreased visits, with the highest decline belonging to commercial facilities. Visits to health care and production facilities were stable on weekdays but increased on holidays. Educational institutions' visits decreased on weekdays but increased on holidays. People's visits to residential housing and open spaces increased, with the rise in residential housing visits being more substantial. The results also confirmed that policy interventions (e.g., declaration of emergency and upgrade of restriction level) have a great impact on people's mobility in the short term. The findings would seem to indicate that visit patterns at service and restaurant places decreased least during the pandemic. The analysis outcomes suggest that policy makers should pay more attention to risk perception enhancement as a long-term measure. Furthermore, the study clarified the population density of each facility type in a time series. Improving model performance would be promising for tracking and predicting the spread of future pandemics.