Spatial Patterns of the Spread of COVID-19 in Singapore and the Influencing Factors

Identifying spatial heterogeneity of COVID-19 transmission clusters and their built-environment features at the neighbourhood scale

The identification of high-risk areas for infectious disease transmission and its built-environment features are crucial for targeted surveillance and early prevention efforts. While previous research has explored the association between infectious disease incidence and urban built environment, the investigation of spatial heterogeneity of built-environment features in high-risk areas has been insufficient. This paper aims to address this gap by analysing the spatial heterogeneity of COVID-19 clusters in Shanghai at the neighbourhood scale and examining associated built-environment features. Using a spatiotemporal clustering algorithm, the study analysed 1395 reported cases in Shanghai from March 6 to March 17, 2022. Both global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models were applied to examine the association between built-environment variables and the size of COVID-19 clusters. Our findings suggest that larger COVID-19 clusters emerging in the suburbs compared with the downtown and multiple built-environment features are significantly associated with this pattern. Specifically, neighbourhoods with a higher proportion of commercial, public service and industrial land, higher centrality of metro stations, and proximity to hospitals are positively associated with larger COVID-19 clusters, while neighbourhoods with higher land use mix and green/open spaces density are associated with smaller COVID-19 clusters. Moreover, we identified that metro stations with high centrality present the highest risk in the downtown, while commercial and public service places exhibit the highest risk in the suburbs. By highlighting the overlooked spatial heterogeneity of built-environment features for high-risk areas, this study aims to provide valuable guidance for public health departments in implementing place-based interventions to effectively prevent the spread of potential epidemics.

An analysis of the spatio-temporal behavior of COVID-19 patients using activity trajectory data

During the global pandemic, COVID-19 patients' activity trajectories and actions emerge as revelatory conduits elucidating their spatiotemporal behavior and transmission dynamics. This study analyzes COVID-19 patients' behavior in Nanjing and Yangzhou, China, by using patient activity trajectory data in conjunction with complex network theory. The main findings are as follows: (1) The evaluation of the activity network structure of patients revealed that "residential areas" and "vegetable markets" had the highest betweenness centrality, indicating that these are the primary nodes of COVID-19 transmission. (2) The power-law distribution of the degree distribution of nodes for different facility types revealed that residential areas, vegetable markets, and shopping malls had the most scale-free characteristics, indicating that a large number of patients visited these three facility types at a few access points. (3) Community detection showed that patient visitation sites in Nanjing and Yangzhou were divided into five or six communities, with the largest community containing the outbreak origin and several residential areas surrounding it. (4) Patients had fewer activities across administrative regions but more activities across the life circle when the pandemic broke out in the suburbs, and more activities across administrative regions but fewer activities across the life circle when the pandemic broke out in the central city. Based on these findings, this paper makes recommendations for future pandemic preparedness in an effort to achieve effective pandemic control and reduce the damage caused by pandemics. Overall, this study provides insights into understanding the transmission patterns of COVID-19 and may inform future pandemic control strategies.

Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19

Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies.

Multiscale Geographically Weighted Regression in the Investigation of Local COVID-19 Anomalies Based on Population Age Structure in Poland

A growing number of various studies focusing on different aspects of the COVID-19 pandemic are emerging as the pandemic continues. Three variables that are most commonly used to describe the course of the COVID-19 pandemic worldwide are the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered. In this paper, using the multiscale geographically weighted regression, an analysis of the interrelationships between the number of confirmed SARS-CoV-2 cases, the number of confirmed COVID-19 deaths, and the number of COVID-19 vaccine doses administered were conducted. Furthermore, using maps of the local R² estimates, it was possible to visualize how the relations between the explanatory variables and the dependent variables vary across the study area. Thus, analysis of the influence of demographic factors described by the age structure and gender breakdown of the population over the course of the COVID-19 pandemic was performed. This allowed the identification of local anomalies in the course of the COVID-19 pandemic. Analyses were carried out for the area of Poland. The results obtained may be useful for local authorities in developing strategies to further counter the pandemic.

Green spaces, especially nearby forest, may reduce the SARS-CoV-2 infection rate: A nationwide study in the United States

The coronavirus pandemic is an ongoing global crisis that has profoundly harmed public health. Although studies found exposure to green spaces can provide multiple health benefits, the relationship between exposure to green spaces and the SARS-CoV-2 infection rate is unclear. This is a critical knowledge gap for research and practice. In this study, we examined the relationship between total green space, seven types of green space, and a year of SARS-CoV-2 infection data across 3,108 counties in the contiguous United States, after controlling for spatial autocorrelation and multiple types of covariates. First, we examined the association between total green space and SARS-CoV-2 infection rate. Next, we examined the association between different types of green space and SARS-CoV-2 infection rate. Then, we examined forest-infection rate association across five time periods and five urbanicity levels. Lastly, we examined the association between infection rate and population-weighted exposure to forest at varying buffer distances (100 m to 4 km). We found that total green space was negative associated with the SARS-CoV-2 infection rate. Furthermore, two forest variables (forest outside park and forest inside park) had the strongest negative association with the infection rate, while open space variables had mixed associations with the infection rate. Forest outside park was more effective than forest inside park. The optimal buffer distances associated with lowest infection rate are within 1,200 m for forest outside park and within 600 m for forest inside park. Altogether, the findings suggest that green spaces, especially nearby forest, may significantly mitigate risk of SARS-CoV-2 infection.

Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada

The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.