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The frequent occurrence of local COVID-19 today gives a strong necessity to better understand the effects of "source-case" distance and gathering places, which are often considered to be the key factors of the localized spatial clustering of an epidemic. In this study, the localized spatial clustering of COVID-19 cases, which originated in the Xinf...
Citations
... Investigates how the COVID-19 pandemic influenced travel behavior in Huzhou, China, focusing on the impact on social equity and the effectiveness of governance policies. Zhang et al. [77] Human Mobility in Urban Areas ...
The COVID-19 pandemic has greatly impacted the global economy, human health, and daily life. The World Health Organization declared it a pandemic on March 11, 2020. By May 2023, it had caused over seven million deaths. Until now, its economic and social effects are still felt. This systematic review and bibliometric analysis focuses on how COVID-19 has affected the economy and mobility using geospatial data. Geospatial data from sensors, social media, mobile apps, cars, and remote sensing give us near-real-time insights into people's behaviors and perceptions during the pandemic. The study examines how COVID-19 spread over time and space to help understand and reduce its impact. Even with challenges in combining different datasets, spatial analysis shows patterns of how humans and the pandemic interact. The finding answers key questions: How did COVID-19 affect economic activities and mobility? What common patterns do geospatial data show during the pandemic? By identifying common geospatial datasets and analyzing research trends, the study provides insights for policymakers and researchers to better prepare for future pandemics. This review helps understand the complex systems of pandemics and their effects on society using geospatial big data. Notably, the findings show that nighttime light intensity and mobile phone mobility data were the most consistently used indicators to monitor pandemic-driven disruptions and recovery, captured shifts in behavior and compliance with public health measures, offering a critical data source for real-time health surveillance, enabling health experts to better understand population responses, and adaptive policy interventions.
... This, in turn, hampers the accuracy of identifying factors that influence diseases. 51 Therefore, conducting spatial autocorrelation analysis is necessary before undertaking spatial statistical modeling of diseases. Spatial autocorrelation analysis is not only the foundation for identifying the spatial distribution characteristics of diseases but also the basis for spatial regression modeling of diseases. ...
This study examines the spatial-temporal evolution of overweight and obesity among Chinese adolescents aged 14–17. Data from five national surveys conducted between 2016 and 2020 were analyzed to determine distribution patterns and trends. Results showed that overweight and obesity exhibit spatial clustering, with greater severity in the north and less severity in the south. The issue has spread from the northeast to the southwest of Mainland China. Using a local autocorrelation model, the regions were divided into a northern disease cold spot area (Inner Mongolia) and a southern disease hot spot area (Guangxi). Over the past five years, overweight rates among Chinese adolescents have not been effectively curbed, but obesity has shown some success in control and reversal until 2019. Future efforts should focus on the spatial-temporal pattern of disease spread, targeting hotspot areas and abnormal values for regional synergy and precise prevention and control.
... Mathematical models have been used to study the relationship between the spatio-temporal spread of COVID-19 and human mobility [37][38][39][40][41][42][43][44][45]. A city-based epidemic and mobility model together with multi-agent network technology and big data on population migration were used to simulate the spatio-temporal spread of COVID-19 in China [45]. ...
The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.
... Although several spatiotemporal modeling on district-wise aggregated COVID-19 data were carried out in Malaysia [18][19][20] and specifically for Sarawak [21], to our best knowledge, no such geo-visualization and geospatial analysis were performed for COVID-19 exposed location point data within a division in Sarawak. Motivated by several studies on examining the distribution of the contagion by COVID-19 in urban environments of a city in Spain [22] and China [23] using authentic but anonymized microdata of infected people, this paper illustrates the utility of QGIS on geospatial visualization and analysis for the communication of publicly available COVID-19 exposed location data in Kuching, Sarawak. The list of exposed locations published to general public is useful for notifying the public on the selected locations visited by an infectious person and can serve as a type of bulletin board contact tracing [24]. ...
... The list of exposed locations published to general public is useful for notifying the public on the selected locations visited by an infectious person and can serve as a type of bulletin board contact tracing [24]. By using exposed locations data, but not microdata of infected people as in [22][23], whether the amount of exposed locations listed is sufficient to comprehend the disease intensity can be examined. Besides, this offers a significant case study on how and how much public communication needs spatial related data [25], as well as effective implementation of open-source geospatial software can impact decision-making at finer spatial levels. ...
The state government of Sarawak with the help of the Sarawak Disaster Management Committee (SDMC) has continuously made the updated information on the state COVID-19 situation and its ensuing control measures available to general public in the form of daily press statements. However, these statements are merely providing textual information on daily basis though the data are in fact rich in temporal and spatial properties. Since the onset of COVID-19 pandemic, spatiotemporal analysis becomes the key element to better understand the spread of COVID-19 in various spatial levels worldwide. Hence, there is an urgent need to convert this textual information into more valuable insights by applying geo-visualization techniques and geospatial statistics. The paper demonstrates the prospect of retrieving geospatial data from publicly available document to locate, map and analyze the spread of COVID-19 up to division level of Sarawak. Specifically, map visualization and geospatial statistical analysis are performed for the list of exposed locations, which are indeed locations visited by COVID-19 patients prior to being tested positive in Kuching division, using open-source geospatial software QGIS. It is found that these exposed locations concentrate on the build-up areas in the division and are in south-west to north-east direction of the center of Kuching in September and October 2021. Despite the number of exposed locations published is relatively small compared to the number of confirmed cases reported, both are nearly strongly correlated. The insights gained from such geospatial analysis may assist the local public health authorities to impose applicable disease control interventions at division level.
... [2] This has to begin with risk evaluation, wherein the baseline risk of transmission of the infection is ascertained along with the preparedness of the health system to deal with a potential outbreak. [16] This has to be supported with risk mitigation measures, which essentially refers to the implementation of standard prevention measures and the logistics required for the same. [16] Finally, the organizers should also look to disseminate the required information about all the planned measures to everyone involved, justifying the need for each one of them. ...
... [16] This has to be supported with risk mitigation measures, which essentially refers to the implementation of standard prevention measures and the logistics required for the same. [16] Finally, the organizers should also look to disseminate the required information about all the planned measures to everyone involved, justifying the need for each one of them. Regardless of the size of the gathering, it is always necessary that we should strongly adhere to the precautionary strategies and take efforts to minimize the potential risk, as the overall risk cannot be completely eliminated. ...
... Regardless of the size of the gathering, it is always necessary that we should strongly adhere to the precautionary strategies and take efforts to minimize the potential risk, as the overall risk cannot be completely eliminated. [14][15][16][17] In this regard, we also have to plan for strategic testing, isolation of the confirmed cases, contact tracing and their quarantine, and intensifying immunization against the infection. [5][6][7] ...
The ongoing coronavirus disease – 2019 (COVID-19) pandemic has changed the dynamics of all sectors and has significantly impacted the functioning of the healthcare delivery system. The purpose of the current review was to explore the significance of gatherings in COVID-19 outbreaks and the strategies to be implemented prior to organize a gathering to minimize the potential risk of a COVID-19 outbreak. An extensive search of all materials related to the topic was carried out in the PubMed search engine and a total of 14 articles were selected. Keywords used in the search include COVID-19 and gathering in the title alone only. As COVID-19 infection spreads via close contact, a gathering of any size carries the definite potential to amplify the risk of transmission and initiate a new chain of disease outbreaks. In conclusion, the COVID-19 pandemic is far from being yet over, and the decision to organize a gathering has to be based on the risk evaluation, risk mitigation, and establishment of a risk communication strategy. Even with all this, zero risk does not exist, and the best approach will be to strictly implement all the prevention and control measures and be responsible in all the gatherings.
... Therefore, the "source-case" distance was almost the only factor that significantly affected the transmission of COVID-19 in the model estimation of the epidemic. Therefore, we could obtain from the overall model estimation results that the expansion of the epidemic in the spatial dimension was mainly related to the "source-case" distance and the gathering of public service places, while the spread of the epidemic was pulled by more kinds of gathering places (S. Zhang, Yang, Wang, & Zhang, 2021). There was two interesting point worthy of attention: the first one is when the number of infected cases was taken as the spatial scale, the catering industry had a significant positive effect on the development of the epidemic within two cities, which is possible that individual-level data can better capture the influence of activities with smaller scope and shorter duration, such as dining, on the spread of the epidemic; ...
From the onset of the COVID-19 pandemic in 2020, studies on the microgeographies of epidemics have surged. However, studies have neglected the significant impact of multiple spatiotemporal units, such as report timestamps and spatial scales. This study examines three cities with localized COVID-19 resurgence after the first wave of the pandemic in mainland China to estimate the differential impact of spatiotemporal unit on exploring the influencing factors of epidemic spread at the microscale. The quantitative analysis results suggest that future spatial epidemiology research should give greater attention to the “symptom onset” timestamp instead of only the “confirmed” data and that “spatial transmission” should not be confused with “spatial sprawling” of epidemics, which can greatly reduce comparability between epidemiology studies. This research also highlights the importance of considering the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP) in future studies.
... Due to these characteristics, modeling studies that predict epidemic risk from the perspective of urban planning based on facility density, the mapping of urban functions in gathering places, and calculating the probability of people gathering and coming into contact in airtight places have attracted great attention [21,29,30]. Although several empirical studies have highlighted the significance of the local epidemic source of COVID-19 as a driving mechanism in the initial stage of the pandemic, various density indicators play a dominant role in the modeling of micro-scale spatiotemporal epidemiology [13,31]. ...
... However, the use of "densities" is obviously not perfect for the micro-scale spatiotemporal modeling of COVID-19 risk. Firstly, a considerable number of studies have found that "densities" have limited effects in the initial stage of regional epidemics [31]. Secondly, no matter what the simple population density is, trajectory density or facility density cannot reflect the real mechanism of human interaction. ...
... This area is about 249.3 km 2 and represents only 2.21% of Qingdao, but more than 42% of its population, making it a densely populated area within Qingdao and an area with a high COVID-19 infection risk ( Figure 1). To better understand the spatiotemporal risk of COVID-19 at the micro scale, we referred to the idea of Ling (2020) regarding community grid management and considered the advantages of the use of a hexagonal grid for dealing with adjacency problems, modified area unit problems (MAUPs), and high-precision spatiotemporal modeling [31,36]. In our study, a hexagonal grid with a side length of 250 m was used to divide the study area into blocks, and 1684 independent hexagons were selected for further analysis [37,38]. ...
Risk assessments for COVID-19 are the basis for formulating prevention and control strategies, especially at the micro scale. In a previous risk assessment model, various “densities” were regarded as the decisive driving factors of COVID-19 in the spatial dimension (population density, facility density, trajectory density, etc.). However, this conclusion ignored the fact that the “densities” were actually an abstract reflection of the “contact” frequency, which is a more essential determinant of epidemic transmission and lacked any means of corresponding quantitative correction. In this study, based on the facility density (FD), which has often been used in traditional research, a novel micro-scale COVID-19 risk predictor, facility attractiveness (FA, which has a better ability to reflect “contact” frequency), was proposed for improving the gravity model in combination with the differences in regional population density and mobility levels of an age-hierarchical population. An empirical analysis based on spatiotemporal modeling was carried out using geographically and temporally weighted regression (GTWR) in the Qingdao metropolitan area during the first wave of the pandemic. The spatiotemporally nonstationary relationships between facility density (attractiveness) and micro-risk of COVID-19 were revealed in the modeling results. The new predictors showed that residential areas and health-care facilities had more reasonable impacts than traditional “densities”. Compared with the model constructed using FDs (0.5159), the global prediction ability (adjusted R2) of the FA model (0.5694) was increased by 10.4%. The improvement in the local-scale prediction ability was more significant, especially in high-risk areas (rate: 107.2%) and densely populated areas (rate in Shinan District: 64.4%; rate in Shibei District: 57.8%) during the outset period. It was proven that the optimized predictors were more suitable for use in spatiotemporal infection risk modeling in the initial stage of regional epidemics than traditional predictors. These findings can provide methodological references and model-optimized ideas for future micro-scale spatiotemporal infection modeling.
Context
Understanding the scale-specific effects of different landscape variables on the COVID-19 epidemics is critical for developing the precise and effective prevention and control strategies within urban areas.
Objective
Based on the landscape epidemiology framework, we analyzed the scale-specific effects of urban landscape pattern on COVID-19 epidemics in Hangzhou, China.
Methods
We collected COVID-19 cases in Hangzhou from 2020‒2022 and combined the datasets of land use and land cover (LULC) and social gathering point (SGP) to quantify the urban landscape pattern. Optimal general linear model with stepwise regression was applied to explore the dominant landscape factors driving the COVID-19 transmission in the city. Furthermore, multi-scale geographically weighted regression illustrated the spatial heterogeneity and scale specificity of these landscape variables’ effects to COVID-19 epidemic.
Results
Eight landscape variables of LULC and SGP patterns were identified which explained 68.5% of the variance in spatial risk of COVID-19. Different optimal bandwidths across these variables in MGWR indicated their scale-specific effects. LSI of green space enhanced the spatial risk across the entire region. The effects of landscape contagion, the number of water bodies, LSI of cropland and built-up areas, and the density of commercial houses were detected to vary between urban and suburban areas. The effects of LSI of water bodies and the density of shopping malls were found to vary among different districts.
Conclusions
In this study, we firstly discriminated the scale-specific effects of different landscape variables on the COVID-19 epidemic in the urban region. These findings can help to optimize the differentiated zoning prevention and control strategies for COVID-19 in cities and guide policy-making and urban planning at a multi-scale hierarchical perspective to improve public health and urban sustainability.
The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices.
We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions.
This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions.
