The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level

The effect of public health interventions on COVID-19 incidence in Queensland, Australia: a spatial cluster analysis

BACKGROUND

Using SaTScan™ Geographical Information Systems (GIS), spatial cluster analysis was used to examine spatial trends and identify high-risk clusters of Coronavirus 2019 (COVID-19) incidence in response to changing levels of public health intervention phases including international and state border closures, statewide vaccination coverage, and masking requirements.

METHODS

Changes in COVID-19 incidence were mapped at the statistical area 2 (SA2) level using a GIS and spatial cluster analysis was performed using SaTScan™ to identify most-likely clusters (MLCs) during intervention phases.

RESULTS

Over the study period, significant high-risk clusters were identified in Brisbane city (relative risk = 30.83), the southeast region (RR = 1.71) and moving to Far North Queensland (FNQ) (RR = 2.64). For masking levels, cluster locations were similar, with MLC in phase 1 in the southeast region (RR = 2.56) spreading to FNQ in phase 2 (RR = 2.22) and phase 3 (RR = 2.64). All p values <.0001.

CONCLUSIONS

Movement restrictions in the form of state and international border closures were highly effective in delaying the introduction of COVID-19 into Queensland, with very low levels of transmission prior to border reopening while mandatory masking may have played a role in decreasing transmission through behavioural changes. Early clusters were in highly populated regions, as restrictions eased clusters were identified in regions more likely to be rural or remote, with higher numbers of Indigenous people, lower vaccination coverage or lower socioeconomic status.

The transmission network and spatial-temporal distributions of COVID-19: A case study in Lanzhou, China

Public emergencies exert substantial adverse effects on the socioeconomic development of cities. Investigating the transmission characteristics of COVID-19 can lead to evidence-based strategies for future pandemic intervention and prevention. Drawing upon primary COVID-19 data collected at both the street level and from individuals with confirmed cases in Lanzhou, China, our study examined the spatial-temporal distribution of the pandemic at a detailed level. First, we constructed transmission networks based on social relationships and spatial behavior to elucidate the actual natural transmission chain of COVID-19. We then analyze key information regarding pandemic spread, such as superspreaders, superspreading places, and peak hours. Furthermore, we constructed a space-time path model to deduce the spatial transmission trajectory of the pandemic while validating it with real activity trajectory data from confirmed cases. Finally, we investigate the impacts of pandemic prevention and control policies. The progression of the pandemic exhibits distinct stages and spatial clustering characteristics. People with complex social relationships and daily life trajectories and places with high pedestrian flow and commercial activity venues are prone to becoming superspreaders and superspreading places. The transmission path of the pandemic showed a pattern of short-distance and adjacent transmission, with most areas not affected. Early-stage control measures effectively disrupt transmission hotspots and impede the spatiotemporal trajectory of pandemic propagation, thereby enhancing the efficacy of prevention and control efforts. These findings elucidate the characteristics and transmission processes underlying pandemics, facilitating targeted and adaptable policy formulation to shape sustainable and resilient cities.

A raster-based spatial clustering method with robustness to spatial outliers

Spatial clustering is an essential method for the comprehensive understanding of a region. Spatial clustering divides all spatial units into different clusters. The attributes of each cluster of the spatial units are similar, and simultaneously, they are as continuous as spatially possible. In spatial clustering, the handling of spatial outliers is important. It is necessary to improve spatial integration so that each cluster is connected as much as possible, while protecting spatial outliers can help avoid the excessive masking of attribute differences This paper proposes a new spatial clustering method for raster data robust to spatial outliers. The method employs a sliding window to scan the entire region to determine spatial outliers. Additionally, a mechanism based on the range and standard deviation of the spatial units in each window is designed to judge whether the spatial integration should be further improved or the spatial outliers should be protected. To demonstrate the usefulness of the proposed method, we applied it in two case study areas, namely, Changping District and Pinggu District in Beijing. The results show that the proposed method can retain the spatial outliers while ensuring that the clusters are roughly contiguous. This method can be used as a simple but powerful and easy-to-interpret alternative to existing geographical spatial clustering methods.

Variegated spatial-temporal landscape of COVID-19 infection in England: findings from spatially filtered multilevel models

BACKGROUND

Although there are empirical studies examining COVID-19 infection from a spatial perspective, majority of them focused on the USA and China, and there has been a lacuna of systematic research to unpack the spatial landscape of infection in the UK and its related factors.

METHODS

England's spatial-temporal patterns of COVID-19 infection levels in 2020 were examined via spatial clustering analysis. Spatially filtered multilevel models (SFMLM), capturing both hierarchical and horizontal spatial interactive effects, were applied to identify how different demographic, socio-economic, built environment and spatial contextual variables were associated with varied infection levels over the two waves in 2020.

RESULTS

The fragmented spatial distribution of COVID incidence in the first wave has made a rural-urban shift and resulted in a clearer north-south divide in England throughout 2020. The SFMLM results do not only identify the association between variables at different spatial scales with COVID-19 infection level but also highlight the increasing importance of spatial-dependent effect of the pandemic over time and that the locational spatial contexts also help explain variations in infection rates.

Spatiotemporal Patterns of the Omicron Wave of COVID-19 in the United States

COVID-19 has undergone multiple mutations, with the Omicron variant proving to be highly contagious and rapidly spreading across many countries. The United States was severely hit by the Omicron variant. However, it was still unclear how Omicron transferred across the United States. Here, we collected daily COVID-19 cases and deaths in each county from 1 December 2021 to 28 February 2022 as the Omicron wave. We adopted space-time scan statistics, the Hoover index, and trajectories of the epicenter to quantify spatiotemporal patterns of the Omicron wave of COVID-19. The results showed that the highest and earliest cluster was located in the Northeast. The Hoover index for both cases and deaths exhibited phases of rapid decline, slow decline, and relative stability, indicating a rapid spread of the Omicron wave across the country. The Hoover index for deaths was consistently higher than that for cases. The epicenter of cases and deaths shifted from the west to the east, then southwest. Nevertheless, cases were more widespread than deaths, with a lag in mortality data. This study uncovers the spatiotemporal patterns of Omicron transmission in the United States, and its underlying mechanisms deserve further exploration.

The utility of Zip4 codes in spatial epidemiological analysis

There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations.

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.

Is the virus-laden standing water change the transmission intensity of SARS-CoV-2 after precipitation? A framework for empirical studies

Understanding the relationship between precipitation and SARS-CoV-2 is significant for combating COVID-19 in the wet season. However, the causes for the variation of SARS-CoV-2 transmission intensity after precipitation is unclear. Starting from "the Zhengzhou event," we found that the virus-laden standing water formed after precipitation might trigger some additional routes for SARS-CoV-2 transmission and thus change the transmission intensity of SARS-CoV-2. Then, we developed an interdisciplinary framework to examine whether the health risk related to the virus-laden standing water needs to be a concern. The framework enables the comparison of the instant and lag effects of precipitation on the transmission intensity of SARS-CoV-2 between city clusters with different formation risks of the virus-laden standing water. Based on the city-level data of China between January 01, 2020, and December 31, 2021, we conducted an empirical study. The result showed that in the cities with a high formation risk of the virus-laden standing water, heavy rain increased the instant transmission intensity of SARS-CoV-2 by 6.2% (95%CI: 4.85-10.2%), while in the other cities, precipitation was uninfluential to SARS-CoV-2 transmission, revealing that the health risk of the virus-laden standing water should not be underestimated during the COVID-19 pandemic. To reduce the relevant risk, virus-laden water control and proper disinfection are feasible response strategies.

Tracking short-term health impacts attributed to ambient PM2.5 and ozone pollution in Chinese cities: an assessment integrates daily population

Joint and synergistic control of PM_2.5 and ozone pollution is an urgent need in China and a global-widely concerned issue. Health impact assessment could provide a comprehensive perspective for PM_2.5-ozone coordinated control strategies. For a detailed understanding of the seasonality and regionality of the health impacts attributed to PM_2.5 and ozone in China, this study extended the classic health impact function by daily population and assessed the short-term (daily) health impacts in 335 Chinese cities in 2021. Population migration indexes from Baidu were introduced to estimate the cities' daily population. Using this method, we quantitatively investigated the influence of population on short-term health impact assessment and identified which was significant in the Pearl River Delta (PRD) region and other populous cities. Although the annual sums of PM_2.5- and ozone-related daily health impacts were close for all Chinese cities, the PM_2.5-related health impact was equivalent to 333.96% and 32.07% of that ozone-related, during the cold and warm periods. The correlation and local spatial association analysis found significant city-specific and city-cluster associations of daily health impacts during the warm period and in Beijing-Tianjin-Hebei and surrounding regions (BTHS) and the Yangtze River Delta (YRD). Policymakers could promote period- and pollutant-targeted control actions for the major city groups, especially the BTHS, YRD, and PRD. Our methods and findings investigated the various influences of the population on short-term health impact assessment and proposed the PM_2.5-ozone collaborative control idea for key regions and city groups.

Promoting Strategies for Healthy Environments in University Halls of Residence under Regular Epidemic Prevention and Control: An Importance-Performance Analysis from Zhejiang, China

In the post-epidemic era, regular epidemic prevention and control is a daunting and ongoing task for nations all around the world. University halls of residence have been important spaces where university students balance their studies, work, and personal lives after COVID-19. Therefore, a healthy physical living environment deserves more attention. This paper compares situations before and after COVID-19 in an effort to evaluate the impact of indoor environments in university halls of residence on students. The study proposed eight vital dimensions for creating a healthy university hall of residence environment and, from 14 September to 4 October 2022, used an online questionnaire to collect data from 301 university students studying in Zhejiang, China. The key quality of service characteristics for fostering a healthy environment in university halls of residence were discovered using descriptive statistical analysis and revised importance-performance analysis (IPA). We found that an improved indoor physical environment and efficient arrangement of indoor space were crucial for the health of university students. The quality of educational services could be improved, and indoor exercise should be utilized effectively, both of which can contribute significantly to a healthy indoor environment. This study aims to contribute to the development of future initiatives to support healthy physical living environments in university halls of residence.

Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review

Background

The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases.

Methods

We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China’s Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Results

A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran’s I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors.

Conclusions

Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07669-9.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Understanding the spatial diffusion dynamics of the COVID-19 pandemic in the city system in China

Investigating the spatial epidemic dynamics of COVID-19 is crucial in understanding the routine of spatial diffusion and in surveillance, prediction, identification and prevention of another potential outbreak. However, previous studies attempting to evaluate these spatial diffusion dynamics are limited. Using city as the research unit and spatial association analysis as the primary strategy, this study explored the changing primary risk factors impacting the spatial spread of COVID-19 across Chinese cities under various diffusion assumptions and throughout the epidemic stage. Moreover, this study investigated the characteristics and geographical distributions of high-risk areas in different epidemic stages. The results empirically indicated rapid intercity diffusion at the early stage and primarily intracity diffusion thereafter. Before countermeasures took effect, proximity, GDP per capita, medical resources, outflows from Wuhan and intercity mobility significantly affected early diffusion. With speedily effective countermeasures, outflows from the epicenter, proximity, and intracity outflows played an important role. At the early stage, high-risk areas were mainly cities adjacent to the epicenter, with higher GDP per capita, or a combination of higher GDP per capita and better medical resources, with more outflow from the epicenter, or more intercity mobility. After countermeasures were effected, cities adjacent to the epicenter, or with more outflow from the epicenter or more intracity mobility became high-risk areas. This study provides an insightful understanding of the spatial diffusion of COVID-19 across cities. The findings are informative for effectively handling the potential recurrence of COVID-19 in various settings.

Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland

This study aims to elucidate the variations in spatiotemporal patterns and sociodemographic determinants of SARS-CoV-2 infections in Helsinki, Finland. Global and local spatial autocorrelation were inspected with Moran's I and LISA statistics, and Getis-Ord Gi* statistics was used to identify the hot spot areas. Space-time statistics were used to detect clusters of high relative risk and regression models were implemented to explain sociodemographic determinants for the clusters. The findings revealed the presence of spatial autocorrelation and clustering of COVID-19 cases. High-high clusters and high relative risk areas emerged primarily in Helsinki's eastern neighborhoods, which are socioeconomically vulnerable, with a few exceptions revealing local outbreaks in other areas. The variation in COVID-19 rates was largely explained by median income and the number of foreign citizens in the population. Furthermore, the use of multiple spatiotemporal analysis methods are recommended to gain deeper insights into the complex spatiotemporal clustering patterns and sociodemographic determinants of the COVID-19 cases.

Spatial clustering behaviour of Covid-19 conditioned by the development level: Case study for the administrative units in Romania

Spatial analyses related to Covid-19 have been so far conducted at county, regional or national level, without a thorough assessment at the continuous local level of administrative-territorial units like cities, towns, or communes. To address this gap, we employ daily data on the infection rate provided for Romanian administrative units from March to May 2021. Using the global and local Moran I spatial autocorrelation coefficients, we identify significant clustering processes in the Covid-19 infection rate. Additional analysis based on spatially smoothed rate maps and spatial regressions prove that this clustering pattern is influenced by the development level of localities, proxied by unemployment rate and Local Human Development Index. Results show the features of the 3rd wave in Romania, characterized by a quadratic trend.

Are spatial patterns of Covid-19 changing? Spatiotemporal analysis over four waves in the region of Cantabria, Spain

This research approaches the empirical study of the pandemic from a social science perspective. The main goal is to reveal spatiotemporal changes in Covid-19, at regional scale, using GIS technologies and the emerging three-dimensional bins method. We analyze a case study of the region of Cantabria (northern Spain) based on 29,288 geocoded positive Covid-19 cases in the four waves from the outset in March 2020 to June 2021. Our results suggest three main spatial processes: a reversal in the spatial trend, spreading first followed by contraction in the third and fourth waves; then the reduction of hot spots that represent problematic areas because of high presence of cases and growing trends; and finally, an increase in cold spots. All this generates relevant knowledge to help policy-makers from regional governments to design efficient containment and mitigation strategies. Our research is conducted from a geoprevention perspective, based on the application of targeted measures depending on spatial patterns of Covid-19 in real time. It represents an opportunity to reduce the socioeconomic impact of global containment measures in pandemic management.

Banana Fusarium Wilt Disease Detection by Supervised and Unsupervised Methods from UAV-Based Multispectral Imagery

Banana Fusarium wilt (BFW) is a devastating disease with no effective cure methods. Timely and effective detection of the disease and evaluation of its spreading trend will help farmers in making right decisions on plantation management. The main purpose of this study was to find the spectral features of the BFW-infected canopy and build the optimal BFW classification models for different stages of infection. A RedEdge-MX camera mounted on an unmanned aerial vehicle (UAV) was used to collect multispectral images of a banana plantation infected with BFW in July and August 2020. Three types of spectral features were used as the inputs of classification models, including three-visible-band images, five-multispectral-band images, and vegetation indices (VIs). Four supervised methods including Support Vector Machine (SVM), Random Forest (RF), Back Propagation Neural Networks (BPNN) and Logistic Regression (LR), and two unsupervised methods including Hotspot Analysis (HA) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) were adopted to detect the BFW-infected canopies. Comparing to the healthy canopies, the BFW-infected canopies had higher reflectance in the visible region, but lower reflectance in the NIR region. The classification results showed that most of the supervised and unsupervised methods reached excellent accuracies. Among all the supervised methods, RF based on the five-multispectral-band was considered as the optimal model, with higher overall accuracy (OA) of 97.28% and faster running time of 22 min. For the unsupervised methods, HA reached high and balanced OAs of more than 95% based on the selected VIs derived from the red and NIR band, especially for WDRVI, NDVI, and TDVI. By comprehensively evaluating the classification results of different metrics, the unsupervised method HA was recommended for BFW recognition, especially in the late stage of infection; the supervised method RF was recommended in the early stage of infection to reach a slightly higher accuracy. The results found in this study could give advice for banana plantation management and provide approaches for plant disease detection.

A Regional Economy’s Resistance to the COVID-19 Shock: Sales Revenues of Micro-, Small-, and Medium-Sized Enterprises in South Korea

The coronavirus disease 2019 (COVID-19) is a global pandemic that has heavily impacted not only the health sector, but also the economic sector in general. Many countries have projected a negative economic impact, and the effect on micro-, small-, and medium-sized enterprises (MSMEs) is predicted to be significantly large. This study estimated the regional resistance of MSME sales revenues and identified the regional economic factors that affect resistance by analyzing South Korea, a country with one of the lowest economic impact projections from COVID-19. Resistance was estimated by comparing sales revenues and changes in resistance observed during the early COVID-19 period to those recorded in the same weeks (weeks 6 to 9) of 2019. The factors that affect regional resistance were determined by conducting robust regression and spatial regression analyses. The results show that the number of confirmed COVID-19 cases, a direct risk factor, is negatively related to regional resilience, while diversity is positively related to regional resistance. To improve the regional resistance against uncertain events, this study recommends increased diversity among regional industrial structures to reduce the duration of the early shock of an unexpected adverse event.

Dynamic impact of negative public sentiment on agricultural product prices during COVID-19

The COVID-19 pandemic has had a significantly negative impact on public sentiment, which has resulted in panic and some irrational buying behavior, which in turn has had a complex impact on agricultural product prices. This study quantified online negative sentiment using micro-blog text mining and a time-varying parameter vector autoregressive model (TVP-VAR) to empirically analyze the dynamic impact of negative public emotions on agricultural product prices during the COVID-19 pandemic in China. It was found that the online negative sentiment impacted agricultural products prices during COVID-19 and had significant time-varying, lag, and life cycle characteristics, with the responses being most significant in the spread and recession periods. Differences were found in the price responses for different agricultural products and in different risk areas. The online negative sentiment was found to have the greatest impact on vegetable prices, with livestock products and vegetable prices being mainly positively impacted, fruit prices being mainly negatively impacted, and aquatic product prices being negatively impacted in the early stage and positively impacted in the middle and late stages. The online negative sentiment had the greatest impact on medium-risk area agricultural product prices, followed by low-risk areas, with the lowest impact found on the high-risk area agricultural product prices. Three policy suggestions for epidemic monitoring, public opinion guidance and control, and the timely release of agricultural product information are given based on the results.

Spatiotemporal Analysis of Covid-19 in Turkey

The Covid-19 pandemic continues to threaten public health around the world. Understanding the spatial dimension of this impact is very important in terms of controlling and reducing the spread of the pandemic. This study measures the spatial association of the Covid-19 outbreak in Turkey between February 8 and May 28, 2021 and reveals its spatiotemporal pattern. In this context, global and local spatial autocorrelation was used to determine whether there is a spatial association of Covid-19 infections, while the spatial regression model was employed to reveal the geographical relationship of the potential factors affecting the number of Covid-19 cases. As a result of the analyzes made in this context, it has been observed that there are spatial associations and distinct spatial clusters in Covid-19 cases at the provincial level in Turkey. The results of the spatial regression model showed that population density and elderly dependency ratio are very important in explaining the model of Covid-19 case numbers. Additionally, it has been revealed that Covid-19 is affected by the Covid-19 numbers of neighboring provinces, apart from the said explanatory variables. The findings of the study revealed that spatial analysis is helpful in understanding the spread of the pandemic in Turkey. It has been determined that geographical location is an important factor to be considered in the investigation of the factors affecting Covid-19.

Local Characteristics Related to SARS-CoV-2 Transmissions in the Seoul Metropolitan Area, South Korea

The Seoul metropolitan area is one of the most populated metropolitan areas in the world; hence, Seoul's COVID-19 cases are highly concentrated. This study identified local demographic and socio-economic characteristics that affected SARS-CoV-2 transmission to provide locally targeted intervention policies. For the effective control of outbreaks, locally targeted intervention policies are required since the SARS-CoV-2 transmission process is heterogeneous over space. To identify the local COVID-19 characteristics, this study applied the geographically weighted lasso (GWL). GWL provides local regression coefficients, which were used to account for the spatial heterogeneity of SARS-CoV-2 outbreaks. In particular, the GWL pinpoints statistically significant regions with specific local characteristics. The applied explanatory variables involving demographic and socio-economic characteristics that were associated with higher SARS-CoV-2 transmission in the Seoul metropolitan area were as follows: young adults (19~34 years), older population, Christian population, foreign-born population, low-income households, and subway commuters. The COVID-19 case data were classified into three periods: the first period (from January 2020 to July 2021), the second period (from August to November 2020), and the third period (from December 2020 to February 2021), and the GWL was fitted for the entire period (from January 2020 to February 2021). The result showed that young adults, the Christian population, and subway commuters were the most significant local characteristics that influenced SARS-CoV-2 transmissions in the Seoul metropolitan area.

Environmental impact of increased soap consumption during COVID-19 pandemic: Biodegradable soap production and sustainable packaging

A year into the coronavirus disease 2019 pandemic, the role of washing hands with soap and hand disinfectants is unavoidable as a primary way to control the infection spread in communities and healthcare facilities. The extraordinary surge in demand for handwashing products has led to environmental concerns. Since soaps are complex mixtures of toxic and persistent active ingredients, the prudent option is to promote eco-friendly replacements for the current products. On the other hand, with the increase in soap packaging waste production, soap packaging waste management and recycling become essential to reduce environmental impact. This systematic review aimed to collect some recent methods for identifying biodegradable and sustainable raw materials to produce and package cleaning agents, especially soap.

Spatial priority for COVID-19 vaccine rollout against limited supply

The COVID-19 vaccines are limited in supply which requires vaccination by priority. This study proposes a spatial priority-based vaccine rollout strategy for Bangladesh. Demographic, economic and vulnerability, and spatial connectivity - these four types of factors are considered for identifying the spatial priority. The spatial priority is calculated and mapped using a GIS-based analytic hierarchy process. Our findings suggest that both demographic and economic factors are keys to the spatial priority of vaccine rollout. Secondly, spatial connectivity is an essential component for defining spatial priority due to the transmissibility of COVID-19. A total of 12 out of 64 districts were found high-priority followed by 22 medium-priorities for vaccine rollout. The proposed strategy by no means suggests ending mass vaccination by descending age groups but an alternative against limited vaccine supply. The spatial priority of the vaccine rollout strategy proposed in this study might help to curb down COVID-19 transmission and to keep the economy moving. The inclusion of granular data and contextual factors can significantly improve the spatial priority identification which can have wider applications for other infectious and transmittable diseases and beyond.