Geo-visualization of Sarawak COVID-19 Publicly Available Data Employing Open-source Geospatial Software

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. © 2023 Horizon Research Publishing. All rights reserved.

Increase in Motor Vehicle Crash Severity: An Unforeseen Consequence of COVID-19.

INTRODUCTION: The 2019 coronavirus (COVID-19) pandemic led to stay-at-home (SAH) orders in Pennsylvania targeted at reducing viral transmission. Limitations in population mobility under SAH have been associated with decreased motor vehicle collisions (MVC) and related injuries, but the impact of these measures on severity of injury remains unknown. The goal of this study is to measure the incidence, severity, and outcomes of MVC-related injuries associated with SAH in Pennsylvania. MATERIALS & METHODS: We conducted a retrospective geospatial analysis of MVCs during the early COVID-19 pandemic using a state-wide trauma registry. We compared characteristics of patients with MVC-related injuries admitted to Pennsylvania trauma centers during SAH measures (March 21-July 31, 2020) with those from the corresponding periods in 2018 and 2019. We also compared incidence of MVCs for each zip code tabulation area (ZCTA) in Pennsylvania for the same time periods using geospatial mapping. RESULTS: Of 15,550 trauma patients treated during the SAH measures, 3486 (22.4%) resulted from MVCs. Compared to preceding years, MVC incidence decreased 10% under SAH measures with no change in mortality rate. However, in ZCTA where MVC incidence decreased, there was a 16% increase in MVC injury severity. CONCLUSIONS: Stay-at-home orders issued in response to the COVID-19 pandemic in Pennsylvania were associated with significant changes in MVC incidence and severity. Identifying such changes may inform resource allocation decisions during future pandemics or SAH events.

Urban air quality changes resulting from the lockdown period due to the COVID-19 pandemic.

This work aims to quantify potential pollution level changes in an urban environment (Madrid city, Spain) located in South Europe due to the lockdown measures for preventing the SARS-CoV-2 transmission. Polluting 11 species commonly monitored in urban zones were attended. Except for O3, a prompt target pollutant levels abatement was reached, intensely when implanted stricter measures and moderately along those measures' relaxing period. In the case of TH and CH4, it is evidenced a progressive diminution over the lockdown period. While the highest decreasing average changes relapsed on NOx (NO2: - 40.0% and NO: - 33.3%) and VOCs (C7H8: - 36.3% and C6H6: - 32.8%), followed by SO2 (- 27.0%), PM10 (- 19.7%), CO (- 16.6%), CH4 (- 14.7%), TH (- 11.6%) and PM2.5 (- 10.1%), the O3 level slightly raised 0.4%. These changes were consistently dependent on the measurement station location, emphasizing urban background zones for SO2, CO, C6H6, C7H8, TH and CH4, suburban zones for PM2.5 and O3, urban traffic sites for NO and PM10, and keeping variations reasonably similar at all the stations in the case of NO2. Those pollution changes were not translated in variations on geospatial pattern, except for NO, O3 and SO2. Although the researched urban atmosphere improvement was not attributable to meteorological conditions' variations, it was in line with the decline in traffic intensity. The evidenced outcomes might offer valuable clues to air quality managers in urban environments regarding decision-making in favor of applying punctual severe measures for quickly and considerably relieving polluting high load occurred in urban environments. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04464-6.

Assessing food safety risks based on a geospatial analysis: toward a cross-regional food safety management.

BACKGROUND: Food safety risks (FSRs) are increasingly characterized by geographical complexity along with rapid urbanization, changing dietary pattern, and the modernization of the food industry. These factors pose challenges for food risk control in developing economies, more so during the global COVID-19 pandemic. The accurate assessment of risk source and transfer path is a crucial step toward enhancing cross-regional food safety management. This study aims to examine the spatial distribution, transfer path and driving factors of FSRs in China, provided with a national food safety database collected from 8.63 million batches of food sampling inspections for 33 different types of foods across 30 provinces. RESULTS: The findings reveal significant regional disparities in FSRs, which is the highest in the west with small-scale sampling inspection and the lowest in the east with intensive sampling inspection. Catering and processed foods with higher daily consumption suffer more profound FSR than agricultural products. As evidenced by the shrinking low-low agglomeration areas, the local FSRs have been effectively controlled. The high-high agglomeration areas playing positive impacts on risk control are expanding while distributed discretely. CONCLUSION: The spatial transfer of FSRs is significantly driven by multiple drivers: regulatory capacity and intensity, information disclosure, food industry, regional economy, and food consumption. Assessing FSRs based on a geospatial analysis contributes to identifying risk sources, optimizing risk management, and constructing a sustainable food safety system. © 2023 Society of Chemical Industry.

Breast Cancer Champions: a peer-to-peer education and mobile mammography program improving breast cancer screening rates for women of African heritage.

INTRODUCTION: Nationally, women of African heritage die at higher rates from breast cancer than women of other races or ethnicities. We developed Breast Cancer Champions (BCC) a peer-to-peer education program, which recruited 12 women and deployed them into the community in August 2020 during the height of the COVID pandemic. BCC aims to improve breast cancer screening rates for women of African heritage through peer-to-peer education, which has proven successful for addressing cancer-related health disparities. METHODS: BCC community experts, or "Champions," are peer-to-peer educators that conduct awareness and screening events in their communities. Champion's education activities were tracked by bi-weekly check-in calls, which recorded the activity type, location, and the number of participants for each event. We used spatial and statistical analyses to determine the efficacy of the program at increasing screening rates for women within the area of Champion activity versus women outside of their activity area. RESULTS: Over 15 months, Champions conducted 245 in-person or online events to engage women in their community for screening. More women of African heritage were screened in areas Champions were active during the intervention compared to historical data comparing areas outside of the Champion activity in the prior 15 months (X 2 = 3.0845, p = 0.079). CONCLUSION: BCC successes could be attributed to pivoting to online community building when in-person events were restricted and enabling Champions to design and conduct their own events, which increased outreach possibilities. We demonstrate improved screening outcomes associated with an updated peer-to-peer education program.

Leveraging natural language processing and geospatial time series model to analyze COVID-19 vaccination sentiment dynamics on Tweets.

Objective: To develop and apply a natural language processing (NLP)-based approach to analyze public sentiments on social media and their geographic pattern in the United States toward coronavirus disease 2019 (COVID-19) vaccination. We also aim to provide insights to facilitate the understanding of the public attitudes and concerns regarding COVID-19 vaccination. Methods: We collected Tweet posts by the residents in the United States after the dissemination of the COVID-19 vaccine. We performed sentiment analysis based on the Bidirectional Encoder Representations from Transformers (BERT) and qualitative content analysis. Time series models were leveraged to describe sentiment trends. Key topics were analyzed longitudinally and geospatially. Results: A total of 3 198 686 Tweets related to COVID-19 vaccination were extracted from January 2021 to February 2022. 2 358 783 Tweets were identified to contain clear opinions, among which 824 755 (35.0%) expressed negative opinions towards vaccination while 1 534 028 (65.0%) demonstrated positive opinions. The accuracy of the BERT model was 79.67%. The key hashtag-based topics include Pfizer, breaking, wearamask, and smartnews. The sentiment towards vaccination across the states showed manifest variability. Key barriers to vaccination include mistrust, hesitancy, safety concern, misinformation, and inequity. Conclusion: We found that opinions toward the COVID-19 vaccination varied across different places and over time. This study demonstrates the potential of an analytical pipeline, which integrates NLP-enabled modeling, time series, and geospatial analyses of social media data. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccination, help address the concerns of vaccine skeptics, and provide support for developing tailored policies and communication strategies to maximize uptake.

Analyzing Access to Health Facilities by Road Using Unconventional Data Sources

The COVID-19 pandemic has led to an urgent need in emerging economies to quickly identify vulnerable populations that do not live within access of a health facility for testing and vaccination. This access information is critical to prioritize investments in mobile and temporary clinics. To meet this need, the World Bank team sought to develop an open-source methodology that could be quickly and easily implemented by government health departments, regardless of technical and data collection capacity. The team explored use of readily available open-source and licensable data, as well as non-intensive computational methodologies. By bringing together population data from Facebook's Data for Good program, travel-time calculations from Mapbox, road network and point-of-interest data from the OpenStreetMap (OSM), and the World Bank's open-source GOSTNets network routing tools, we created a computational framework that supports efficient and granular analysis of road-based access to health facilities in two pilot locations—Indonesia and the Philippines. Our findings align with observed health trends in these countries and support identification of high-density areas that lack sufficient road access to health facilities. Our framework is easy to replicate, allowing health officials and infrastructure planners to incorporate access analysis in pandemic response and future health access planning. © National Academy of Sciences: Transportation Research Board 2022.

Expanding Vulnerability Indices for Pandemic Effects

In this study, our goal is to identify potentially vulnerable communities that could be subject to ongoing or compounding impacts from the pandemic and/or that may experience a slower recovery due to sociodemographic factors. For this purpose, we compiled information from multiple databases related to sociodemographic and health variables. We used a ranking-based method to integrate them and develop new combined indices. We also investigated a time-dependent correlation between vulnerability components and COVID-19 statistics to understand their time-dependent relationship. We ultimately developed pandemic vulnerability indices by combining CDC's social vulnerability index, our newly developed composite health vulnerability index, and COVID-19 impact indices. We also considered additional assessments include expected annual loss due to natural hazards and community resilience. Potential hot spots (at the county level) were identified throughout the United States, and some general trends were noted. Counties with high COVID-19 impact indices and higher values of the pandemic vulnerability indices were primarily located in the southern United States or coastal areas in the Eastern and Southwestern United States at the beginning of the COVID-19 pandemic. Over time, the computed pandemic vulnerability indices shifted to higher values for counties in the southern and north-central United States, while values calculated for the northwestern and northeastern communities tended to decrease.

A Shape of Geo-Tagged Media Bias in COVID-19 Related Twitter

Warning: This paper contains content that may be offensive or upsetting. Social media has become an essential data source for understanding many aspects of our lives, from personal opinions to local patterns. However, it also contains more subjective and biased information than traditional media due to community bubbles and echo chambers. This study aims to examine the correlation between media bias on Twitter and COVID-19-related critical events. We used an open-Access dataset of COVID-19 tweets from March 2020 to July 2021. We first developed a classification model to identify media bias using an attention-based bidirectional long short-Term memory (BiLSTM) model. Using this classification model, we classified 350k geo-Tagged tweets into two classes: "biased"and "unbiased", focusing on four countries: The US, UK, Canada, and India. In our study, we found that critical events, such as the sharp increase of the coronavirus death toll, would exert the rise of biased information on Twitter. Additionally, we found that in the US, the states' bachelor degree per capita correlated with the ratio of biased tweets, which is consistent with the Dunning-Kruger effect. The unemployment rate was only found positively correlated with the ratio of biased tweets in the UK. Presumably, other factors (e.g., income inequality, social trust, etc.) should be introduced to understand the dissemination of biased tweets. © 2022 ACM.

Expanding Vulnerability Indices for Pandemic Effects

In this study, our goal is to identify potentially vulnerable communities that could be subject to ongoing or compounding impacts from the pandemic and/or that may experience a slower recovery due to sociodemographic factors. For this purpose, we compiled information from multiple databases related to sociodemographic and health variables. We used a ranking-based method to integrate them and develop new combined indices. We also investigated a time-dependent correlation between vulnerability components and COVID-19 statistics to understand their time-dependent relationship. We ultimately developed pandemic vulnerability indices by combining CDC's social vulnerability index, our newly developed composite health vulnerability index, and COVID-19 impact indices. We also considered additional assessments include expected annual loss due to natural hazards and community resilience. Potential hot spots (at the county level) were identified throughout the United States, and some general trends were noted. Counties with high COVID-19 impact indices and higher values of the pandemic vulnerability indices were primarily located in the southern United States or coastal areas in the Eastern and Southwestern United States at the beginning of the COVID-19 pandemic. Over time, the computed pandemic vulnerability indices shifted to higher values for counties in the southern and north-central United States, while values calculated for the northwestern and northeastern communities tended to decrease.

The role of care and the local economy in women’s labour force participation: evidence from Mexico and Colombia in the pandemic era

Women face disproportionate care burdens on their time because of traditional gender roles, lack of public policies supporting them and the lack of government services for satisfying society's care needs. This unequal distribution of care responsibilities reduces their opportunities to fully participate in labour markets. We argue that all else equal, women's physical proximity to affordable care services is key to determining their accessibility to them. In addition, services may have different effects on women's labour force participation (LFP), depending on their care responsibilities and other characteristics of their social and economic local conditions, such as size and type of economic output. We use geospatial analysis to explore the relationship between the local supply of care services and women's LFP. We use the population census and the intercensal population survey of Mexico, together with data from economic censuses and directories of care and financial services. We also develop an exploratory data analysis model for the Colombian case. We find that, given gender roles in care provision and women's accessibility to economic sectors, the supply of care services and the type of local economies are quite significant in determining their LFP, regardless of their educational level. Accordingly, mere investment in care services may not be enough since the economic output and type of activities also interfere with LFP. Besides, this effect increased considerably during the COVID-19 pandemic. © 2022 Oxfam KEDV.

Geospatial analysis of COVID-19 distribution and its relation to public transportation services

COVID-19 has changed our lifestyle;nowadays, activities such as studying, working, and meetings, among others, have drastically changed from being face to face to being remote;however, there is still an activity that has not changed as quickly as needed because of its main purpose, i.e., transportation. In this approach, a complete COVID-19 geospatial analysis is conducted correlating official reported cases of COVID-19-infected individuals and those who died with the data of public transportation, focusing on specific areas and the subway service in Mexico City. The geospatial analysis allows identifying the importance of some subway stations and their influence on the rate of infected people and also allows visualizing the distribution of COVID-19 all over the geographic areas near the subway stations and understanding the distribution of COVID-19 in the city. Finally, the approach generates a visualization model of the distribution of COVID-19 and its relation to the subway service using geospatial intelligence. © 2022 Elsevier Inc. All rights reserved.

Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence

Background: Unequal housing access resulted in more than 150 million homeless people worldwide, with mil-lions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research. Methods: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research. Results: Our findings indicated a geographic clustering of case study locations- 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters. Conclusion: Most studies were restricted in analyzing and visualizing the patterns and disease clusters;however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.

Emergence of COVID-19 and Patterns of Early Transmission in an Appalachian Sub-Region.

Background: In mid-March 2020, very few cases of COVID-19 had been confirmed in the Central Blue Ridge Region, an area in Appalachia that includes 47 jurisdictions across northeast Tennessee, western North Carolina, and southwest Virginia. Authors described the emergence of cases and outbreaks in the region between March 18 and June 11, 2020. Methods: Data were collected from the health department websites of Tennessee, North Carolina, and Virginia beginning in mid-March for an ongoing set of COVID-19 monitoring projects, including a newsletter for local healthcare providers and a Geographic Information Systems (GIS) dashboard. In Fall 2020, using these databases, authors conducted descriptive and geospatial cluster analyses to examine case incidence and fatalities over space and time. Results: In the Central Blue Ridge Region, there were 4432 cases on June 11, or 163.22 cases per 100,000 residents in the region. Multiple days during which a particularly high number of cases were identified in the region were connected to outbreaks reported by local news outlets and health departments. Most of these outbreaks were linked to congregate settings such as schools, long-term care facilities, and food processing facilities. Implications: By examining data available in a largely rural region that includes jurisdictions across three states, authors were able to describe and disseminate information about COVID-19 case incidence and fatalities and identify acute and prolonged local outbreaks. Continuing to follow, interpret, and report accurate and timely COVID-19 case data in regions like this one is vital to residents, businesses, healthcare providers, and policymakers.

Political polarization drives online conversations about COVID-19 in the United States.

Since the outbreak in China in late 2019, the novel coronavirus (COVID-19) has spread around the world and has come to dominate online conversations. By linking 2.3 million Twitter users to locations within the United States, we study in aggregate how political characteristics of the locations affect the evolution of online discussions about COVID-19. We show that COVID-19 chatter in the United States is largely shaped by political polarization. Partisanship correlates with sentiment toward government measures and the tendency to share health and prevention messaging. Cross-ideological interactions are modulated by user segregation and polarized network structure. We also observe a correlation between user engagement with topics related to public health and the varying impact of the disease outbreak in different U.S. states. These findings may help inform policies both online and offline. Decision-makers may calibrate their use of online platforms to measure the effectiveness of public health campaigns, and to monitor the reception of national and state-level policies, by tracking in real-time discussions in a highly polarized social media ecosystem.

How education and racial segregation intersect in neighborhoods with persistently low COVID-19 vaccination rates in Philadelphia.

BACKGROUND: COVID-19 infection has disproportionately affected socially disadvantaged neighborhoods. Despite this disproportionate burden of infection, these neighborhoods have also lagged in COVID-19 vaccinations. To date, we have little understanding of the ways that various types of social conditions intersect to explain the complex causes of lower COVID-19 vaccination rates in neighborhoods. METHODS: We used configurational comparative methods (CCMs) to study COVID-19 vaccination rates in Philadelphia by neighborhood (proxied by zip code tabulation areas). Specifically, we identified neighborhoods where COVID-19 vaccination rates (per 10,000) were persistently low from March 2021 - May 2021. We then assessed how different combinations of social conditions (pathways) uniquely distinguished neighborhoods with persistently low vaccination rates from the other neighborhoods in the city. Social conditions included measures of economic inequities, racial segregation, education, overcrowding, service employment, public transit use, health insurance and limited English proficiency. RESULTS: Two factors consistently distinguished neighborhoods with persistently low COVID-19 vaccination rates from the others: college education and concentrated racial privilege. Two factor values together - low college education AND low/medium concentrated racial privilege - identified persistently low COVID-19 vaccination rates in neighborhoods, with high consistency (0.92) and high coverage (0.86). Different values for education and concentrated racial privilege - medium/high college education OR high concentrated racial privilege - were each sufficient by themselves to explain neighborhoods where COVID-19 vaccination rates were not persistently low, likewise with high consistency (0.93) and high coverage (0.97). CONCLUSIONS: Pairing CCMs with geospatial mapping can help identify complex relationships between social conditions linked to low COVID-19 vaccination rates. Understanding how neighborhood conditions combine to create inequities in communities could inform the design of interventions tailored to address COVID-19 vaccination disparities.

Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis

Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step heading of a human for instance, with spatial information is an ubiquitous way to correct for the cumulative noises. This so-called map-matching process can be achieved in several ways. In this paper, a novel real-time map-matching approach was developed, using a backtracking particle filter that benefits from the implemented geospatial analysis, which reduces the complexity of spatial queries and provides flexibility in the use of different kinds of spatial constraints. The goal was to generalize the algorithm to permit the use of any kind of odometry data calculated by different sensors and approaches as the input. Further research, development, and comparisons have been done by the easy implementation of different spatial constraints and use cases due to the modular structure. Additionally, a simple map-based optimization using transition areas between floors has been developed. The developed algorithm could achieve accuracies of up to 3 m at approximately the 90th percentile for two different experiments in a complex building structure.

The need of an environmental justice approach for wastewater based epidemiology for rural and disadvantaged communities: A review in California.

Amid the 2019 coronavirus disease pandemic (COVID-19), the scientific community has a responsibility to provide accessible public health resources within their communities. Wastewater based epidemiology (WBE) has been used to monitor community spread of the pandemic. The goal of this review was to evaluate the need for an environmental justice approach for COVID-19 WBE starting with the state of California in the United States. Methods included a review of the peer-reviewed literature, government-provided data, and news stories. As of June 2021, there were twelve universities, nine public dashboards, and 48 of 384 wastewater treatment plants monitoring wastewater for SARS-CoV-2 within California. The majority of wastewater monitoring in California has been conducted in the urban areas of Coastal and Southern California (34/48), with a lack of monitoring in more rural areas of Central (10/48) and Northern California (4/48). Similar to the access to COVID-19 clinical testing and vaccinations, there is a disparity in access to wastewater testing which can often provide an early warning system to outbreaks. This research demonstrates the need for an environmental justice approach and equity considerations when determining locations for environmental monitoring.

Visualization of dataflows: A casestudy of COVID-19 rumors

One of the most significant and rapidly developing works in the field of data analysis is information flow management. Within the analysis targeted and stochastic dissemination patterns are studied. The solving of such problems is relevant due to the global growth in the amount of information and its availability for a wide range of users. The paper presents a study of dissemination of information messages in open networks on the example of COVID-19. The study was conducted with the use of visual analytics. Informational messages from the largest world and Russian information services, social networks and instant messengers were used as sources of information. Due to the large amount of information on the topic, the authors proposed a pattern of the wave-like dissemination of information on the example of topic clusters on the connection of COVID-19, hydroxychloroquine and 5G. The developed methods can be scaled up to analyze information events of various topics. © 2021 Copyright for this paper by its authors.