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COVID-19, imagine having a temporary lip sticker that offers the protection of an n95 mask without the uncomfortable bulk. Using green electrospun nanofibers the lip sticker filters the virus and can communicate geospatial data to your phone using embedded NFC technology. Available in different designs and skins, some fiber formations can display temperature changes on your face. This paper investigates several prototypes of the described product. © 2022 Owner/Author.
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South Africa also has the highest burden of coronavirus disease 2019 (COVID-19) related comorbidities in Africa. We aimed to quantify the temporal and geospatial changes in unemployment, food insecurity, and their combined impact on depressive symptoms among South Africans who participated into several rounds of national surveys. We estimated the population-attributable risk percent (PAR%) for the combinations of the risk factors after accounting for their correlation structure in multifactorial setting. Our study provided compelling evidence for immediate and severe effect of the pandemic where 60% of South Africans reported household food insecurity or household hunger, shortly after the pandemic emerged in 2020. Despite the grants provided by the government, these factors were also identified as the most influential risk factors (adjusted odds ratios (aORs) ranged from 2.06 to 3.10, p < 0.001) for depressive symptoms and collectively associated with 62% and 53% of the mental health symptoms in men and women, respectively. Similar pattern was observed among pregnant women and 41% of the depressive symptoms were exclusively associated with those who reported household hunger. However, aORs associated with the concerns around pandemic and vaccine were mostly not significant and ranged from 1.12 to 1.26 which resulted substantially lower impacts on depressive symptoms (PAR%:7%-and-14%). Our findings suggest that South Africa still has unacceptably high rates of hunger which is accelerated during the pandemic. These results may have significant clinical and epidemiological implications and may also bring partial explanation for the low vaccine coverage in the country, as priorities and concerns are skewed towards economic concerns and food insecurity. © 2023, The Author(s).
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As epidemics such as COVID-19 and monkeypox spread, tracing specific people with restricted activities (targets) within administrative areas (targeted areas) is an effective option to slow the spread. Global Navigation Satellite Systems (GNSS) that can provide autonomous geospatial positioning of targets can assist this issue. K-nearest neighbors (KNN) is one of the most widely used algorithms for various classifications or predictions. In this paper, we will use the technique of KNN to classify the areas of the targets and explore the relationship between the density of targets to a area and the accuracy of classifications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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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.
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More than one year has passed since the outbreak of a new phenomenon in the world, a phenomenon that has affected and transformed all aspects of human life, it is nothing but pandemic of COVID-19. The field of electrical energy is no exception to this rule and has faced many changes and challenges over the 2020. In this paper, by applying artificial intelligence and the integrated clustering model, by k-means technique, combined with the meta-heuristic artificial bee colony (ABC) algorithm a new methodology is presented in order to optimal positioning of the repair crew based on annual data of power grid under situation of COVID-19 to improve the reliability and resiliency of the network due to the importance of electricity for medical purposes, home quarantine, telecommuting, and electronic services. Current research benefits from real interruption data related to year 2020 in Isfahan Province (Iran), reflexing both the huge changes in patterns of power consumption and dispatching as well as novel geographical distribution of blackouts due to COVID pandemic. The temporal distribution of interruptions is very close to the uniform distribution and the geographical distribution of interruptions relative to the density of subscribers had a normal distribution. Accordingly, proposed model is implemented for clustering the spatial data of blackouts recorded during 2020. The number of clusters is equal to the number of repair teams which in this study is considered equal to three. In the next step, the average spatial coordinates of the points of each cluster are calculated, which after reviewing the geographical conditions in the geo-spatial information system (GIS), indicates the optimal point for the deployment of electrical repair crew related to that cluster. The research findings show that after using the optimal points for a month, system average interruption duration index (SAIDI) decreased by an average of 23% compared to the same period of the 2020.
ABSTRACT
Proper territorial data management is critical for territorial planning projects, research, innovation, and the appropriate follow-up to act for the well-being of populations. A multidisciplinary team of professionals established a pilot project named Cortes Data Hub (Centro de Datos de Cortés). It presents several dashboards that show official statistics on the energy sector, mapping the region's energy demand, data on COVID-19 cases and vaccination rates by municipality or department, and a project using Google Earth that combines post-Eta and Iota observations and a social media campaign for disaster awareness and for the promotion of activities to develop tourism in the San Manuel Municipality. This pilot project shows the importance to observe and monitor various key environmental, health, and socioeconomic data. This will help improve initiatives for local development, disaster prevention and control, and the promotion of the One Health approach. The challenges to overcome are the quality and timing of data. Training more academics, government teams, and decision-makers in the use of new tools for data integration with earth observations are important for the Cortés department's development. © 2022 IEEE.
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BACKGROUND: Self-Organizing Maps (SOM) are an unsupervised learning clustering and dimensionality reduction algorithm capable of mapping an initial complex high-dimensional data set into a low-dimensional domain, such as a two-dimensional grid of neurons. In the reduced space, the original complex patterns and their interactions can be better visualized, interpreted and understood. METHODS: We use SOM to simultaneously couple the spatial and temporal domains of the COVID-19 evolution in the 278 municipalities of mainland Portugal during the first year of the pandemic. Temporal 14-days cumulative incidence time series along with socio-economic and demographic indicators per municipality were analyzed with SOM to identify regions of the country with similar behavior and infer the possible common origins of the incidence evolution. RESULTS: The results show how neighbor municipalities tend to share a similar behavior of the disease, revealing the strong spatiotemporal relationship of the COVID-19 spreading beyond the administrative borders of each municipality. Additionally, we demonstrate how local socio-economic and demographic characteristics evolved as determinants of COVID-19 transmission, during the 1st wave school density per municipality was more relevant, where during 2nd wave jobs in the secondary sector and the deprivation score were more relevant. CONCLUSIONS: The results show that SOM can be an effective tool to analysing the spatiotemporal behavior of COVID-19 and synthetize the history of the disease in mainland Portugal during the period in analysis. While SOM have been applied to diverse scientific fields, the application of SOM to study the spatiotemporal evolution of COVID-19 is still limited. This work illustrates how SOM can be used to describe the spatiotemporal behavior of epidemic events. While the example shown herein uses 14-days cumulative incidence curves, the same analysis can be performed using other relevant data such as mortality data, vaccination rates or even infection rates of other disease of infectious nature.
Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Portugal/epidemiology , Algorithms , Pandemics , Cluster Analysis , Spatio-Temporal AnalysisABSTRACT
An explosion of interest has been observed in disease mapping with the developments in advanced spatial statistics, data visualization and geographic information system (GIS) technologies. This technique is known as "Geo-Spatial Disease Clustering," mainly used for visualization and future disease expansion prediction. Its importance has been overwhelmingly observed since the COVID-19 pandemic outbreak. Government, Medical Institutes, and other medical practices gather large amounts of data from surveys and other sources. This data is in the form of notes, databases, spread sheets and text data files. Mostly this information is in the form of feedback from different groups like age group, gender, provider (doctors), region, etc. Incorporating such heterogeneous nature of data is quite challenging task. In this regard, variety of techniques and algorithms have been proposed in the literature, but their effectiveness varies due to data types, volume, format and structure of data and disease of interest. Mostly, the techniques are confined to a specific data type. To overcome this issue, in this research, a data visualization technique combined with data warehousing and GIS for disease mapping is proposed. This includes data cleansing, data fusion, data dimensioning, analysis, visualization, and prediction. Motivation behind this research is to create awareness about the disease for the guidance of patients, healthcare providers and government bodies. By this, we can extract information that describes the association of disease with respect to age, gender, and location. Moreover, the temporal analysis helps earlier prediction and identification of disease, to be care of and necessary avoiding arrangements can be taken.
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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.
ABSTRACT
More than one year has passed since the outbreak of a new phenomenon in the world, a phenomenon that has affected and transformed all aspects of human life, it is nothing but pandemic of COVID-19. The field of electrical energy is no exception to this rule and has faced many changes and challenges over the 2020. In this paper, by applying artificial intelligence and the integrated clustering model, by k-means technique, combined with the meta-heuristic artificial bee colony (ABC) algorithm a new methodology is presented in order to optimal positioning of the repair crew based on annual data of power grid under situation of COVID-19 to improve the reliability and resiliency of the network due to the importance of electricity for medical purposes, home quarantine, telecommuting, and electronic services. Current research benefits from real interruption data related to year 2020 in Isfahan Province (Iran), reflexing both the huge changes in patterns of power consumption and dispatching as well as novel geographical distribution of blackouts due to COVID pandemic. The temporal distribution of interruptions is very close to the uniform distribution and the geographical distribution of interruptions relative to the density of subscribers had a normal distribution. Accordingly, proposed model is implemented for clustering the spatial data of blackouts recorded during 2020. The number of clusters is equal to the number of repair teams which in this study is considered equal to three. In the next step, the average spatial coordinates of the points of each cluster are calculated, which after reviewing the geographical conditions in the geo-spatial information system (GIS), indicates the optimal point for the deployment of electrical repair crew related to that cluster. The research findings show that after using the optimal points for a month, system average interruption duration index (SAIDI) decreased by an average of 23% compared to the same period of the 2020.
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Prior work has studied the interaction experiences of screen-reader users with simple online data visualizations (e.g., bar charts, line graphs, scatter plots), highlighting the disenfranchisement of screen-reader users in accessing information from these visualizations. However, the interactions of screen-reader users with online geospatial data visualizations, commonly used by visualization creators to represent geospatial data (e.g., COVID-19 cases per US state), remain unexplored. In this work, we study the interactions of and information extraction by screen-reader users from online geospatial data visualizations. Specifically, we conducted a user study with 12 screen-reader users to understand the information they seek from online geospatial data visualizations and the questions they ask to extract that information. We utilized our findings to generate a taxonomy of information sought from our participants' interactions. Additionally, we extended the functionalities of VoxLens-an open-source multi-modal solution that improves data visualization accessibility-to enable screen-reader users to extract information from online geospatial data visualizations. © 2022 Owner/Author.
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The emergence of the novel corona virus disease (COVID-19) since 2019 has been a cause of significant concern for people throughout the world. While tremendous effort has been put in to it by healthcare facilities, both public and private, it would not be a stretch to state that the resources allotted were not enough to handle the floods of covid and the non-covid patients at the same time. As the entire world was under lockdown, it was considerably tougher for people to move around. This meant getting check-ups for covid was fairly tough. Thus, building up many hospital camps around a city became important. In this article, the locations of different healthcare institutions and residential flats in and around the city of Bhubaneswar were analysed. Clusters were generated out of highly dense regions utilising a number of unsupervised learning density based clustering techniques and the best model was picked among them. Folium leaflet maps in Python were used to show the clusters created from the best performing clustering method. This would allow us to collect crucial information identifying areas in severe need of medical attention. Thus, resources can be divided evenly among the population with the information acquired. © 2022 ACM.
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Communication during emergency and crises times is a critical aspect. When available information contains a spatial dimension, maps and interactive localization features may help conveying strong messages to audiences that are otherwise difficult to reach. The COVID-19 pandemic has prompted the design and implementation of a great number of online tools to communicate data of the disease spread and its dynamics that are helpful to support informed decisions for both people in their everyday life and decision makers. Observing this phenomenon has inspired this conceptualization of the geo-Online Explanatory Data Visualization (geo-OEDV) tools, set in the context of available geospatial information, of statistical visualisation tools and of the solid tradition of Geographical Information Systems. Blending classical statistical tools, digital cartography, and the confluence of many elements into a single screen, has produced the currently most spread geo-OEDV instance, i.e., the geo-dashboard and geo-infographics. In particular this paper conceptualises geo-OEDV as a category of meta-cartography that blends online communication with cartographic representation and management principles.
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Kernel density visualization (KDV) has been widely used in many geospatial analysis tasks, including traffic accident hotspot detection, crime hotspot detection, and disease outbreak detection. Although KDV can be supported by many scientific, geographical, and visualization software tools, none of these tools can support high-resolution KDV with large-scale datasets. Therefore, we develop the first versatile programming library, called LIBKDV, based on the set of our complexity-optimized algorithms. Given the high efficiency of these algorithms, LIBKDV not only accelerates the KDV computation but also enriches KDV-based geospatial analytics, including bandwidth-tuning analysis and spatiotemporal analysis, which cannot be natively and feasibly supported by existing software tools. In this demonstration, participants will be invited to use our programming library to explore interesting hotspot patterns on large-scale traffic accident, crime, and COVID-19 datasets. © 2022, VLDB Endowment. All rights reserved.
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With still ongoing COVID pandemic, there is an immediate need for a deeper understanding of how Twitter discussions (or chatters) in disinformation spreading communities get triggered. More specifically, the value is in monitoring how such trigger events in Twitter discussion do align with the timelines of relevant influencing events in the society (indicated in this work as campaign events). For campaign events in regards to COVID pandemic, we consider both NPI (Nonpharmaceutical Interventions) campaigns and disinformation spreading campaigns together. In this short paper we have presented a novel methodology to quantify, compare and relate two Twitter disinformation communities, in terms of their reaction patterns to the timelines of major campaign events. We have also analyzed these campaigns at their three geospatial granularity contexts: local county, state, and country/ federal. We have conducted a novel dataset collection on campaigns (NPI + Disinformation) at these different geospatial granularities. Then, with collected dataset on Twitter disinformation communities, we have performed a case study to validate our proposed methodology. © 2022 Public Domain.
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The SARS-CoV-2 epidemic has been extended by the evolution of more transmissible viral variants. In autumn 2020, the B.1.177 lineage became the dominant variant in England, before being replaced by the B.1.1.7 (Alpha) lineage in late 2020, with the sweep occurring at different times in each region. This period coincided with a large number of non-pharmaceutical interventions (e.g. lockdowns) to control the epidemic, making it difficult to estimate the relative transmissibility of variants. In this paper, we model the spatial spread of these variants in England using a meta-population agent-based model which correctly characterizes the regional variation in cases and distribution of variants. As a test of robustness, we additionally estimated the relative transmissibility of multiple variants using a statistical model based on the renewal equation, which simultaneously estimates the effective reproduction number R. Relative to earlier variants, the transmissibility of B.1.177 is estimated to have increased by 1.14 (1.12-1.16) and that of Alpha by 1.71 (1.65-1.77). The vaccination programme starting in December 2020 is also modelled. Counterfactual simulations demonstrate that the vaccination programme was essential for reopening in March 2021, and that if the January lockdown had started one month earlier, up to 30 k (24 k-38 k) deaths could have been prevented. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , Communicable Disease Control , Humans , SARS-CoV-2/genetics , SeasonsABSTRACT
The abundance of available information on social networks can provide invaluable insights into people’s responses to health information and public health guidance concerning COVID-19. This study examines tweeting patterns and public engagement on Twitter, as forms of social networks, related to public health messaging in two U.S. states (Washington and Louisiana) during the early stage of the pandemic. We analyze more than 7M tweets and 571K COVID-19-related tweets posted by users in the two states over the first 25 days of the pandemic in the U.S. (Feb. 23, 2020, to Mar. 18, 2020). We also qualitatively code and examine 460 tweets posted by selected governmental official accounts during the same period for public engagement analysis. We use various methods for analyzing the data, including statistical analysis, sentiment analysis, and word usage metrics, to find inter-and intra-state disparities of tweeting patterns and public engagement with health messaging. Our findings reveal that users inWashington were more active on Twitter than users in Louisiana in terms of the total number and density of COVID-19-related tweets during the early stage of the pandemic. Our correlation analysis results for counties or parishes show that the Twitter activities (tweet density, COVID-19 tweet density, and user density) were positively correlated with population density in both states at the 0.01 level of significance. Our sentiment analysis results demonstrate that the average daily sentiment scores of all and COVID-19-related tweets inWashington were consistently higher than those in Louisiana during this period. While the daily average sentiment scores of COVID-19-related tweets were in the negative range, the scores of all tweets were in the positive range in both states. Lastly, our analysis of governmental Twitter accounts found that these accounts’messages were most commonly meant to spread information about the pandemic, but that users were most likely to engage with tweets that requested readers take action, such as hand washing. Author
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Algorithms for home location inference from mobile phone data are frequently used to make high-stakes policy decisions, particularly when traditional sources of location data are unreliable or out of date. This paper documents analysis we performed in support of the government of Togo during the COVID-19 pandemic, using location information from mobile phone data to direct emergency humanitarian aid to individuals in specific geographic regions. This analysis, based on mobile phone records from millions of Togolese subscribers, highlights three main results. First, we show that a simple algorithm based on call frequencies performs reasonably well in identifying home locations, and may be suitable in contexts where machine learning methods are not feasible. Second, when machine learning algorithms can be trained with reliable and representative data, we find that they generally out-perform simpler frequency-based approaches. Third, we document considerable heterogeneity in the accuracy of home location inference algorithms across population subgroups, and discuss strategies to ensure that vulnerable mobile phone subscribers are not disadvantaged by home location inference algorithms. © 2022 Owner/Author.
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Conspiracy theories have seen a rise in popularity in recent years. Spreading quickly through social media, their disruptive effect can lead to a biased public view on policy decisions and events. We present a novel approach for LDA-pre-processing called Iterative Filtering to study such phenomena based on Twitter data. In combination with Hashtag Pooling as an additional pre-processing step, we are able to achieve a coherent framing of the discussion and topics of interest, despite of the inherent noisiness and sparseness of Twitter data. Our novel approach enables researchers to gain detailed insights into discourses of interest on Twitter, allowing them to identify tweets iteratively that are related to an investigated topic of interest. As an application, we study the dynamics of conspiracy-related topics on US Twitter during the last four months of 2020, which were dominated by the US-Presidential Elections and Covid-19. We monitor the public discourse in the USA with geo-spatial Twitter data to identify conspiracy-related contents by estimating Latent Dirichlet Allocation (LDA) Topic Models. We find that in this period, usual conspiracy-related topics played a marginal role in comparison with dominating topics, such as the US-Presidential Elections or the general discussions about Covid-19. The main conspiracy theories in this period were the ones linked to "Election Fraud" and the "Covid-19-hoax." Conspiracy-related keywords tended to appear together with Trump-related words and words related to his presidential campaign.
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City-making is a process in which several endogenous and exogenous variables associated with socio-economic, environmental, historical, and physical parameters play a significant role. The neoliberal and market-led notion of smart cities is highly criticized by many scholars for its polarized and inequitable approach to development. The traditional communities have continued for generations and inherit a unique living and residential culture bestowing them with an inherent smartness quotient. This concept of smartness for city planning is even more critical during the present times to understand the impact of the spatial structure of existing cities to deal with the COVID-19 outbreak. Authors identify a strong need to merge the two concepts of traditional communities and urban smartness for a holistic approach to building smart communities. This study aims to assess the smart spatial attributes of the traditional neighborhood-level urban communities such as compactness, walkability, and diversity. Primary household surveys were conducted in the walled city of Alwar, Rajasthan, India. The case study reveals compactly designed residential enclaves known as mohallas with mixed land use. The indigenous spatial elements such as squares (chowks), markets (bazaars), and streets (gali) proved to be crucial community gathering places for these settlements. Such zero-level assessment of existing socio-cultural and spatial attributes may enable the appropriate integration of intelligent technologies into our urban systems. Authors recommend harnessing the untapped potential of traditional communities in culturally rich countries like India to achieve the goals of a smart community. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.