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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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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
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.
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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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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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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.
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INTRODUCTION: the new coronavirus (COVID-19) that emerged from Wuhan, Hubei Province of China in December 2019, causing severe acute respiratory syndrome (SARS) has fast spread across the entire globe, with most countries struggling to slow and reduce the spread of the virus through rapid screening, testing, isolation, case management, contact tracing, implementing social distancing and lockdowns. This has been shown to be a major factor in countries that have been successful in containing COVID-19 transmission. Early detection of cases is important, and the use of geospatial technology can support to detect and easily identify potential hotspots that will require timely response. The use of spatial analysis with geographic information systems (GIS) had proved to be effective in providing timely and effective solutions in supporting epidemic response and pandemics over the years. It has developed and evolved rapidly with a complete technological tool for representing data, model construction, visualization and platform construction among others. METHODS: we conducted a geospatial analysis to develop a web mapping application using ArcMap and ArcGIS online to guide and support active case search of potential COVID-19 cases, within 500m radius of COVID-19 confirmed cases to improve detection and testing of suspected cases. RESULTS: the web mapping application tool guides the active case search teams in the field, with clear boundaries on the houses to be visited within 500-meter radius of confirmed positive cases, to conduct active case search of all cases of severe acute respiratory illnesses (SARI), acute respiratory illnesses (ARI), pneumonia etc, to detect and test for COVID-19 towards containing the pandemic. CONCLUSION: the use of GIS and spatial statistical tools have become an important and valuable tool in decision-making and, more importantly, guiding health care professional and other stakeholders in the response being carried out in a more coherent and easy manner. It has proven to be effective in supporting the active case search process to rapidly detect, test and isolate cases during the process, towards containing the COVID-19 pandemic.