ABSTRACT
Development of applications based on open API is becoming increasingly popular today. Innovative projects using these technologies provide new opportunities for real-time human health monitoring. Such opportunities are also implemented using Internet of Things (IoT), artificial intelligence (AI) and cloud computing technologies. In the study, we developed an application based on open APIs using smart gadgets and meteorological geographic information system in the process of generating a message about the dangers to human health associated with: the presence of pollen in the air (grass pollen, birch pollen and olive pollen) indicating the level of its concentration in the air;problems with air quality, if the air quality indicator exceeds the permissible standards. The addition of such functions expands the possibilities to provide timely information about potential risks and threats and, accordingly, is an "anthropo-geo-sensor-digital"prerequisite for effective decision-making, prevailing. The implementation of this IoT system has significant methodological and technological potential that can be used to improve the efficiency of Healthcare, both in extreme conditions and in conditions of sustainable existence. First of all, this is relevant during and after the COVID-19 pandemic. The system we have developed can also be seen as one of the ways to innovate in Healthcare, in the educational process in institutions of higher education and in further scientific research on this topic. Further research in this area may be related to data processing in Healthcare systems based on machine learning, deep learning. © 2023 Copyright for this paper by its authors.
ABSTRACT
Bali is one of the islands known as a tourist destination with the main purpose of traveling to Bali to enjoy the beauty of nature. Various tourist activities in Bali are well known both nationally and internationally. With so many types of tours available in Bali, travelers, especially first-time visitors, may need a guide on the tourist attractions they want to visit in Bali. However, some tourists, especially backpackers, backpackers are known to explore tourist attractions independently, freely, and with as little budget as possible. Concerning this statement and the decline in tourists to Bali due to the impact of the Covid-19 pandemic, the author decided to use the Haversine formula to develop a geographic information system for smart backpacker travel recommendations. with this problem. This Android application helps tourists travel from registration to tour guides. Implementing the Haversine formula in your app is equivalent to up to 88% of the distance reported by the Google Maps API. These results are used as a recommendation function for the selection of attractions by showing the distance between the previous backpacker's location selection and attractions. © 2022 IEEE.
ABSTRACT
The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin/destination (O/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease. © 2023 by the authors.
ABSTRACT
The declaration of COVID-19 as a pandemic has largely amplified the spread of related information on social platforms, such as Twitter, Facebook and WeChat. In this work, we investigate how the disease and information co-evolve in the population. We focus on COVID-19 and its information during the period when the disease was widely spread in China, i.e., from January 25th to March 24th, 2020. The co-evolution between disease and information is explored via the spatial analysis of the two spreading processes. We visualize the geo-location of both disease and information at the province level and find that disease is more geo-localized compared to information. High correlation between disease and information data is observed, and also people care about the spread of disease only when it comes to their neighborhood. Regard to the content of the information, we obtain that positive messages are more negatively correlated with the disease compared to negative and neutral messages. Additionally, two machine learning algorithms, i.e., linear regression and random forest, are introduced to further predict the number of infected using characteristics, such as disease spatial related and information-related features. We obtain that both the disease spatial related characteristics of nearby cities and information-related characteristics can help to improve the prediction accuracy. The methodology proposed in this paper may shed light on new clues of emerging infections prediction. © 2013 IEEE.
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.
ABSTRACT
This paper aims to understand the impact of the COVID-19 on human mobility. We explore individual traces through spatial-temporal check-ins on social media. In particular, we leverage geo-tagged tweets, to extrapolate people's geo-locations in New York City (NYC) when they check in Twitter. Building on these data, we perform gyration and travel similarity analysis to study the change of travel pattern during the pandemic. We make a comparison of users' gyration and the number of COVID-19 deaths across time. We find that (1) Users' gyration decreased by 35% after the stay-at-home order. (2) Check-in activities on social media is related to the fear of coronavirus: User's gyration has a negative correlation (-0.7) with the number of deaths across time. (3) Travel similarity decreased by 15% from March 2020 to June 2020 because many people did not travel outside after the stay-at-home order. (4) Inter-personal travel similarity among users was lower than 0.2 and individual traces of a majority of people had no overlap during the pandemic. © 2022 IEEE.
ABSTRACT
Various aspects of the recent COVID-19 outbreak have been extensively discussed on online social media platforms and, in particular, on Twitter. Geotagging COVID-19-related discourse data on Twitter is essential for understanding the different discourse facets and their regional relevance, including calls for social distancing, acceptance of measures implemented to contain virus spread, anti-vaccination campaigns, and misinformation. In this paper, we aim at enriching TweetsCOV19 - a large COVID-19 discourse knowledge base of more than 20 million tweets - with geographic information. For this purpose, we evaluate two state-of-the-art Geotagging algorithms: (1) DeepGeo - predicting the tweet location and (2) GeoLocation - predicting the user location. We compare pre-trained models with models trained on context-specific ground truth geolocation data extracted from TweetsCOV19. Models trained on our context-specific data achieve more than 6.7% improvement in Acc@25 compared to the pre-trained models. Further, our results show that DeepGeo outperforms GeoLocation and that longer tweets are, in general, easier to geotag. Finally, we use the two geotagging methods to study the distribution of tweets per country in TweetsCOV19 and compare the geographic coverage, i.e., the number of countries and cities each algorithm can detect. © 2022 ACM.
ABSTRACT
The COVID-19 pandemic has contributed to un-precedented rates of unemployment and greater uncertainty in the job market. There is a growing need for data-driven tools and analyses to better inform the public on trends within the job market. In particular, obtaining a 'snapshot' of available employment opportunities mid-pandemic promises insights to inform policy and support retraining programs. In this work, we combine data scraped from the Canadian Job Bank and Numbeo globally crowd-sourced repository to explore the relationship between job postings during a global pandemic and Key Performance Indicators (e.g. quality of life [QOL] index, cost of living) for major cities across Canada. This analysis aims to help Canadians make informed career decisions, collect a 'snapshot' of the Canadian employment opportunities amid a pandemic, and inform job seekers in identifying the correct fit between the desired lifestyle of a city and their career. We collected a new high-quality dataset of job postings from jobbank.gc.ca obtained with the use of ethical web scraping and performed exploratory data analysis on this dataset to identify job opportunity trends. When optimizing for average salary of job openings with QOL, affordability, cost of living, and traffic indices, it was found that Edmonton, AB consistently scores higher than the mean, and is therefore an attractive place to move. Furthermore, we identified optimal provinces to relocate to with respect to individual skill levels. It was determined that Ajax, Marathon, and Chapleau, ON are each attractive cities for IT professionals, construction workers, and healthcare workers respectively when maximizing average salary. Finally, we publicly release our scraped dataset as a mid-pandemic snapshot of Canadian employment opportunities and present a public web application that provides an interactive visual interface that summarizes our findings for the general public and the broader research community. © 2022 IEEE.
ABSTRACT
Attendance system has evolved along with the industrial revolution that now has reached a new era. Furthermore, nowadays with the struck of COVID-19, the government issued a prohibition policy towards companies to urge their employees to work from home (WFH). Several issues have come across the Human Resources Development (HRD) manager regarding this policy. Employees' taking vacation when they are obliged to work from home and employees' faking their attendance are two of many problems regarding this policy. Hence, this study aims to design a relevant attendance system model that will overcome these problems with the integration of face recognition and geolocation through mobile platform. The study built the proposed attendance system with the approach of User-Centered Design (UCD) methodology. The findings within the study shows that above 80% of participants are satisfied with the proposed model and are ready to implement the designed system in their organizations respectively. © 2021 IEEE.
ABSTRACT
Wearable gadgets, portable mHealth applications, and geo-location advancements have the capacity to track, screen, and report information related to COVID-19 pandemic. These developments make an “associated wellbeing,” where people gather information outside of the medical care experience and acknowledge caretakers for it. Assortment of this PGHD or Patient Generated Health Data can possibly sway conveyance of medical care through distant observing, and by permitting patients and medical care groups to give focused on and productive consideration that lines up with the wellbeing status of individual patients. We examine the idea of a participatory computerized contact notice way to deal with help following of contacts who are presented to affirmed instances of Covid infection (COVID-19);It includes two types of approaches;one is based on apps and other on data collection. The proposed tool fills in as a supplemental agreement following way to deal with check the scarcity of medical services staff. To comprehend the worth and boundaries related to clinical reconciliation of PGHD, this study took data of stakeholders, looking at their viewpoints and encounters of PGHD use. Moreover, this research looked to exhibit the utilization of a cell phone and tablet application that upholds PHR (patient health records) based wellbeing perception by coordinating checking capacities explicit to Coronavirus. Interpreting progresses in innovation and data following into effective clinical usage requires seeing how stakeholders conceptualize and utilize PGHD, the potential worth that PGHD can add to mind, and the difficulties that may restrict PGHD's guarantee. Results represent the worth and difficulties related to health-framework execution of PGHD. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
COVID-19 is an infectious disease caused by the newly discovered coronavirus. With the number of detected cases increasing exponentially each day, it is very challenging to track the movements of an infected person for the duration of the incubation period of the virus. As there is no active vaccination that has been developed yet, it has become a huge concern globally to mitigate the effects of this virus. Countries across the world have been looking for ways to prevent the spread of this disease. There are various precautionary measures implemented worldwide, but this might not be very effective in the future for containing the transmission of the virus. This necessitates the development of a technique which can help track the whereabouts of the infected person and trace the spread of the virus. The aim of this paper is to discuss our progressive Web application (PWA) which compares and matches the user’s data with the data of a patient who has tested positive for COVID-19. This PWA uses geolocation to find the real-world geographic location of a person without Bluetooth and GPS turned on at all times. It alerts a user if they have come in contact with a COVID-positive patient in any particular place at any specific instance of time to take the necessary precautions. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.