A survey of automatic contact tracing approaches using bluetooth low energy

To combat the ongoing Covid-19 pandemic, many new ways have been proposed on how to automate the process of finding infected people, also called contact tracing. A special focus was put on preserving the privacy of users. Bluetooth Low Energy as base technology has the most promising properties, so this survey focuses on automated contact tracing techniques using Bluetooth Low Energy. We define multiple classes of methods and identify two major groups: systems that rely on a server for finding new infections and systems that distribute this process. Existing approaches are systematically classified regarding security and privacy criteria.Copyright © 2021 ACM.

Crowding on public transport using smart card data during the COVID-19 pandemic: New methodology and case study in Chile.

Most crowding measures in public transportation are usually aggregated at a service level. This type of aggregation does not help to analyze microscopic behavior such as exposure risk to viruses. To bridge such a gap, our paper proposes four novel crowding measures that might be well suited to proxy virus exposure risk at public transport. In addition, we conduct a case study in Santiago, Chile, using smart card data of the buses system to compute the proposed measures for three different and relevant periods of the COVID-19 pandemic: before, during, and after Santiago's lockdown. We find that the governmental policies diminished public transport crowding considerably for the lockdown phase. The average exposure time when social distancing is not possible passes from 6.39 min before lockdown to 0.03 min during the lockdown, while the average number of encountered persons passes from 43.33 to 5.89. We shed light on how the pandemic impacts differ across various population groups in society. Our findings suggest that poorer municipalities returned faster to crowding levels similar to those before the pandemic.

COUNTERSAVIOR: AIoMT and IIoT-Enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics

In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject's cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.

An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management

Many highly contagious infectious diseases, such as COVID-19, monkeypox, chickenpox, influenza, etc., have seriously affected or currently are seriously affecting human health, economic activities, education, sports, and leisure. Many people will be infected or quarantined when an epidemic spreads in specific areas. These people whose activities must be restricted due to the epidemic are represented by targets in the article. Managing targets by using targeted areas is an effective option for slowing the spread. The Centers for Disease Control (CDC) usually determine management strategies by tracking targets in specific areas. A global navigation satellite system (GNSS) that can provide autonomous geospatial positioning of targets by using tiny electronic receivers can assist in recognition. The recognition of targets within a targeted area is a point-in-polygon (PtInPy) problem in computational geometry. Most previous methods used the method of identifying one target at a time, which made them unable to effectively deal with many targets. An earlier method was able to simultaneously recognize several targets but had the problem of the repeated recognition of the same targets. Therefore, we propose a GNSS coordinate recognition algorithm. This algorithm can efficiently recognize a large number of targets within a targeted area, which can provide assistance in epidemic management. © 2023 by the authors.

Research on KNN-Based GNSS Coordinate Classification for Epidemic Management

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.

Tracking Changes in Mobility Before and After the First SARS-CoV-2 Vaccination Using Global Positioning System Data in England and Wales (Virus Watch): Prospective Observational Community Cohort Study.

BACKGROUND: Evidence suggests that individuals may change adherence to public health policies aimed at reducing the contact, transmission, and spread of the SARS-CoV-2 virus after they receive their first SARS-CoV-2 vaccination when they are not fully vaccinated. OBJECTIVE: We aimed to estimate changes in median daily travel distance of our cohort from their registered addresses before and after receiving a SARS-CoV-2 vaccine. METHODS: Participants were recruited into Virus Watch starting in June 2020. Weekly surveys were sent out to participants, and vaccination status was collected from January 2021 onward. Between September 2020 and February 2021, we invited 13,120 adult Virus Watch participants to contribute toward our tracker subcohort, which uses the GPS via a smartphone app to collect data on movement. We used segmented linear regression to estimate the median daily travel distance before and after the first self-reported SARS-CoV-2 vaccine dose. RESULTS: We analyzed the daily travel distance of 249 vaccinated adults. From 157 days prior to vaccination until the day before vaccination, the median daily travel distance was 9.05 (IQR 8.06-10.09) km. From the day of vaccination to 105 days after vaccination, the median daily travel distance was 10.08 (IQR 8.60-12.42) km. From 157 days prior to vaccination until the vaccination date, there was a daily median decrease in mobility of 40.09 m (95% CI -50.08 to -31.10; P<.001). After vaccination, there was a median daily increase in movement of 60.60 m (95% CI 20.90-100; P<.001). Restricting the analysis to the third national lockdown (January 4, 2021, to April 5, 2021), we found a median daily movement increase of 18.30 m (95% CI -19.20 to 55.80; P=.57) in the 30 days prior to vaccination and a median daily movement increase of 9.36 m (95% CI 38.6-149.00; P=.69) in the 30 days after vaccination. CONCLUSIONS: Our study demonstrates the feasibility of collecting high-volume geolocation data as part of research projects and the utility of these data for understanding public health issues. Our various analyses produced results that ranged from no change in movement after vaccination (during the third national lock down) to an increase in movement after vaccination (considering all periods, up to 105 days after vaccination), suggesting that, among Virus Watch participants, any changes in movement distances after vaccination are small. Our findings may be attributable to public health measures in place at the time such as movement restrictions and home working that applied to the Virus Watch cohort participants during the study period.

How Different Predominant SARS-CoV-2 Variants of Concern Affected Clinical Patterns and Performances of Infected Professional Players during Two Soccer Seasons: An Observational Study from Split, Croatia.

There are limited data describing clinical patterns and match running performance (MRP) among players with COVID-19 infection before and after infection, particularly related to different predominant SARS-CoV-2 variants, as well as in comparison to uninfected players. This observational study was conducted during two consecutive soccer seasons in one professional club in Split, Croatia. There were four clusters of mild, self-limited, or asymptomatic infection characterised by low adherence to preventive measures. Infected players had significantly more symptoms (t-test = 3.24; p = 0.002), a longer period of physical inactivity (χ2 = 10.000; p = 0.006) and a longer period of self-assessment for achieving full fitness (χ2 = 6.744; p = 0.034) in the 2020-2021 season (Wuhan wild strain and Alpha variant) than in the 2021-2022 season (Omicron variant). It was also found that, despite the milder clinical presentation of the infection in the 2021-2022 season, the players had significantly more abnormal laboratory findings (χ2 = 9.069240; p = 0.002), although without clinical significance at the time of the study. As for the MRP, player performance in the 2021-2022 season was not negatively affected by the Omicron variant, while there was an improvement in MRP in scores for a sample of all players. The RTP protocol was correctly applied because it helped the athletes to recover their pre-infection physical capacities relatively quickly. This study advances the understanding that an optimally and individually planned RTP protocol is crucial for the MRP of infected players. Future research needs to replicate the findings of abnormal laboratory results and extend the study focusing on their potential long-term clinical significance.

Towards assisting visually impaired individuals: A review on current status and future prospects

Visually impaired people require support with regular tasks including navigating, detecting obstacles, and maintaining safety, especially in both indoor and outdoor environments. As a result of the advancement of assistive technology, their lives have become substantially more convenient. Here, cutting-edge assistive devices and technologies for the visually impaired are reviewed, along with a chronology of their evolution. These methodologies are classified according to their intended applications. The taxonomy is combined with a description of the tests and experiments that can be used to examine the characteristics and assessments of assistive technology. In addition, the algorithms used in assistive devices are examined. This paper looks at solar industry innovations and promotes using renewable energy sources to create assistive devices, as well as, addresses the sudden advent of COVID-19 and the shift in the development of assistive devices. This review can serve as a stepping stone for further research on the topic. Copyright © 2022 The Author(s)

TerraVet: A Mobile and Web Application Framework for Pet Owners and Veterinary Clinic

The lives of pet animals are equally essential as how a human life. Pet owners and the veterinarian are responsible for providing good welfare for pets despite the problems. However, the rise of COVID-19 temporarily disturbs the veterinary services where some of them limit or stop their operations, resulting in the absence and difficulties for the pet owners to locate the available veterinarian, especially when there is an immediate need for treatment, vaccination, or consultation. Aside from that, setting an appointment and buying the pet's needs are seen to be a problem with regards to the situation since most of the pet owners are afraid to go outside because they might be infected with the virus. In line with this, TerraVet: A Mobile and Web Application Framework for Veterinary Clinics and Pet Owners is proposed to resolve the underlying dilemmas in administering and facilitating veterinary care. The main objective of this suggested project is to develop and design a platform where pet owners may locate their nearby veterinarian using the Global Positioning System (GPS) technology. In addition, the application enables the pet owner to arrange an appointment, product reservation, and online consultation. The veterinary clinic may post details regarding their offered services, products, and medicines. TerraVet will also design an electronic pet card to monitor their health status. © 2022 ACM.

STRENUOUS: Edge-Line Computing, AI, and IIoT Enabled GPS Spatiotemporal Data-Based Meta-Transmission Healthcare Ecosystem for Virus Outbreaks Discovery

COVID-19 is not the last virus;there would be many others viruses we may face in the future. We already witnessed the loss of economy and daily life through the lockdown. In addition, vaccine, medication, and treatment strategies take clinical trials, so there is a need to tracking and tracing approach. Suitably, exhibiting and computing social evolution is critical for refining the epidemic, but maybe crippled by location data ineptitude of inaccessibility. It is complex and time consuming to identify and detect the chain of virus spread from one person to another through the terabytes of spatiotemporal GPS data. The proposed research aims an HPE edge line computing and big data analytic supported virus outbreak tracing and tracking approach that consumes terabytes of spatiotemporal data. The proposed STRENUOUS system discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus-like COVID-19 epidemic transmission. The method and the mechanical assembly further contained an alert component to demonstrate a suspected case if there was a potential exposure with the confirmed subject. The proposed system tracks location data related to a suspected subject in the confirmed subject route, where the location data expresses one or more geographic locations of each user over a period. It recognizes a subcategory of the suspected subject who is expected to transmit a contagion based on the location data. System measure an exposure level of a carrier to the infection based on contaminated location data and a subset of carriers connected with the second location carrier. They investigated whether the people in the confirmed subject's cross-path can be infected and suggest quarantine followed by testing. The proposed STRENUOUS system produces a report specifying that the people have been exposed to the virus.

Location Contact Tracing: Penetration, Privacy, Position, and Performance

The 2020 COVID-19 pandemic radically changed the world and how people interact, move, and behave. Following a lockdown that was imposed worldwide, although with different timing, Mobile Contact Tracing Apps (MCTAs) were proposed to digitally trace contacts between individuals while gradually releasing mobility constraints mandated to contain the spread of disease. General concern for privacy regarding the use of GPS data shifted the efforts toward distributed applications, which use Bluetooth technology to trace proximity and potential infections. Nonetheless, GPS data would help more health operators to understand where hotbeds are and to what extent the spread is progressing and at what pace. In addition to these issues, in this work we take a closer look at the major pillars of MCTA: Penetration, Privacy, Position, and Performance. We focus on (i) how the penetration rate affects the ability of a tracing application to work;(ii) the proposal of a novel method of tracing, which builds on the GPS technology;(iii) how the position of infections is beneficial to rapidly reduce the infection;and (iv) the discussion of the effects of such paradigms in different scenarios. © 2022 Association for Computing Machinery.

A Mobile Based Garbage Collection System

Garbage disposal and collection is an ongoing global crisis amplified by the increasing world population, lack of funds and public awareness, and recently because of the Covid-19 pandemic. Information Technology can be utilized as a solution for the existing garbage collection methods that are old-fashioned, time-consuming, and energy-consuming due to the lack of a unified and consistent system that incorporates all the parties involved in garbage production and collection. A mobile-based garbage collection system is proposed to overcome the issues aforementioned through route and schedule optimization, AI chatbot, and optimized GPS tracking. The route and schedule optimization is achieved through vehicle routing problem with time windows(VRPTW) with synchronization and precedence that was optimized using LNS;the total travel cost went from 172 minutes to 144 minutes. The AI chatbot feature facilitates reporting garbage collection issues and complaints and enquiring about waste management tips (reduce, recycle, and reuse tips) to be used at home. The most prominent role of developing this AI chatbot is replacing the manual process of reporting garbage collection issues in Sri Lanka with an efficient and interactive way. The chatbot has waste management tips Q and A. In Optimized GPS Tracking, the user can use the map to find the nearest garbage disposal place based on the type of rubbish they generate. The truck driver can find the optimal path to the closest current garbage disposal centres and public trash bins and view the location of Homeowners on the map. The optimized path between two points is displayed based on distance, time, and fuel consumption. The main goal of the component is to show the location of garbage disposal bins and the optimal paths for truck drivers using Linear regression and the Node2vec algorithm. © 2022 IEEE.

COVID Hotspot Alert System using Geo-Fencing Technique

Covid-19 is an extremely communicable disease. It becomes extremely hard to control once it begins to spread. One of the most important and effective steps to break the chain and keep healthy people from getting infected is social isolation/distancing. When an infected person comes into contact with a healthy person, that person becomes infected as well, and the chain reaction continues. To curb this, COVID alert system using geo-fencing is developed. This system uses a GPS module to create a Geo Fence around the infected area and the healthy area. The live/current GPS location/coordinate is compared with the hotspot co-ordinates. The GSM module with Sim800L will send an alert to healthy people when they come into contact with virus-infected areas. The device comes with a GPS, GSM module with Sim800L and an OLED which displays the alert message. The device can be fit into any public or private transport, so that the healthy person will be prevented from entering the hotspot zones unnecessarily, thereby blocking the virus spread. © 2022 IEEE.

An IoT-Based Wristband for Automatic People Tracking, Contact Tracing and Geofencing for COVID-19.

The coronavirus disease (COVID-19) pandemic has triggered a huge transformation in the use of existing technologies. Many innovations have been made in the field of contact tracing and tracking. However, studies have shown that there is no holistic system that integrates the overall process from data collection to the proper analysis of the data and actions corresponding to the results. It is critical to identify any contact with infected people and to ensure that they do not interact with others. In this research, we propose an IoT-based system that provides automatic tracking and contact tracing of people using radio frequency identification (RFID) and a global positioning system (GPS)-enabled wristband. Additionally, the proposed system defines virtual boundaries for individuals using geofencing technology to effectively monitor and keep track of infected people. Furthermore, the developed system offers robust and modular data collection, authentication through a fingerprint scanner, and real-time database management, and it communicates the health status of the individuals to appropriate authorities. The validation results prove that the proposed system identifies infected people and curbs the spread of the virus inside organizations and workplaces.

Geodesic Embedded System for Real-Time Tracking and Communication Offline

Nowadays, hybrid (air and land) systems are applied for solving syndemic problems, which have caused millions of deaths by COVID-19. In support to military and medical communications with tracking in real-time, public, or private security of objects and people, we propose a multiplatform Geodesic Embedded System for Real-Time Tracking and communication offline. The Middleware was built with Encrypted Global Navigation Satellite System (GNSS) and DB async replication. It was built under a client-server architecture of multi-Tiers and logical multilayers, was developed with Nodejs-Express-Angular with Object Relational Mapping and Data Transfer Object with PostgreSQL. Generally, tracking systems with GNSS technology are slow without a base architecture and adequate development tools and require an internet connection in some of their stages. The proposed system was tested with other applications;it used Geoserver and Dockers in two different environments. The results showed its functionality in different situations applied in problems of aeronautic and terrestrial tracking, guaranteeing a projection of personalized geodetic maps, and OpenStreetMap used in an agile way, efficient, and secure communication in real-time. The proposed architecture allows native development, integration of new modules, and cross-platform implementation in an easy way, and a cellular transceiver with 94.6% of efficiency. The proposed architecture, permit to use the application in different platforms with the same configuration. [ FROM AUTHOR]

New page of agriculture: On the view of 5G generation and GPS

The coronavirus outbreak emphasizes the potential crisis of traditional agriculture, weak resilience to natural disasters and natural resources, and highly relies on the human workforce. 5G is the fifth generation of the mobile network with higher speed and bigger capacity. GPS is the position and timing operation system. Based on secondary research and content analysis, this review paper concluded that it is possible for a sustainable agriculture era by implementing 5G and GPS and other related sophisticated technologies. The smart agriculture scheme can maximize the economic benefits of agriculture, maximize agricultural production, and minimize the cost and environmental effects. However, the threats and security risks of 5G and GPS performing in agriculture are also important topics left for future researchers. © 2022 IEEE.

A warehouse of information, COVID HUB

A most life-threatening situation caused by a novel coronavirus has been unfolding over the last 2years at breakneck speed. The general public is desperate to learn more about how the current situation with COVID-19 is unfolding, including statistics on the number of people who have been affected, as well as news about deaths, recovery rates, and the various ages of people who have been affected across India, particularly those with disabilities and the elderly. This chapter is intended to combine the COVID-19 demand and proceeds with the available statistics onto a single platform. The virus has spread across the globe, and much of the information in the public domain is not accurate. It also affects people adversely if information is unreliable, and so there should be dependable and trustworthy data that individuals can use and rely on in such perilous situations. COVID HUB, a valuable smart application, will provide the public with the accurate information that they deserve. This suggested handy application includes data on the number of people impacted, death rates, recovery rates, and the numbers of elderly and differently abled people affected. The information sources for this new smart app are observed through the application program interface, which obtains data from a remote source, maps its key points and values, and aligns them in a data center. Once the app is started automatically by the system, the application program interface call is made. If COVID symptoms are detected in one or more of the neighboring states, this function collects all of the phone numbers to be dialed from each state. Dart is used for implementation of this COVID HUB. Flutter, which supports both Android and iOS, as well as being a tool chain, coding platform, SDK management, and Dart extensions, are all included in this implementation. © 2022 Elsevier Inc. All rights reserved.

Simple indicators of crime and police: How big data can be used to reveal temporal patterns

This study demonstrates how temporal summary statistics can be a guiding tool for big data analyses to unravel temporal patterns of crime and police presence. Simple indicator statistics were used to identify temporal clusters of crimes and police presence, and to investigate potential links between the two. The methodology was applied on an anonymized police database, including reported crime events and police presence data, from a medium-sized European police department. The results illustrated that certain crime types occurred more during the day (e.g., burglaries), while others were more prevalent at night (e.g., drug crimes, motorbike and car theft). Police presence showed dispersed temporal patterns and little temporal focus on any type of crime. The research shows that temporal summary statistics can be used to support an explorative analysis of big datasets and guide subsequent spatiotemporal analyses of crime and police data. The summary statistics offer an accessible approach to analysing extensive datasets of policing activity and improving evidence-based policing strategies.

A Cloud Based Intelligent Model for Predicting the Pre-Testing Corona Virus Infection Outbreak

Corona virus disease (COVID) is a transmittable disease caused by a newly discovered corona virus. For this a system is require which trace the location and predict the health of the people. In the present study, a cloud based a model is proposed. The proposed model will be connect with a cloud computing system that will predict the corona virus infected patients using naïve bayes classifier and provides geographic based danger areas to prevent the spreading of corona virus. This way will provide the great help to the local administration and health care agencies to control the spreading of covid. © 2022 IEEE.

EasyBand2.0: A Framework with Context-Aware Recommendation Mechanism for Safety-Aware Mobility during Pandemic Outbreaks

COVID-19 (Corona Virus Disease 2019) is a pandemic which has been spreading exponentially around the globe. Many countries had adopted stay-at-home or lockdown policies to control its spreading. However, prolonged stay-at-home can cause worse effects like economic crises, unemployment, food scarcity, and mental health problems of individuals. EasyBand2.0 is a wearable personal safety device that helps in social distancing and also helps in safe mobility. Under the IoMT (Internet of Medical Things) framework the wearable EasyBand2.0 device helps in social distancing, it avoids human-to-human contact and helps maintain a safer distance. EasyBand2.0 uses the Low Power BLE technology to sense distance between two user devices and alert them based on the distance and time spent in proximity. Safe mobility of people is also important as travel is resumed in all forms. This paper proposes a software application along with the easy band to further be integrated with a system that works based on GPS (Global Positioning System) or GIS (Geographic Information System) to provide travel logging for contact tracing without exposing personal data. A CARS (Context Aware Recommendation System) based safe zone recommender system is proposed in this paper to aid safe mobility. © 2022 IEEE.