Mobile Surveillance Research Articles

Mobile Phone Syndromic Surveillance for Respiratory Conditions in an Emergency (COVID-19) Context in Colombia: Representative Survey Design.

Syndromic surveillance for respiratory infections such as COVID-19 is a crucial part of the public health surveillance toolkit as it allows decision makers to detect and prepare for new waves of the disease in advance. However, it is labor-intensive, costly, and increases exposure to survey personnel. This study assesses the feasibility of conducting a mobile phone-based respiratory syndromic surveillance program in a middle-income country during a public health emergency, providing data to support the inclusion of this method in the standard infection control protocols at the population level. This study aims to assess the feasibility of a national active syndromic surveillance system for COVID-19 disease in Colombia. In total, 2 pilots of syndromic mobile phone surveys (MPSs) were deployed using interactive voice response technology in Colombia (367 complete surveys in March 2022 and 451 complete surveys in April and May 2022). Respondents aged 18 years and older were sampled using random digit dialing, and after obtaining consent, they were sent a 10-minute survey with modules on sociodemographic status, respiratory symptoms, past exposure to COVID-19 infection and vaccination status, preferences about COVID-19 vaccination, and information source for COVID-19. Pilot 1 used a nationally representative sample while pilot 2 used quota sampling to yield representative results at the regional level. In this work, we assessed the performance characteristics of the survey pilots and compared the demographic information collected with a nationally representative household survey. For both pilots, contact rates were between 1% and 2%, while participation rates were above 80%. The results revealed that younger, female, and higher educated participants were more likely to participate in the syndromic survey. Survey rates as well as demographics, COVID-19 vaccination status, and prevalence of respiratory symptoms are reported for both pilots. We found that respondents of the MPSs are more likely to be younger and female. In a COVID-19 pandemic setting, using an interactive voice response MPS to conduct syndromic surveillance may be a transformational, low-risk, and feasible method to detect outbreaks. This evaluation expects to provide a path forward to the inclusion of MPSs as a traditional surveillance method.

Social sorting in Europe: Self-censorship in a digital asylum

In recent decades, global mobility control and digital surveillance measures have increasingly prioritised affective aspects and perception-based policies. However, these practices encounter resistance, particularly in the everyday use of connected migrants. Through qualitative data analysis within the tradition of critical surveillance studies, this paper investigates how marginalised mobile groups – often labelled suspects of terrorism and organised crime – circumvent mobile surveillance and social sorting mechanisms within and beyond Fortress Europe. Rising tech literacy and surveillance awareness among users challenge digital policing, reshaping interactions between suspected newcomers and border control authorities. While existing studies focus on countersurveillance activities, less attention is given to strategic “silences” and social filters used to evade profiling and sorting mechanisms, protecting those who fear the risks of crossing a border. Based on notions of secure connectivity, this research employs a multi-site analysis of refugee polymedia use to examine countersurveillance strategies and digital self-censorship practices in transit countries.

A Mobile Surveillance System with Face Detection and Recognition Abilities

This paper proposes a mobile surveillance system similar to an electronic dog which has the ability to move around a certain location all while detecting and recognizing the individuals and notifying the respective authorities about the detected person. The system uses an Arduino UNO microcontroller along with IR receiver and remote to function as a remote-controlled vehicle while also providing an option for automatic movement of the vehicle. The system also has an integrated camera that constantly feeds live video to a computer system. Haar-cascades take in the frames of videos and detect appearance of human face. Then, pretrained FaceNet Convolutional Neural Network (CNN) compares the detected face to the authorized faces in an attempt to recognize the face. The system also boasts the feature of email notification for which it employs the SMTP protocol to send an email with the detected face to the authorized personnel. The methods and techniques used to implement these features are presented in depth in this paper.

A dynamic analysis of UAV -based IoT networks for efficient communication using falcon optimization approach

Unmanned Aerial Vehicles (UAVs) play a pivotal role in Internet of Things (IoT) applications by sensing data, conducting continuous surveillance, and maintaining connectivity among users. However, their high mobility and continuous surveillance for data transmission result in high energy consumption, possibly resulting in lesser network performance, including link failure, node depletion, data loss, and high delays. To address these challenges, a novel scheme termed as 'On-Demand Cross-Layered Falcon Optimization Approach (OCLFO)' has been proposed to enhance connectivity and optimize network performance in UAV-based IoT systems. The on- demand Cross-Layer Optimization (CLO) scheme facilitates data transmission across each layer of the OSI model. CLO is applied to the design of UAVs with the integration of on-demand, based on the queuing size. Falcon Optimization Approach (FOA) has been integrated with the CLO to overcome the energy consumption drawback, and provide successful data transmission between UAV users with the help of fitness value. The fitness value depends maximum on the energy and minimum on the queuing size, and this provides the energy efficient routing path for an efficient data transmission. The proposed OCLFO aims to optimize energy consumption while ensuring successful and expedited data transmission among UAV users. Simulations have been conducted using a network simulator to analyse Quality of Service (QoS) parameters, considering high mobility scenarios by varying energy levels such as 100 J and 200 J. Compared to the existing optimization algorithms, the proposed OCLFO achieves high delivery ratio, high throughput, less delay, lesser energy consumption and enhanced link efficiency.

Implementation of a photovoltaic system for charging mobile devices and video surveillance in the community of Cuicuno, Latacunga, Ecuador

Implementation of a photovoltaic system for charging mobile devices and video surveillance in the community of Cuicuno, Latacunga, Ecuador

Harnessing technology for infectious disease response in conflict zones: Challenges, innovations, and policy implications.

Epidemic outbreaks of infectious diseases in conflict zones are complex threats to public health and humanitarian activities that require creativity approaches of reducing their damage. This narrative review focuses on the technology intersection with infectious disease response in conflict zones, and complexity of healthcare infrastructure, population displacement, and security risks. This narrative review explores how conflict-related destruction is harmful towards healthcare systems and the impediments to disease surveillance and response activities. In this regards, the review also considered the contributions of technological innovations, such as the improvement of epidemiological surveillance, mobile health (mHealth) technologies, genomic sequencing, and surveillance technologies, in strengthening infectious disease management in conflict settings. Ethical issues related to data privacy, security and fairness are also covered. By advisement on policy that focuses on investment in surveillance systems, diagnostic capacity, capacity building, collaboration, and even ethical governance, stakeholders can leverage technology to enhance the response to infectious disease in conflict settings and, thus, protect the global health security. This review is full of information for researchers, policymakers, and practitioners who are dealing with the issues of infectious disease outbreaks in conflicts worn areas.

Operationalising ‘One Health’ through Primary Healthcare Approach

Abstract The countries worldwide have recognised the importance of the One Health (OH). This review examines the need and potential for the integration of OH into Primary Health Care (PHC) Approach. One Health through PHC has been implemented in many. Successful initiatives include mobile phone-based surveillance for zoonotic diseases in Kenya, innovative vaccination campaigns in Chad and measures to combat antimicrobial resistance in several nations. Collaboration and partnerships play a pivotal role in overcoming challenges such as resource constraints, policy alignment and limited intersectoral collaboration. The review emphasises integrating OH in primary healthcare so that the healthcare providers can contribute to addressing complex global health challenges and promoting the well-being of individuals, animals and the environment. The sustainable strategies, capacity building, advocacy and technological innovations to effectively implement the OH approach through PHC, ultimately improving health outcomes and ensuring a sustainable planet.

VIDEO SURVEILLANCE ON THE DRILLING RIG

The article describes a mobile video surveillance system on a drilling rig, which is designed to carry out technological control in high-risk areas at oil and gas producing enterprises. The advantages of this complex, circuit design solutions and functional properties of the device are presented. The field kit is installed directly at the facility in the hazardous area that needs to be monitored. The Polar Eye video surveillance system is designed to meet a number of industrial safety requirements at oil and gas production facilities. Video information from the danger zone is transmitted using the BSPD system, operating in the unlicensed 2.4 GHz frequency range, to the safe zone, where the analytical center for visual and operational control is located.The most important competitive advantages of the complex – low weight, compactness and mobility – were achieved by developing their own layout and circuit design solutions and by implementing most of the nodes on printed circuit boards of their own production.

Reinforcement learning for optimizing real-time interventions and personalized feedback using wearable sensors

Many countries have seen a steady increase in lifetime duration through the past many years as a result of notable advancements in ecological and individual cleanliness, general health, including healthcare. Hence, it is anticipated that rising life duration and declining birth levels will result in a sizable aging population in the foreseeable future, placing a heavy strain on such nations' societal structures. For the benefit of senior medical and wellness, it is crucial to create affordable, user-friendly technologies. Older people can remain in their cozy homes rather than costly medical centers due to mobile health surveillance, which depends upon portable, non-intrusive devices, and actuators, as well as contemporary interaction and data innovations. It is an operational and economical approach. This work investigates how electronic devices can be used to optimize in-the-moment actions and deliver customized feedback using reinforced learning. By utilizing the information obtained from such sensors, the suggested structure dynamically modifies solutions according to each individual's reaction, increasing the efficacy of customized input. The reinforced method of learning optimizes the course of action for a variety of circumstances by interactively improving its tactics. By introducing an innovative strategy to improve immediate responses and provide customized input, this study advances the nascent field of wireless technologies as well as artificial learning and eventually raises the effectiveness of customized healthcare and well-being apps.

Unconventional data, unprecedented insights: leveraging non-traditional data during a pandemic.

The COVID-19 pandemic prompted new interest in non-traditional data sources to inform response efforts and mitigate knowledge gaps. While non-traditional data offers some advantages over traditional data, it also raises concerns related to biases, representativity, informed consent and security vulnerabilities. This study focuses on three specific types of non-traditional data: mobility, social media, and participatory surveillance platform data. Qualitative results are presented on the successes, challenges, and recommendations of key informants who used these non-traditional data sources during the COVID-19 pandemic in Spain and Italy. A qualitative semi-structured methodology was conducted through interviews with experts in artificial intelligence, data science, epidemiology, and/or policy making who utilized non-traditional data in Spain or Italy during the pandemic. Questions focused on barriers and facilitators to data use, as well as opportunities for improving utility and uptake within public health. Interviews were transcribed, coded, and analyzed using the framework analysis method. Non-traditional data proved valuable in providing rapid results and filling data gaps, especially when traditional data faced delays. Increased data access and innovative collaborative efforts across sectors facilitated its use. Challenges included unreliable access and data quality concerns, particularly the lack of comprehensive demographic and geographic information. To further leverage non-traditional data, participants recommended prioritizing data governance, establishing data brokers, and sustaining multi-institutional collaborations. The value of non-traditional data was perceived as underutilized in public health surveillance, program evaluation and policymaking. Participants saw opportunities to integrate them into public health systems with the necessary investments in data pipelines, infrastructure, and technical capacity. While the utility of non-traditional data was demonstrated during the pandemic, opportunities exist to enhance its impact. Challenges reveal a need for data governance frameworks to guide practices and policies of use. Despite the perceived benefit of collaborations and improved data infrastructure, efforts are needed to strengthen and sustain them beyond the pandemic. Lessons from these findings can guide research institutions, multilateral organizations, governments, and public health authorities in optimizing the use of non-traditional data.

Tackling the resurgence of monkeypox in Africa: challenges and strategies for eradication

Monkeypox is a zoonotic viral disease previously limited to isolated regions of Central Africa. The reasons for resurgence of the disease in Africa are multifaceted, including waning herd immunity acquired from smallpox vaccination, the evolution of the virus, disruption of health systems by the COVID-19 pandemic, and lack of adequate global funding for monkeypox research and intervention commodities in African countries. Policymakers should regulate the distribution of African rodents, and healthcare workers must be trained in monkeypox surveillance and case definition. The adoption of the mobile digital Surveillance Outbreak Response Management and Analysis System (SORMAS) is recommended to improve disease surveillance and outbreak management. African governments must collaborate with global leaders and pharmaceutical industries to develop and distribute monkeypox vaccines and specific therapeutics. These measures are crucial in preventing and managing monkeypox outbreaks.

Multi-Objective Path Planning of Autonomous Underwater Vehicles Driven by Manta Ray Foraging

Efficient navigation of multiple autonomous underwater vehicles (AUVs) plays an important role in monitoring underwater and off-shore environments. It has encountered challenges when AUVs work in complex underwater environments. Traditional swarm intelligence (SI) optimization algorithms have limitations such as insufficient path exploration ability, susceptibility to local optima, and difficulty in convergence. To address these issues, we propose an improved multi-objective manta ray foraging optimization (IMMRFO) method, which can improve the accuracy of trajectory planning through a comprehensive three-stage approach. Firstly, basic model sets are established, including a three-dimensional ocean terrain model, a threat source model, the physical constraints of AUV, path smoothing constraints, and spatiotemporal coordination constraints. Secondly, an innovative chaotic mapping technique is introduced to initialize the position of the manta ray population. Moreover, an adaptive rolling factor “S” is introduced from the manta rays’ rolling foraging. This allows the collaborative-vehicle population to jump out of local optima through “collaborative rolling." In the processes of manta ray chain feeding and manta ray spiral feeding, Cauchy reverse learning is integrated to broaden the search space and enhance the global optimization ability. The optimal Pareto front is then obtained using non-dominated sorting. Finally, the position of the manta ray population is mapped to the spatial positions of multi-AUVs, and cubic spline functions are used to optimize the trajectory of multi-AUVs. Through detailed analysis and comparison with five existing multi-objective optimization algorithms, it is found that the IMMRFO algorithm proposed in this paper can significantly reduce the average planned path length by 3.1~9.18 km in the path length target and reduce the average cost by 18.34~321.872 in the cost target. In an actual off-shore measurement process, IMMRFO enables AUVs to effectively bypass obstacles and threat sources, reduce risk costs, and improve mobile surveillance safety.

Oncologic treatment support via adedicated mobile app: aprospective feasibility evaluation (OPTIMISE-1).

Mobile health (mhealth) is gaining interest, with mobile devices and apps being ever more available among medical facilities and patients. However, in the field of radiation oncology, the medical benefits of mhealth apps are still underexplored. As an additional approach to patient care during radiotherapy, we designed amobile treatment surveillance app based on patient-reported outcomes. We aimed to examine the feasibility of app-based treatment surveillance in patients undergoing radiotherapy (RT). Alongside technical practicability and acceptance, we assessed patient satisfaction and quality of life during treatment. This prospective single-center study was performed at Heidelberg University Hospital between August 2018 and January 2020. During RT we measured patients' quality of life, symptoms, and treatment satisfaction. Respective questionnaires (EORTC QLQ-C30 with diagnosis-specific modules, RAND PSQ-18) were presented to patients via amobile app running on adesignated tablet device. The primary endpoint was determined by the fraction of patients who completed at least 80% of the items. Secondary endpoints were disease-related quality of life and patient satisfaction. A total of 49cancer patients (14breast, 13pelvic, 12lung, 10prostate) were eligible for analysis. 79.6% (95% confidence interval: 66.4-88.5%; n = 39) of all patients completed at least 80% of the items received by the mobile app. Amean of 227.5 ± 48.25 questions were answered per patient. Breast cancer patients showed the highest rate of answered questions, with 92.9% (n = 13) completing at least 80% of the items. Patients showed high acceptance, with 79.6% (n = 39) completing at least 80% of the given items. The use of amobile app for reporting symptoms and quality of life during RT is feasible and well accepted by patients. It may allow for resource-efficient, detailed feedback to the medical staff and assist in the assessment of side effects over time.

Routini Al Yawmi: exploring taboo, risk, and the erotic gaze across Moroccan (non)digital landscapes

ABSTRACT This paper dwells into the sensitive topic of eroticism and sexuality within Islam, with a specific focus on contemporary Morocco. It explores the challenges faced by Muslims in openly discussing these subjects due to cultural sensitivity. The study investigates the impact of eroticism on Moroccan society, considering encounters with the West, media influence, cinematic and technological interactions, and responses to global risks. The analysis is structured around three distinct historical moments: “composition,” which examines the colonial-era constructions of eroticism in meticulously staged representations; “decomposition,” which explores the proliferation of erotic imagery and the subsequent disintegration of long-held Islamic notions of shame; and “re-composition,” which signifies a shift in power dynamics within spectatorship and the active manipulation of visual content by “my daily routine” (MDR) female creators. By drawing on Sartre’s concept of keyhole eroticism, the paper connects the erotic gaze and shame to modern practices of mobile phone-mediated surveillance.

Community transmission of SARS-CoV-2 during the Delta wave in New York City

BackgroundUnderstanding community transmission of SARS-CoV-2 variants of concern (VOCs) is critical for disease control in the post pandemic era. The Delta variant (B.1.617.2) emerged in late 2020 and became the dominant VOC globally in the summer of 2021. While the epidemiological features of the Delta variant have been extensively studied, how those characteristics shaped community transmission in urban settings remains poorly understood.MethodsUsing high-resolution contact tracing data and testing records, we analyze the transmission of SARS-CoV-2 during the Delta wave within New York City (NYC) from May 2021 to October 2021. We reconstruct transmission networks at the individual level and across 177 ZIP code areas, examine network structure and spatial spread patterns, and use statistical analysis to estimate the effects of factors associated with COVID-19 spread.ResultsWe find considerable individual variations in reported contacts and secondary infections, consistent with the pre-Delta period. Compared with earlier waves, Delta-period has more frequent long-range transmission events across ZIP codes. Using socioeconomic, mobility and COVID-19 surveillance data at the ZIP code level, we find that a larger number of cumulative cases in a ZIP code area is associated with reduced within- and cross-ZIP code transmission and the number of visitors to each ZIP code is positively associated with the number of non-household infections identified through contact tracing and testing.ConclusionsThe Delta variant produced greater long-range spatial transmission across NYC ZIP code areas, likely caused by its increased transmissibility and elevated human mobility during the study period. Our findings highlight the potential role of population immunity in reducing transmission of VOCs. Quantifying variability of immunity is critical for identifying subpopulations susceptible to future VOCs. In addition, non-pharmaceutical interventions limiting human mobility likely reduced SARS-CoV-2 spread over successive pandemic waves and should be encouraged for reducing transmission of future VOCs.

A Real Time Employee Attendance Monitoring System using ANN

Face recognition refers to the technology that examines and contrasts a person's face characteristics to recognise or verify their identity. Recently, this technology has drawn a lot of attention due to the potential uses it may have in security, marketing, and law enforcement. Face recognition involves studying a picture or video of a person's face to identify features like the space between their eyes, the contour of their nose, and the curve of their mouth. The person's identity is then established or verified by comparing these characteristics to a database of previously saved pictures. A series of techniques called facial recognition algorithms are used to identify and authenticate persons based on the features of their faces. These algorithms compare a person's facial attributes to those in a database of recognised faces by looking at things like the shape of their face, the distance between their eyes, and other distinctive facial features. There are many different types of face recognition algorithms, including geometric-based algorithms, appearance-based algorithms, and hybrid algorithms that combine both approaches. Geometric-based algorithms employ the geometry of face traits to identify and validate people, while appearance-based algorithms use image processing techniques to compare the patterns and textures of facial features. Recent advances in deep learning have significantly improved the accuracy of facial recognition algorithms. Artificial Neural Network (ANN) has shown to be highly effective and have been used in a range of applications, including mobile devices, security, and surveillance. Face recognition algorithms provide advantages, but there are also moral dilemmas with regard to its application, such as potential biases and privacy difficulties. As technology advances, it is imperative to address these problems and ensure that face recognition algorithms are used ethically and responsibly.

Pandemic surveillance and mobilities across Sydney, New South Wales

AbstractIn response to the acute public health crisis of COVID‐19 in 2020 and 2021, governments in Australia and around the world rushed to institute technologies to track human bodies with “live” surveillance. In Australia, “lockdowns” halted human mobilities in all states for different periods, and technologies for tracking bodies and collecting data were introduced after and between physical lockdowns. In this article, we analyse both the monitoring technologies developed to contend with COVID‐19 and the complex array of regulations and public health orders governing space and mobility in New South Wales and Sydney. Our focus on monitoring technologies is based on the COVIDSafe App (National) and the New South Wales COVIDSafe Check‐in App. These apps enabled the surveillance of individual mobilities before their sudden demise apparently unrelated to the public health scenario at the time. We argue that these technologies are examples of sensory power which rapidly enrolled human bodies in systems of surveillance that were difficult to unravel through 2022 and beyond. Our focus on regulations and public health orders outlines the shifting legal geographies during public health crisis and the ways these were enacted as mobility restrictions, surveillance, and punishment in western Sydney. We argue that the scars of the peak pandemic will endure in particular locations and communities, signalling the persistence of sensory power beyond the life of specific COVID‐19 tools.

What can AI see? The image of the ‘migrant’ in the era of AI post-visualization

ABSTRACT Over the past decade, artificial intelligence (AI) has become omnipresent in migration control and mobility surveillance, with AI systems now deployed across all aspects of migration management. Critics of such trends typically examine questions of ethics and rights from the vantage point of regulatory mechanisms and the limited venues for the redress of grievances. But if legal frameworks are as of yet forthcoming and do not necessarily apply to migrants, are there alternative mechanisms to critique algorithmic decision making? To explore this and related questions, this paper engages one such alternative by taking a ‘visual turn.’ In asking ‘what can AI see’ the paper interrogates the role of images in constructing AI's capacity to both understand migration and make appropriate decisions about migrants. In addition, a visual turn allows for exploration of an emergent age of post-visualization: a phenomenon whereby the values and meanings of what we see will be increasingly imparted to us by AI systems. The paper examines what AI sees with the help of an experiment, prompting an AI generative platform to draw distinctions between migrants, refugees, and people.

Mobile Health-Supported Active Syndrome Surveillance for COVID-19 Early Case Finding in Addis Ababa, Ethiopia: Comparative Study.

Since most people in low-income countries do not have access to reliable laboratory services, early diagnosis of life-threatening diseases like COVID-19 remains challenging. Facilitating real-time assessment of the health status in a given population, mobile health (mHealth)-supported syndrome surveillance might help identify disease conditions earlier and save lives cost-effectively. This study aimed to evaluate the potential use of mHealth-supported active syndrome surveillance for COVID-19 early case finding in Addis Ababa, Ethiopia. A comparative cross-sectional study was conducted among adults randomly selected from the Ethio telecom list of mobile phone numbers. Participants underwent a comprehensive phone interview for COVID-19 syndromic assessments, and their symptoms were scored and interpreted based on national guidelines. Participants who exhibited COVID-19 syndromes were advised to have COVID-19 diagnostic testing at nearby health care facilities and seek treatment accordingly. Participants were asked about their test results, and these were cross-checked against the actual facility-based data. Estimates of COVID-19 detection by mHealth-supported syndromic assessments and facility-based tests were compared using Cohen Kappa (κ), the receiver operating characteristic curve, sensitivity, and specificity analysis. A total of 2741 adults (n=1476, 53.8% men and n=1265, 46.2% women) were interviewed through the mHealth platform during the period from December 2021 to February 2022. Among them, 1371 (50%) had COVID-19 symptoms at least once and underwent facility-based COVID-19 diagnostic testing as self-reported, with 884 (64.5%) confirmed cases recorded in facility-based registries. The syndrome assessment model had an optimal likelihood cut-off point sensitivity of 46% (95% CI 38.4-54.6) and specificity of 98% (95% CI 96.7-98.9). The area under the receiver operating characteristic curve was 0.87 (95% CI 0.83-0.91). The level of agreement between the mHealth-supported syndrome assessment and the COVID-19 test results was moderate (κ=0.54, 95% CI 0.46-0.60). In this study, the level of agreement between the mHealth-supported syndromic assessment and the actual laboratory-confirmed results for COVID-19 was found to be reasonable, at 89%. The mHealth-supported syndromic assessment of COVID-19 represents a potential alternative method to the standard laboratory-based confirmatory diagnosis, enabling the early detection of COVID-19 cases in hard-to-reach communities, and informing patients about self-care and disease management in a cost-effective manner. These findings can guide future research efforts in developing and integrating digital health into continuous active surveillance of emerging infectious diseases.

Barriers to and facilitators of a just-in-time adaptive intervention for respiratory illness in cerebral palsy: a qualitative study

ObjectiveTo understand caregiver, healthcare professional and national expert perspectives on implementation of a just-in-time adaptive intervention, RE-PACT (Respiratory Exacerbation-Plans for Action and Care Transitions) to prevent respiratory crises in severe...