Service Disruption Research Articles

Optimizing traffic management for public services during high-demand periods using cloud load balancers

In times of high demand, effective traffic management is essential for public service platforms to maintain reliable, continuous access for users. This paper explores the role of cloud load balancers as a solution for optimizing traffic distribution in public services during peak periods. By dynamically distributing network traffic across multiple servers, cloud load balancers enhance system scalability, flexibility, and response time, which are critical for minimizing service disruptions. The paper further discusses best practices for configuring load balancers, including geographic balancing and redundancy, and highlights the importance of integrating dynamic scaling and monitoring tools to adapt to sudden traffic surges. Additionally, the potential of artificial intelligence (AI) and predictive analytics in advancing cloud load balancing is considered, emphasizing their value for predictive scaling and automated traffic management. Key challenges, including data privacy and dependency on third-party infrastructure, are also addressed, with recommendations for enhancing load balancer deployment in public service applications. This paper concludes with strategic recommendations for public service providers to leverage cloud technology effectively, ensuring improved reliability, security, and cost-efficiency in service delivery. Keywords: Cloud Load Balancers, Traffic Management, Public Service Platforms, Dynamic Scaling, Predictive Analytics, Service Reliability.

Impact of the COVID-19 pandemic on hospital-based heart failure care in New South Wales, Australia: a linked data cohort study

BackgroundHealthcare policy implemented during the COVID-19 pandemic may have impacted the health of patients with heart failure. Australian data provide a unique opportunity to examine service disruption independent of significant COVID-19 burden. This study aimed to examine heart failure care during the pandemic in New South Wales (NSW).MethodsAnalysis of hospital utilisation among patients aged ≥ 18 years with a primary diagnosis of heart failure conducted using linked administrative health records from hospital admission, emergency department, non-admitted services, and mortality data collections. Health service utilisation and outcomes were compared “Pre-pandemic” (PP): 16th March 2018 – 28th August 2019 and “During pandemic” (DP): 16th March 2020 – 28th August 2021. Mortality data were available until December 2021.ResultsHeart failure-related ED presentations and hospital admissions were similar between the periods (PP = 15,324 vs DP = 15,023 ED presentations, PP = 24,072 vs DP = 23,145 hospital admissions), though rates of admission from ED were lower DP (PP = 12,783/15,324 (83.4% [95% CI 82.8-84.0]) vs DP = 12,230/15,023 (81.4% [95% CI 80.8-82.0%]). There was no difference according to age, sex, rurality, or socioeconomic status. Outpatient volume reduced DP (PP = 44,447 vs DP = 35,801 occasions of service), but telehealth visits increased nearly threefold (PP = 5,978/44,447 (13.4% [95% CI 13.1-13.8%]) vs DP = 15,901/35,801 (44.4% [95% CI 43.9-44.9%]) with highest uptake among the wealthy and those in major cities. Time to heart failure-related ED presentation, hospitalisation or all-cause mortality following index admission was longer DP (PP = 273 [IQR 259, 290] days, DP = 323 [IQR 300, 342] days, HR 0.91 [95% CI 0.88, 0.95]).ConclusionsPolicies implemented DP had minimal impact on volumes of inpatient heart failure care in NSW hospitals, but there were fewer admissions from ED and reduced volumes of publicly funded outpatient care. A rapid shift from patient-facing to remotely delivered care enabled compliance with restrictions and was associated with increased time to heart failure-related adverse events, but access was not afforded equally across the socio-demographic spectrum.

Early medical abortion provision via telehealth in Victoria: A qualitative descriptive study.

Little is known about the views of service providers currently working in telehealth early medical abortion (EMA) provision in Victoria, Australia. This study aims to contextualise the enablers and barriers to telehealth EMA provision, providing insight for healthcare policy and practice to improve the accessibility of this service. This was a qualitative descriptive study involving semi-structured interviews with 14 Victorian stakeholders with expertise and knowledge on telehealth EMA provision. Data were analysed using conventional content analysis. This study presents perceived enablers and barriers across four different contexts of the Victorian abortion system: (1)perceived patient context; (2) perceived provider context; (3) health organisation-system context; and (4)sociopolitical context. The COVID-19 pandemic's disruption of healthcare services led to greater patient and provider acceptance of telehealth EMA. However, barriers within the patient context included the inability to ensure safety and confidentiality, digital access and literacy issues, language barriers, and theimportance of trusting provider-patient relationships. Providers encountered challenges in delivering holistic care via telehealth, including time and workload issues and working with interpreters. Shortcomings within the organisational context encompassed structural barriers for culturally and linguistically diverse population groups, the absence of standard telehealth guidelines and varying interpretations of telehealth. Although temporary Medicare item number changes improved access, they presented financial challenges for mixed and private billing practices. The application of these findings by relevant health services and policymakers has the potential to improve the quality of, and increase accessibility to, telehealth EMA, better meeting the needs of individuals seeking this service.

A Simheuristic Approach to Scheduling Sustainable and Reliable Maintenance for Bridge Infrastructure

Designing maintenance strategies for a vast portfolio of aging infrastructures requires decision-makers to ensure adequate safety levels while addressing the requirements on service interruptions, costs, and workforce availability. This study addresses the problem of scheduling maintenance interventions for a portfolio of bridges, aiming to minimize CO2 emissions while meeting minimum reliability requirements and adhering to workforce and budget constraints. To achieve this, we present a Simheuristic algorithm that combines a metaheuristic core based on the Adaptive Large Neighborhood Search metaheuristic with a Monte Carlo simulation module. This integration allows for the evaluation of optimized scheduling solutions, accounting for the inherent randomness in the structural deterioration process. The proposed approach is tested in a comparative analysis against traditional time-based and condition-based scheduling methods. Results from diverse bridge portfolios demonstrate that the proposed algorithm offers improved performance in terms of both total costs and CO2 emissions.

Impact of COVID-19 on essential healthcare services in Addis Ababa, Ethiopia: Implications for future pandemics.

Responding to the COVID-19 pandemic has presented an unprecedented challenge to health systems, with countries needing to balance the demands of responding directly to the pandemic, while simultaneously continuing provision of essential health services. This study aimed to explore the impact of COVID-19 on essential healthcare services in Addis Ababa, Ethiopia. A facility-based retrospective study was undertaken in 30 health centers in Addis Ababa which were selected using simple random sampling. Secondary data were extracted for 22 indicators on maternal and child health, communicable and non-communicable diseases, and outpatient services for the period spanning between July 2019 and October 2020. These indicators were selected based on the WHO operational guidance on maintaining essential health services during an outbreak guide, essential packages of health services in Ethiopia and expert consultation. The difference in the trends of services before and during COVID-19 was compared using linear-by-linear tests and the difference of magnitude across the indicators was compared using Autoregressive Integrated Moving Average (ARIMA) interrupted time series analysis at a 5% significance level. Overall, more than 1.7 million people visited the studied facilities for outpatient services, and 18,325 mothers attended skilled delivery in the study period. The present study found that the mean number of patients treated for TB declined by 35 patients (β: -34.62; 95%CI: -50.29, -18.95) compared to the pre-COVID-19 era while the number of new patients enrolled for ART decreased by 71 patients (β: -70.62; 95%CI: -107.19, -34.05). Regarding maternal health services, the number of women who received post-natal care decreased by about 215 mothers (β: -214.87; 95%CI: -331.57, -98.17). Similarly, the mean number of clients served at inpatient services declined by 34 (β: -33.72; 95%CI: -68.55, 1.05). On the other hand, the mean number of patients screened for diabetes and hypertension during the pandemic increased by more than 1014 (β: 1014.5; 95%CI: 103.07, 1925.92) and 610 patients (β: 611.21; 95%CI: 302.42, 919.99), respectively. However, changes with regard to antenatal care, skilled birth delivery, and children immunization services did not show a statistically significant change after COVID-19 was reported in Ethiopia. Despite the notable efforts to sustain essential health services amidst the COVID-19 pandemic, our study revealed that there were disruptions in these services. This reinforces the need to adapt strategies to ensure sustainable provision of essential health services when pandemics of COVID19 magnitude cause disruptions of the health services.

Determinants of Unmet Healthcare Needs During the Final Stage of the COVID-19 Pandemic: Insights From a 21-Country Online Survey

ObjectivesDuring the COVID-19 pandemic, essential health services experienced significant disruptions, impacting preventive and chronic care across the world.MethodsUtilizing the Pandemic Recovery Survey (PRS), conducted online with Facebook’s Active User Base across 21 countries between March and May 2023, this cross-sectional study identifies the magnitude of and key factors associated with unmet preventive and chronic care needs during the late stage of the COVID-19 pandemic.ResultsApproximately 28.2% of respondents reported unmet preventive care needs, and 42.1% experienced unmet chronic care needs, with key determinants including food insecurity (aOR 1.94, 95% CI 1.81–2.07 for preventive services; aOR 1.85, 95% CI 1.68–2.03 for existing conditions) and distrust in health professionals (aOR 1.09, 95% CI 1.03–1.15 for preventive services; aOR 1.53, 95% CI 1.41–1.66 for existing conditions).ConclusionThe findings underscore a widespread unmet need for health services, highlighting the impact of social determinants and trust in health professionals on service disruption. The results suggest that pandemic recovery efforts should focus on the most affected groups to bridge health disparities and ensure an equitable recovery.

A Qualitative Study Evaluating the Challenges of Orthopedists and Internists in the Earthquake Zone

Abstract Background The earthquake centered in Kahramanmaras province, Turkey, on February 6, 2023, with a magnitude of 7.7, resulted in the loss of 53,537 lives and the injury of 107,213 people. This study was conducted to evaluate the challenges and experiences of physicians who provided post-disaster medical care. Methods We conducted a qualitative study with orthopedists and internists who had worked in the earthquake zone using in-depth interviews. Participants were selected through snowball sampling. Interviews explored challenges in the disaster zone, obstacles related to healthcare services, and disaster management. Thematic analysis was used. Results A total of 13 physicians participated in the study. Data revealed four main themes: disaster management, organizational deficiencies, challenges faced, and logistical issues. Participants indicated that the Ministry of Health didn’t have a priori operational emergency action plan, which led to problems in healthcare service delivery. Some physicians complained that they had to serve just as emergency consultants and were not able to carry out tasks compatible with their expertise. Some indicated that they organized the services rather than providing medical care. Almost all encountered a variety of interesting cases and evaluated their experience as significant. Logistical needs were met mostly with the support of non-governmental organizations. Conclusions We need to have a priori operational emergency action plan to ensure the effective delivery of healthcare services in the aftermath of a disaster. The Ministry of Health needs to allocate and coordinate healthcare personnel in line with their areas of expertise and prevent service disruptions. Key messages • There is a need to make an operational emergency action plan beforehand to deliver healthcare services effectively following a disaster. • Physicians should be allocated in line with their specialty of expertise to efficiently address the healthcare needs.

Unveiling the dimension of regional disparities: Assessing the disruption of immunisation services by COVID-19 in Bangladesh.

The coronavirus disease 2019 (COVID-19) pandemic disrupted essential health care services worldwide, including those related to immunisation. National data from Bangladesh shows that child immunisation may have been adversely affected by the pandemic but regional evidence is limited. We therefore aimed to explore the regional differences in the indirect effects of COVID-19 on child immunisation in Bangladesh. We extracted data from the District Health Information Software (DHIS2) spanning the period from January 2017 to December 2021. We examined three essential immunisation indicators: Bacille Calmette-Guérin (BCG), pentavalent third dose, and measles vaccinations. We examined both the yearly and monthly trends to explore fluctuations in the number of immunisations to pinpoint specific periods of service utilisation regression. Segmented regression with Poisson distribution was implemented given the count-based outcome. We reported incidence rate ratios (IRRs) with 95% confidence intervals (CIs) in different regions in 2020 and 2021 compared to the reference period (2017-19). We initially observed a notable decline in vaccine administration in April 2020 compared to the pre-pandemic period of 2017-19 with a drop of approximately 53% for BCG vaccines, 55% for pentavalent third doses, and 51% for measles vaccines followed by May 2020. The second half of 2020 saw an increase in vaccination numbers. There were noticeable regional disparities, with Sylhet (IRR = 0.75; 95% CI = 0.67-0.84 for pentavalent administration, IRR = 0.79; 95% CI = 0.71-0.88 for measles administration) and Chattogram (IRR = 0.77; 95% CI = 0.72-0.83 for BCG administration) experiencing the most significant reductions in 2020. In April 2020, Dhaka also experienced the largest decline of 67% in measles vaccination. In 2021, most divisions experienced a rebound in BCG and pentavalent administration, exceeding 2019 levels, except for Chittagong, where numbers continued to decline, falling below the 2019 figure. Our findings highlight the impact of the COVID-19 pandemic on childhood immunisation across regions in Bangladesh. Sylhet, Chattogram, and Dhaka divisions experienced the most significant reductions in immunisation services during 2020. This underscores the importance of targeted interventions and regional strategies to mitigate the indirect effects of future challenges on essential health care services, particularly childhood immunisation, in Bangladesh.

Transitional Infrastructures Extending Access to Safe Water in Informal Settlements: A Cross-Sectional Study in Nairobi, Kenya.

There is a pressing need for transitional water infrastructures in rapidly growing cities where conventional infrastructures lag behind human settlement expansion. In Kenya, sectoral innovations have addressed local socioeconomic challenges, but empirical evidence on their efficacy (combining quantitative indicators of safety and continuity) is lacking. We addressed this gap by comparing different water infrastructures in their ability to provide constant access to safe water in informal settlements in Nairobi, Kenya. We conducted a cross-sectional survey including 1,147 households in two informal settlements. Water infrastructures were categorized based on their distribution system: 1) piped to premises; 2) piped to a neighboring compound; 3) public tap/dispenser; and 4) street vendor. We tested associations between these systems with two outcomes: constant water availability and diarrhea (stratified by age group). We used adjusted odds ratios (AORs) to test associations between distribution systems and the selected outcomes, while accounting for confounders. Obtaining water from public taps/dispensers or street vendors was associated with service continuity (AOR = 1.45, 95% confidence interval [CI]: 1.06-1.99; AOR = 11.16, 95% CI: 2.45-50.82). Piped sources were associated with service disruption, especially when obtained from a neighboring compound (AOR = 0.45, 95% CI: 0.28-0.70). Public taps/dispensers were the only system consistently associated with lower odds of diarrhea, notably in children under the age of 5 years (AOR = 0.47, 95% CI: 0.29-0.79). Hence, in cities with a high prevalence of informal settlements and limited financial resources, public taps and dispensers hold promise as transitional water infrastructures.

Sustainability nexus AID: infrastructure resilience

Infrastructure resilience advanced through nexus thinking is pivotal for societies to handle disruptions and ensure sustainable functionality. This interconnected approach understands infrastructure as an interdependent complex system and enables cooperative planning to achieve resilience. However, challenges like data inadequacy, financial limitations and governance issues impede its adoption, especially in developing regions. The United Nations University (UNU) Sustainability Nexus Analytics, Informatics and Data (AID) Programme strives to promote integrated resource management for sustainable development and fulfilling the UN 2030 Agenda. Through its Infrastructure Resilience Module, the initiative provides tools, data platforms and localised capacity building to empower professionals and communities for evidence-based, collaborative decision-making accounting for intersectoral relationships. By supporting context-specific analytical capabilities, bridging data gaps, and governance silos, the programme aims to pave the way for resilient and sustainable infrastructure development, particularly across vulnerable regions in the Global South, which face disproportionate infrastructure service disruptions.

Comparison of Moving Average and Exponential Smoothing Methods for Estimating the Number of Internet Service Disruptions

Fairly tight competition in telecommunication business requires companies to continue improving the quality of their services, including handling network problems or customer complaints. However, the amount of disturbance that occurs is often unpredictable. Indeed, this condition will impact the allocation of resources and budget for handling customer disturbances in the following period, with uncertainty. This research aims to apply the moving average (MA) and single exponential smoothing (SES) forecasting methods to predict the number of internet service disruptions. The case study used in this paper is an internet service provider company with service coverage in the northern part of Surabaya. The number of disturbances data was collected from January 2022 to May 2022, with 18,453 data in total. The disturbances can be divided into five types: physical, mass, logical, PSB/migration, and others. Forecasting is carried out using the MA method with a period of three months. Meanwhile, forecasting using the SES method was carried out by first determining the alpha value that produces the smallest Mean Absolute Percentage Error (MAPE) value. The analysis results show that both forecasting methods are relatively effective and efficient in estimating the number of disturbances. Forecasting performance testing was carried out by measuring the Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) values. The results of forecasting performance measurements show that the SES method is much better than MA for all types of disturbance data, with MAPE values ​​below 2%.

An Overview of AI Applications in Cybersecurity for IT Management

Background: Cybersecurity serves as a defense mechanism against unauthorized access, theft, information disclosure, and service disruptions for electronic data, networks, and computer systems. Traditional cybersecurity strategies have struggled to counter increasingly sophisticated and automated cyberattacks, necessitating the exploration of advanced solutions such as artificial intelligence (AI). Aim: This research aims to highlight the role of AI technology and systems in enhancing cybersecurity measures, addressing how AI-based solutions can defend against cyber threats and identifying the associated challenges and future research directions. Methodology: A systematic literature review was conducted, focusing on recent advancements in AI applications for cybersecurity. Key databases and search engines were utilized to gather relevant articles, which were then filtered based on criteria such as language, publication quality, citation count, and accessibility. The collected literature was analyzed to extract insights on AI methods and their uses in cybersecurity. Results: The study found that AI offers several benefits in addressing cybersecurity concerns. AI systems can identify novel and complex cyber threats, handle substantial volumes of security data, and establish baselines for normal network behavior to detect deviations. Various AI techniques, including neural networks, expert systems, intelligent agents, and machine learning, were identified as effective tools in combating cyber threats. AI has demonstrated significant improvements in the detection and prevention of cyberattacks, enhancing overall cybersecurity effectiveness. Findings: While AI enhances cybersecurity capabilities, it also introduces new challenges such as the need for substantial data and resources, managing false alarms, and vulnerability to adversarial attacks. The study underscores the importance of continuous research to develop robust AI systems that can adapt to evolving cyber threats. Future research should focus on addressing the limitations of AI in cybersecurity, including adversarial threats, data integrity, and human-machine collaboration.

Emergency Preparedness for Waterborne Diseases in the Wake of Floods in Northern Cameroon: A Call for Immediate Action

Severe flooding in Northern Regions of Cameroon (Far North, North and Adamawa Regions), triggered by torrential rains, has led to widespread displacement, destruction of infrastructure, and significant disruptions in essential services, particularly water, sanitation, and healthcare. This article, issued by public health researchers from Science For Life Foundation and Pinnacle University Institute, highlights the heightened risk of waterborne diseases following the recent floods and underscores the urgent need for enhanced emergency preparedness and response measures. Floods create conditions conducive to the spread of diseases such as cholera, typhoid fever, dysentery, and viral hepatitis by contaminating drinking water supplies with sewage and waste. Vulnerable populations, including children, pregnant women, the elderly, and those with pre-existing health conditions, are at increased risk. Overcrowding in temporary shelters further exacerbates the potential for disease outbreaks. The impact of climate change is intensifying the frequency and severity of such flooding events, making timely and effective intervention critical. This article reviews best practices for preventing and mitigating waterborne disease outbreaks, offering guidance to government authorities, health agencies, humanitarian organizations, and local communities. It aims to catalyze immediate action to safeguard public health and prevent a major crisis in the aftermath of the flooding Keywords: Waterborne Diseases, Flooding, Emergency Preparedness.

IT Service Disruptions and Provider Choice

Digital supply chains are increasingly interconnected and vulnerable to disruption, causing service interruptions impacting many firms and their customers. Combating threats to the digital supply chain is the top challenge for leaders in most supply chain industries, demonstrated by the tacit approval of nation-states for cyber-attacks on corporate supply chains to disrupt downstream firms. Disruptions to digital supply chains are not new. In April 2019, hundreds of flights in the United States were delayed when a critical service provider, AeroData, had a computer systems failure. AeroData delivers flight planning services to many airlines including Southwest, United, American, and Delta. All flight operations for AeroData’s over 100 clients simultaneously ceased and thousands of customers were stranded at airports across the country. In an increasingly connected business environment, competitors may be simultaneously disrupted due to a common service provider, impacting all affected firms’ demand. The synchronization of disruptions for firms that use a common service provider has implications for service provider choice and investment. We use a two-stage game to model how a firm’s customer demand is impacted by disruptions at a service provider, and how this subsequently affects the firms’ choices in managing service provider risk. Considering downstream demand effects from upstream service disruptions, the contribution of this paper is the examination of how risk synchronization impacts provider choice decisions and profits. In addition, we illustrate how these choices impact upstream industry concentration.

Enhancing Machine Learning-Based Autoscaling for Cloud Resource Orchestration

Performance and cost-effectiveness are sustained by efficient management of resources in cloud computing. Current autoscaling approaches, when trying to balance between the consumption of resources and QoS requirements, usually fall short and end up being inefficient and leading to service disruptions. The existing literature has primarily focuses on static metrics and/or proactive scaling approaches which do not align with dynamically changing tasks, jobs or service calls. The key concept of our approach is the use of statistical analysis to select the most relevant metrics for the specific application being scaled. We demonstrated that different applications require different metrics to accurately estimate the necessary resources, highlighting that what is critical for an application may not be for the other. The proper metrics selection for control mechanism which regulates the requried recources of application are described in this study. Introduced selection mechanism enables us to improve previously designed autoscaler by allowing them to react more quickly to sudden load changes, use fewer resources, and maintain more stable service QoS due to the more accurate machine learning models. We compared our method with previous approaches through a carefully designed series of experiments, and the results showed that this approach brings significant improvements, such as reducing QoS violations by up to 80% and reducing VM usage by 3% to 50%. Testing and measurements were conducted on the Hungarian Research Network (HUN-REN) Cloud, which supports the operation of over 300 scientific projects.

The importance of recognition: predictors and outcomes of carer recognition among working carers of ageing relatives

ABSTRACT Many people manage the dual roles of employment and providing unpaid care for an older relative. Although this is increasingly common, carers frequently observe that their caring role is undervalued. This lack of recognition and the adverse consequences that flow from it are of increasing interest to policymakers, advocacy bodies, and employers who have advanced legislative, policy, and workplace accreditation initiatives to enhance recognition of caring. However, the design and impact of these initiatives are constrained by the scarcity of research on carer recognition, its predictors, and its outcomes. Drawing on data from the Australian National Carer Survey, we utilise path analysis to test a conceptual model outlining how life experiences shape carer recognition and subsequent personal outcomes. Our findings provide support for the mediating role of carer recognition linking care-related disruptions in multiple domains (aged care services, employment, family) with carer outcomes (social connectedness, personal well-being, psychological distress, personal time alongside caring). Employees who experienced less disruption in aged care services, employment, and family domains reported higher carer recognition and better outcomes than employees with more disruptive experiences in each domain. The findings suggest new points of intervention to enhance carer perceptions of recognition, and in turn, carer outcomes.

A comparative analysis of an aerosol dry deposition microscale model for overhead powerlines insulators

Aerosol particles deposition on overhead power line insulators may significantly threaten electric systems, affecting dielectric properties and potentially causing surface discharges and service interruptions, leading to economic consequences. This study addresses this challenge by evaluating a dedicated microscale deposition model that integrated into an advanced air quality (AQ) modeling system. The microscale model integration into the AQ system aims to improve predictions of equivalent salt deposit density (ESDD), a key measure of insulator contamination. This enhancement is crucial for improving system resilience and reliability. The microscale model’s performance in reconstructing particulate matter (PM) deposition fluxes onto XP-70 porcelain-disk electrical insulator is assessed against measured ESDD data collected across various regions in Italy during several experimental campaigns. In order to better understand the specific phenomena impacting PM deposition, the microscale model is compared to the state-of-the-art Zhang deposition model used in CAMx. The analysis reveals that the microscale model tends to overestimate measured ESDD values and predicts higher deposition velocities than the Zhang model. These results suggest potential modelling differences between the two models, including variations in obstacle parameterizations, influence of particle aerodynamics, and dispersion modelling in the near-wall region.

Differences in COVID-19 Vaccination and Experiences among Patients with Hypertension in Colombia and Jamaica during the COVID-19 Pandemic.

During the COVID-19 pandemic, hypertensive patients had increased infection and healthcare disruption in many low- and middle-income countries (LMICs) with limited vaccine access. The objective of this report is to describe COVID-19 experiences and vaccination uptake among hypertensive patients in Colombia and Jamaica. A cross-sectional study of patients with hypertension was conducted in primary care clinics in both countries between 2021 and 2022. Trained interviewers used a telephone-administered questionnaire to assess COVID-19 experiences (infection, vaccination, and healthcare access). A total of 576 patients (68.5% female, mean age: 67.5 years) participated. Health service disruption affecting access to care was low (<10%). Compared to Jamaica, more participants from Colombia reported testing positive for COVID-19, having family members or friends testing positive, losing family members or friends due to COVID-19, and being vaccinated. In logistic regression models, adjusted for age, sex, education, and rural/urban clinic status, fear of COVID-19 (OR 2.7, 95% CI: 1.2-6.1) and residence in Colombia (OR 5.9, 95% CI: 2.4-14.6) were associated with higher vaccination uptake. Disparities in access to COVID-19 testing and diagnosis may have influenced these country differences including fear of COVID-19 and vaccine uptake. Other factors need to be better understood to prepare for future pandemic responses.

Assessment of Cybersecurity Risks in Digital Twin Deployments in Smart Cities

Digital Twin (DT) technology has become a cornerstone of modern smart city infrastructure, providing real-time insights and operational efficiencies by creating a virtual replica of physical systems such as traffic networks, energy grids, and public services. While these advancements enable optimized urban management and improved decision-making, they also present new cybersecurity challenges that can potentially jeopardize the safety and reliability of critical infrastructures. This study addresses the cybersecurity risks associated with Digital Twin deployments in smart cities, focusing on threats such as unauthorized access, data manipulation, and hijacking of the DT models, which could result in service disruptions and compromise public safety. The research employs a comprehensive risk assessment methodology based on the NIST Cybersecurity Framework, where potential risks are identified, evaluated, and prioritized according to their severity and likelihood of occurrence. To mitigate these risks, a multi-layered security framework was developed, incorporating encryption mechanisms, robust access control, and an Artificial Immune System (AIS)-based anomaly detection model. The framework was tested through a simulated case study on a smart transportation system within a smart city environment, demonstrating its effectiveness in preventing data tampering and detecting unauthorized access. The results indicate that the proposed security model reduced data manipulation incidents by 35%, decreased response times for threat detection by 25%, and improved overall system resilience by 40%. These findings underscore the critical need for proactive cybersecurity strategies in ensuring the secure and resilient deployment of Digital Twin technologies in smart cities. The study concludes by emphasizing the importance of continuous security monitoring and adaptive threat management to safeguard smart city ecosystems from evolving cyber threats

Multiple Criteria-Based Intelligent Techniques for Efficient Handover in Next-Generation Networks

Background: Several wireless technologies are assumed to be operating in cooperation for next-generation networks. These networks offer various services to mobile users; however, efficient handover between different networks is the most challenging task in high mobility scenarios. The traditional signal strength-based handover algorithms are not able to cope with mobile users' high-quality requirements. Methods: In this paper, multiple criteria-based intelligent techniques are proposed to deal with inefficiencies related to handover. This technique makes use of artificial neural networks that take multiple parameters as inputs in order to predict the degradation of parameters. These predictions are further used to design rules for initiating handover procedures prior to service quality degradation. Results: Based on the prediction results obtained by deep neural networks, the handover decisions are recommended according to the type of application: conversational or streaming. Conclusion: The simulation results demonstrate the efficacy of the proposed method as there is an improvement of up to 40% and 25% in terms of handover rate and service disruption time, respectively, with an acceptable prediction error of 0.05.