ABSTRACT
Fifth-generation (5G) networks offer high-speed data transmission with low latency, increased base station volume, improved quality of service (QoS), and massive multiple-input-multiple-output (M-MIMO) channels compared to 4G long-term evolution (LTE) networks. However, the COVID-19 pandemic has disrupted the achievement of mobility and handover (HO) in 5G networks due to significant changes in intelligent devices and high-definition (HD) multimedia applications. Consequently, the current cellular network faces challenges in propagating high-capacity data with improved speed, QoS, latency, and efficient HO and mobility management. This comprehensive survey paper specifically focuses on HO and mobility management issues within 5G heterogeneous networks (HetNets). The paper thoroughly examines the existing literature and investigates key performance indicators (KPIs) and solutions for HO and mobility-related challenges while considering applied standards. Additionally, it evaluates the performance of current models in addressing HO and mobility management issues, taking into account factors such as energy efficiency, reliability, latency, and scalability. Finally, this paper identifies significant challenges associated with HO and mobility management in existing research models and provides detailed evaluations of their solutions along with recommendations for future research.
Subject(s)
COVID-19 , Humans , Pandemics , Reproducibility of Results , Intelligence , MultimediaABSTRACT
According to a plethora of research and publications, the volume and amount of pollution are largely attributable to human-made emissions. Even during the recently ended Covid-19 outbreak, there was a notable decrease in global pollution, particularly in Pakistan's heavily populated cities. Due to the current situation, it is strategically important to safeguard the environment, and there are many criteria and predictors that should be used to encourage green behavior. This study examines green banking as a means of demonstrating ecologically responsible conduct in a developing nation. A survey questionnaire was used to collect information from 280 respondents via human contact and an internet platform. Software called SmartPLS3.0 was used to analyze the structural relationships between the study's variables. The results show that customers' adoption of green banking practices is statistically significantly influenced by their level of environmental consciousness and attitude. Similarly, green culture exhibits a substantial mediating influence between the independent variables and green behavior as well as a positive significant effect on green behavior. However, it is established that the consumer's apparent behavioral control is negligible. Particularly, the cognitive connection between behavior and culture is weak and insufficient to forecast behavior. For policymakers, especially those working in the field of green education, this study has many real-world applications.
ABSTRACT
BACKGROUND: Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. METHODS: In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. RESULTS: Statistical measures-Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE)-are used for model accuracy. The values of MAPE for the best-selected models for confirmed, recovered and deaths cases are 0.003, 0.006 and 0.115, respectively, which falls under the category of highly accurate forecasts. In addition, we computed 15 days ahead forecast for the daily deaths, recovered, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting the decision-making of evolving short-term policies.
ABSTRACT
Background During third week of September 2019, lady health workers reported twelve suspected cases of dengue fever from UC Bharakahu, Islamabad (population 70,000) to health department. Outbreak investigation conducted with objectives to determine risk factors and recommend control measures. Methods Investigation conducted from 20th September to 04th November 2019. Case was defined as any resident of UC Bharakahu with fever and two or more of following signs/symptoms;headache, retro-orbital pain, arthralgia, myalgia, petechial rash with NS1 (Nonstructural Protein 1) positive during 12th August to 18th November 2019. Age and sex matched healthy controls recruited from same neighborhood. Blood samples from seven suspected cases sent for laboratory confirmation. Results Total 993 houses surveyed and 113 cases identified. Mean age was 34.2 years (range 13-90 years). Most affected age group was 35-44 years (Attack Rate 0.78%), Overall attack rate was 0.15%. Males were predominantly involved n = 70 (62%). Out of total cases, 34 (70%) had stagnant water inside and around houses (OR 2.0, CI 1.06-3.75, p < 0.005), 40 (35%) used repellent lotions (OR 0.55, CI 0.32-0.95, p < 0.05), 34 (30%) used insecticide spray (OR 0.35, CI 0.20-0.61, p < 0.05), 97 (86%) used full protective clothing (OR 0.22, CI 0.07-0.68, p < 0.05). All seven blood samples tested positive for NS-1 Ag. Conclusion Presence of stagnant rain water inside and around houses acted as breeding grounds for aedes aegypti mosquitoes and was most probable cause of outbreak. Following our recommendations, health department initiated mosquito breeding sites control activities through insecticide residual spray and advocacy on use of protective measures against mosquito bites. Key words Outbreak, dengue, stagnant water, Bhara Kahu, Islamabad, 2019 [ABSTRACT FROM AUTHOR] Copyright of International Journal of Epidemiology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
Background. Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. Methods. In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. Results. Statistical measures i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for model accuracy. The values of MAPE for the best selected models for confirmed, recovered and deaths cases are 0.407, 0.094 and 0.124 respectively, which falls under the category of highly accurate forecasts. In addition, we computed fifteen days ahead forecast for the daily deaths, recover, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting decision making of evolving short term policies.
Subject(s)
COVID-19ABSTRACT
BackgroundSouth Asia has become a major epicentre of the COVID-19 pandemic. Understanding South Asians awareness, attitudes and experiences of early measures for the prevention of COVID-19 is key to improving the effectiveness and mitigating the social and economic impacts of pandemic responses at a critical time for the Region. MethodsWe assessed the knowledge, behaviours, health and socio-economic circumstances of 29,809 adult men and women, at 93 locations across four South Asian countries. Data were collected during the national lockdowns implemented from March to July 2020, and compared with data collected prior to the pandemic as part of an ongoing prospective surveillance initiative. ResultsParticipants were 61% female, mean age 45.1 years. Almost half had one or more chronic disease, including diabetes (16%), hypertension (23%) or obesity (16%). Knowledge of the primary COVID-19 symptoms and transmission routes was high, but access to hygiene and personal protection resources was low (running water 63%, hand sanitisers 53%, paper tissues 48%). Key preventive measures were not widely adopted. Knowledge, access to, and uptake of COVID-19 prevention measures were low amongst people from disadvantaged socio-economic groups. Fifteen percent of people receiving treatment for chronic diseases reported loss of access to long-term medications; 40% reported symptoms suggestive of anxiety or depression. The prevalence of unemployment rose from 9.3% to 39.4% (P<0.001), and household income fell by 52% (P<0.001) during the lockdown. Younger people and those from less affluent socio-economic groups were most severely impacted. Sedentary time increased by 32% and inadequate fruit and vegetable intake increased by 10% (P<0.001 for both), while tobacco and alcohol consumption dropped by 41% and 80%, respectively (P<0.001), during the lockdown. ConclusionsOur results identified important knowledge, access and uptake barriers to the prevention of COVID-19 in South Asia, and demonstrated major adverse impacts of the pandemic on chronic disease treatment, mental health, health-related behaviours, employment and household finances. We found important sociodemographic differences for impact, suggesting a widening of existing inequalities. Our findings underscore the need for immediate large-scale action to close gaps in knowledge and access to essential resources for prevention, along with measures to safeguard economic production and mitigate socio-economic impacts on the young and the poor.
Subject(s)
COVID-19ABSTRACT
Background: High number of SARS CoV2 infected patients has overburdened healthcare delivery system, particularly in low-income countries. In the recent past many studies from the developed countries have been published on the prevalence of SARS CoV2 antibodies and the risk factors of COVID 19 in healthcare-workers but little is known from developing countries. Methods: This cross-sectional study was conducted on prevalence of SARS CoV2 antibody and risk factors for seropositivity in HCWs in tertiary care hospitals of Peshawar city, Khyber Pakhtunkhwa province Pakistan. Findings: The overall seroprevalence of SARS CoV2 antibodies was 30.7% (CI, 27.8 to 33.6) in 1011 HCWs. Laboratory technicians had the highest seropositivity (50.0%, CI, 31.8 to 68.1). Risk analysis revealed that wearing face-mask and observing social-distancing within a family could reduce the risk (OR:0.67. p<0.05) and (OR:0.73. p<0.05) while the odds of seropositivity were higher among those attending funeral and visiting local-markets (OR:1.83. p<0.05) and (OR:1.66. p<0.01). In Univariable analysis, being a nursing staff and a paramedical staff led to higher risk of seropositivity (OR:1.58. p< 0.05), (OR:1.79. p< 0.05). Fever (OR:2.36, CI, 1.52 to 3.68) and loss of smell (OR:2.95, CI: 1.46 to 5.98) were significantly associated with increased risk of seropositivity (p<0.01). Among the seropositive HCWs, 165 (53.2%) had no symptoms at all while 145 (46.8%) had one or more symptoms. Interpretation: The high prevalence of SARS CoV2 antibodies in HCWs warrants for better training and use of protective measure to reduce their risk. Early detection of asymptomatic HCWs may be of special importance because they are likely to be potential threat to others during the active phase of viremia. Funding: Prime Foundation Pakistan.
Subject(s)
Viremia , Fever , Severe Acute Respiratory SyndromeABSTRACT
The COVID-19 pandemic caused by SARS-CoV-2 is a public-health emergency of international concern and thus calling for the development of safe and effective therapeutics and prophylactics particularly a vaccine to protect against the infection. SARS-CoV-2 spike glycoprotein is an attractive candidate for vaccine, antibodies and inhibitor development because of many roles it plays in attachment, fusion and entry into the host cell. In this study, we characterized the SARS-CoV-2 spike glycoprotein by immune-informatics techniques to put forward potential B and T cell epitopes, followed by the use of epitopes in construction of a multi-epitope peptide vaccine construct (MEPVC). The MEPVC revealed robust host immune system simulation with high production of immunoglobulins, cytokines and interleukins. Stable conformation of the MEPVC with a representative innate immune TLR3 receptor was observed involving strong hydrophobic and hydrophilic chemical interactions, along with enhanced contribution from salt-bridges towards inter-molecular stability. Molecular dynamics simulation in solution aided further in interpreting strong affinity of the MEPVC for TLR3. This stability is the attribute of several vital residues from both TLR3 and MEPVC as shown by radial distribution function (RDF) and a novel analytical tool axial frequency distribution (AFD). Comprehensive binding free energies estimation was provided at the end that concluded major domination by electrostatic and minor from van der Waals. Summing all, the designed MEPVC has tremendous potential of providing protective immunity against COVID-19 and thus has the potential to be considered in experimental studies.