A Survey on Handover and Mobility Management in 5G HetNets: Current State, Challenges, and Future Directions.

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.

Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak.

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.

Outbreak investigation of dengue fever in union council (UC) Bharakahu, Islamabad, Pakistan, 2019

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.)