Investigating the impact of vaccination and non-pharmaceutical measures in curbing COVID-19 spread: A South Africa perspective.

The year 2020 brought about a pandemic that caught most of the world population by surprise and wreaked unimaginable havoc before any form of effective reaction could be put in place. COVID-19 is proving to be an epidemic that keeps on having an upsurge whenever it looks like it is being curbed. This pandemic has led to continuous strategizing on approaches to quelling the surge. The recent and welcome introduction of vaccines has led to renewed optimism for the population at large. The introduction of vaccines has led to the need to investigate the effect of vaccination among other control measures in the fight against COVID-19. In this study, we develop a mathematical model that captures the dynamics of the disease taking into consideration some measures that are easier to implement majorly within the African context. We consider quarantine and vaccination as control measures and investigate the efficacy of these measures in curbing the reproduction rate of the disease. We analyze the local stability of the disease-free equilibrium point. We also perform sensitivity analysis of the effective reproduction number to determine which parameters significantly lowers the effective reproduction number. The results obtained suggest that quarantine and a vaccine with at least 75% efficacy and reducing transmission probability through sanitation and wearing of protective gears can significantly reduce the number of secondary infections.

A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19.

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks.

BACKGROUND AND OBJECTIVE: The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors' knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis. METHODS: We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. RESULTS: The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. CONCLUSIONS: The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.

Smart healthcare support for remote patient monitoring during covid-19 quarantine.

Social distancing and quarantining are now standard practices which are implemented worldwide since the outbreak of the novel coronavirus (COVID-19) disease pandemic in 2019. Due to the full acceptance of the above control practices, frequent hospital contact visits are being discouraged. However, there are people whose physiological vital needs still require routine monitoring for improved healthy living. Interestingly, with the recent technological advancements in the areas of Internet of Things (IoT) technology, smart home automation, and healthcare systems, contact-based hospital visits are now regarded as non-obligatory. To this end, a remote smart home healthcare support system (ShHeS) is proposed for monitoring patients' health status and receiving doctors' prescriptions while staying at home. Besides this, doctors can also carry out the diagnosis of ailments using the data collected remotely from the patient. An Android based mobile application that interfaces with a web-based application is implemented for efficient patients-doctors dual real-time communication. Sensors are incorporated in the system for automatic capturing of physiological health parameters of patients. Also, a hyperspace analogue to context (HAC) was incorporated into the current monitoring framework for service discovery and context change in the home environment towards accurate readings of the physiological parameters and improved system performance. With the proposed system, patients can be remotely monitored from their homes, and can also live a more comfortable life through the use of some features of smart home automation devices on their phones. Therefore, one main significant contribution of this study is that patients in self-isolation or self-quarantine can use the new platform to send daily health symptoms and challenges to doctors via their mobile phones. Thus, improved healthy living and a comfortable lifestyle can still be achieved even during such a problematic period of the 2019 COVID-19 pandemic that has already recorded 20,026,186 million cases so far with 734,020 thousand deaths globally.

A case-based reasoning framework for early detection and diagnosis of novel coronavirus.

Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 11,301,850 confirmed cases while more than 531,806 have died due to it, with these figures rising daily across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is, therefore, crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is a compelling paradigm that allows for the utilization of case-specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study, therefore, aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in the classification of suspected cases of COVID-19. The CBR model leverages on a novel feature selection and the semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved from 71 (67 adults and 4 pediatrics) cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories with an accuracy of 94.54%. The study found that the proposed model can support physicians to easily diagnose suspected cases of COVID-19 based on their medical records without subjecting the specimen to laboratory tests. As a result, there will be a global minimization of contagion rate occasioned by slow testing and in addition, reduced false-positive rates of diagnosed cases as observed in some parts of the globe.