ABSTRACT
Due to advancements in information and communication technology, the Internet of Things has gained popularity in a variety of academic fields. In IoT-based healthcare systems, numerous wearable sensors are employed to collect various data from patients. The healthcare system has been challenged by the increase in the number of people living with chronic and infectious diseases. There are several existing IoT-based healthcare systems and ontology-based methods to judiciously diagnose, and monitor patients with chronic diseases in real-time and for a very long term. This was done to drastically minimize the vast manual labor in healthcare monitoring and recommendation systems. The current monitoring and recommendation systems generally utilised Type-1 Fuzzy Logic (T1FL) or ontology that is unsuitable owing to uncertainty and inconsistency in the processing, and analysis of observed data. Due to the expansion of risk and unpredictable factors in chronic and infectious patients such as diabetes, heart attacks, and COVID-19, these healthcare systems cannot be utilized to collect thorough physiological data about patients. Furthermore, utilizing the current T1FL ontology-based method to extract the ideal membership value of risk factors becomes challenging and problematic, resulting in unsatisfactory outcomes. Therefore, this chapter discusses the applicability of IoT-based enabled Type-2 Fuzzy Logic (T2FL) in the healthcare system, and the challenges and prospects of their applications were also reviewed. The chapter proposes an IoT-based enabled T2FL system for monitoring patients with diabetes by extracting the physiological factors from patients' bodies. The wearable sensors were used to capture the physiological factors of the patients, and the data capture was used for the monitoring of patients. The results from the experiment reveal that the model is very efficient and effective for diabetes patient monitoring, using patient risk factors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The coronavirus (COVID-19) outbreak has been a global tragedy, which emerged in Wuhan, China December 2019 and has posed critical concerns on prediction, diagnosis, control, and mitigation globally. Subsequently, there have been unprecedented measures to curtail the outbreak worldwide, which include closures of businesses, schools, and country bounders among others. The COVID-19 seems to have a greater impact on the global economy when compared to severe acute respiratory syndrome (SARS) that occurs in 2013. The hypothetical epidemiology has provided remarkable conceptual and technical development, this area of research not only aims to analyze and anticipate the spread of different diseases but also help in controlling the diseases effectively. Mathematical modeling has been at the forefront since the COVID-19 pandemic started in late December 2019 to form decisions concerning various non-pharmaceutical approaches to curtail its’ spread globally, but this has been studies in Nigeria contents. Therefore, this chapter discusses the use of a mathematical model in fighting COVID-19 spread, assessing the impact of the mitigation strategies and control put in place worldwide. The chapter was concluded by using Nigeria as a case study to incorporate features appropriate to COVID-19 spread dynamics and control in Nigeria. The significant contributions of this chapter are (a) Proposed a mathematical model for finding COVID-19 pandemics dynamics and control strategies in Nigeria (b) study prior mathematical models developed to research COVID-19 disease outbreak dynamics behavior and containment globally and (c) lastly, investigation the importance of mathematical model in COVID-19 pandemic. The results show that mathematical modeling can be used as a powerful tool to understand the transmission and exploring different containment scenarios of the COVID-19 outbreak. Dynamic interventions were expected to reduce the percentage of the population infected in a shorter period and could reduce the number of infected cases in ICU below current estimates of Nigeria’s ICU strength. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The occurrence of coronavirus (COVID-19) is greater than that of 2003 representing respiratory infections syndrome (SARS). As of 12 August 2020, the reported cases are more than 73,435 deaths and more than 2000 deaths worldwide, and both COVID-19 and SARS are distributed across regions, infecting living beings. By contrast, in 2003, SARS claimed 774 lives but in the shortest time, COVID-19 claimed more than that. But the major difference among them is that since 17 years of SARS others power new tool has emerged, which could be used as an instrument in fighting this virus and keeping it within reasonable limits. Artificial intelligence (AI) is one of those tools. Recently, AI is causing a paradigm shift in the healthcare sector, and the applicability it in the COVID-19 outbreak might yield profit especially in predicting the location of the next outbreak. The application of AI in COVID-19 can be expediting the diagnoses and monitoring of COVID-19 and minimizes the burden of these processes. Therefore, this chapter discusses the areas of applicability of AI during the COVID-19 pandemic. Discusses several extraordinary opportunities brought by AI in the COVID-19 outbreak and the research challenges of AI during the outbreak. In medical detection, the use of AI has increased tremendously. This has been commonly used to achieve relatively precise recognition accuracy and to reduce the burden on health systems by reducing the time of evaluation associated with conventional approach detection procedure. The AI techniques are seen as a major aspect in identifying the risk of infectious diseases in enhancing the forecasting and identification of potential world health threats. The continued expansion of AI for the Covid-19 disease outbreak has dramatically improved monitoring, diagnosis, monitoring, analysis, forecasting, touch trailing, and medications/vaccine production process and minimized human involvement in nursing treatment. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.