The Internet of Healthcare Things (IoHT) demands massive and smart connectivity, huge bandwidth, lower latency with ultra-high data rate and better quality of healthcare experience. Unlike the 5G wireless network, the upcoming 6G communication system is expected to provide Intelligent IoHT (IIoHT) services everywhere at any time to improve the quality of life of the human being. In this paper, we present the framework of 6G cellular networks, its aggregation with multidimensional communication techniques such as optical wireless communication network, cell-free communication system, backhaul network, and quantum communication, as well as distributed security paradigm in the context of IIoHT. Such low latency and ultra-high-speed communication network will provide a new paradigm for connecting homes to hospitals, healthcare people, medical devices, hospital infrastructure, etc. Also, the requirements of 6G wireless networking, other key techniques, challenges and research direction of deploying IIoHT are outlined in the article.
When it comes to making assessments about public health, the mortality rate is a very important factor. The COVID-19 pandemic has exacerbated well-known biases that affect the measurement of mortality, which varies with time and place. The COVID-19 pandemic took the world off surveillance, and since the outbreak, it has caused damage that many would have thought unthinkable in the present era. By estimating excess mortality for 2020 and 2021, we provide a thorough and consistent evaluation of the COVID-19 pandemic's effects. Excess mortality is a term used in epidemiology and public health to describe the number of fatalities from all causes during a crisis that exceeds what would be expected under 'normal' circumstances. Excess mortality has been used for thousands of years to estimate health emergencies and pandemics like the 1918 "Spanish Flu"6. Positive excess mortality occurs when actual deaths exceed previous data or recognized patterns. It could demonstrate how a pandemic affects the mortality rate. The estimates of positive excess mortality presented in this research are generated using the procedure, data, and methods described in detail in the Methods section and briefly summarized in this study. We explored different regression models in order to find the most effective factor for our estimates. We predict the pandemic period all-cause deaths in locations lacking complete reported data using the Poisson, Negative Binomial count framework. By overdispersion test, we checked the assumption of the Poisson model, and then we chose the negative binomial as a good fitting model for this analysis through Akaike Information Criteria (AIC) and Standardized residual plots, after that checking the P-value<0.05; we found some significant predictors from our choosing model Negative binomial model, and the coefficient of all predictors gave the information that some factors have a positive effect, and some has a negative effect at positive excess mortality at COVID-19 (2020-2021).
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