Background: Coronavirus disease has caused global turmoil especially causing huge impact on human life all over the world. Current reports states more than 3 million people have lost life and more than 160 million people are known to be suspected with the SARS-CoV-2. Transmission and disease incidence rates are indicators to assess the seriousness of COVID-19 pandemic and studies to understand the factors that aid in this direction are very vital to curb the disease. Methods: The study intends to discover the relationship by performing statistical analysis using correlation and multiple linear regression analysis between the variable’s population density, temperature, relative humidity, and active time of virus and find out the parameters that predict the cases reported per million population in 83 countries. Results: Analysis indicates active time of virus in days is very positively associated with the COVID -19 cases in all the countries r = .604, p < .01. Active time of virus shows strong negative correlation with temperature r = -.930, p < .01 revealing that rise in temperature will reduce the virus activity in the population. Together, these variables will account for 36.2% variance in the cases per million population with no significant prediction estimated from any factor. Conclusion: The study outcomes clearly state that population density alone is insufficient to estimate the extent of influence on COVID -19 cases as the number of persons living per sq. km of land is a dynamic quantity tend to fluctuate over time and space due to migration of population. In conjunction to the previous studies reported on the environmental and climatic factors influencing the cases reported, population dynamics does not show much significance on the disease spread and incidence. Contribution: The rise in confirmed cases and the high incidence rate reported in countries can be attributed to the active time of virus life expectancy as there is a positive correlation observed between the COVID-19 cases reported and the virus active time in the examined countries. Also, environment and climatic factors play a role in modulating the infection and transmission rate with less significant influence of population density on the COVID-19.

The principle of Undetectable = Untransmittable (U=U) posits that people living with the human immunodeficiency virus (HIV) who are able to achieve and maintain a viral load of <200 copies/mL by regularly taking antiretroviral drugs (ARVs) are considered virally suppressed and cannot transmit the HIV virus to other individuals through sex. This groundbreaking message has emerged as a key HIV prevention strategy for eliminating transmission risks and enhancing the quality of life of people living with HIV. This narrative review explores the clinical foundation of U=U, the level of awareness and acceptance of the message globally, and the psychosocial impact on people living with HIV. It has been discovered that this message minimizes stigma, improves mental health, promote treatment adherence and good disclosure behaviors among people living with HIV. Evidence has shown that despite the U=U revolution for HIV prevention, there are significant differences in awareness and acceptance of the message among different population groups. The challenges noted were poor communication by healthcare providers, limitations in the health system, and stigma issues. Nevertheless, the inclusion of U=U in mainstream HIV services has proven to increase awareness and enhance its adoption. The urgent need in the present review is to advocate for strategies to increase the equitable distribution of U=U to harness its full potential in public health.

This research is a collaboration involving a university team, a partnering corporation, and a hemodialysis clinic, which is a cross-disciplinary research initiative in the field of Artificial Intelligence of Things (AIoT) within the medical informatics domain. The research has two objectives: (1) The development of an Internet of Things (IoT)-based Information System customized for the hemodialysis machines at the clinic, including transmission bridges, clinical personnel dedicated web/app, and a backend server. The system has been deployed at the clinic and is now officially operational; (2) The research also utilized de-identified, anonymous data (collected by the officially operational system) to train, evaluate, and compare Deep Learning-based Intradialytic Blood Pressure (BP)/Pulse Rate (PR) Predictive Models, with subsequent suggestions provided. Both objectives were executed under the supervision of the Institutional Review Board (IRB) at Mackay Memorial Hospital in Taiwan. The system completed for objective one has introduced three significant services to the clinic, including automated hemodialysis data collection, digitized data storage, and an information-rich human-machine interface as well as graphical data displays, which replaces traditional paper-based clinical administrative operations, thereby enhancing healthcare efficiency. The graphical data presented through web and app interfaces aids in real-time, intuitive comprehension of the patients’ conditions during hemodialysis. Moreover, the data stored in the backend database is available for physicians to conduct relevant analyses, unearth insights into medical practices, and provide precise medical care for individual patients. The training and evaluation of the predictive models for objective two, along with related comparisons, analyses, and recommendations, suggest that in situations with limited computational resources and data, an Artificial Neural Network (ANN) model with six hidden layers, SELU activation function, and a focus on artery-related features can be employed for hourly intradialytic BP/PR prediction tasks. It is believed that this contributes to the collaborating clinic and relevant research communities.