Impact of COVID-19 on mortality and excess mortality of midlife from 40 to 64 age groups

The COVID-19 pandemic has significantly affected the middle-aged population in the US. Leveraging the CDC dataset, this study quantifies the number of fatalities across various midlife age brackets, specifically 40–44, 45–49, 50–54, 55–59, and 60–64 for both males and females, spanning the years 2015 to 2020. A novel Python Package Index (PyPI) application, midlife was developed to compute and visualize these findings. The PyPI midlife application was also validated via Code Ocean for reproducibility of the application. The analysis revealed that males aged 55–59 and females aged 50–54 experienced the highest excess mortality due to COVID-19, likely due to a previously declining death trend in these groups. This research not only provides a method to visualize and calculate the impact of COVID-19 on midlife mortality by age and sex, but also highlights the potential economic repercussions of rising midlife mortality rates

Today’s Common Sense in Science can be Changed Tomorrow

Abstract: STEM-education also creates critical thinkers, increases science literacy, and enables the next generation of innovators. Although mathematics is robust, scientific laws are statements based on repeated experiments or observations that describe or predict a range of natural phenomena. Different observations may produce a new scientific law which may contradict with the conventional scientific laws. The teacher must tell all students that scientific laws are not always true, but only true under assumed observations. In other words, in the future, the current common sense of science can be changed tomorrow. Besides, we need to know that we are not always logical. This paper will present three examples to validate the proposed claims

Set Operations in Python for Translational Medicine

This is the world’s first tutorial article on Python programing on set operations for beginners and practitioners in translational medicine or medicine in general. This tutorial will allow researchers to demonstrate and showcase their tools on PyPI packages around the world. Via the PyPI packaging, a Python application with a single source code can run on Windows, MacOS, and Linux operating systems. In addition to the PyPI packaging, the reproducibility and quality of the source code must be guaranteed. This paper shows how to publish the Python application in Code Ocean after the PyPI packaging. Code Ocean is used in IEEE, Springer, and Elsevier for software reproducibility validation. First, programmers must understand how to scrape a dataset over the Internet. Second, the dataset files must be read in Python. Third, a program must be built to compute the target values using set operations. Fourth, the Python program must be converted to the PyPI package. Finally, the PyPI package is uploaded. Code Ocean plays a key role in publishing validation for software reproducibility. This paper depicts a vaers executable package as an example for calculating the number of deaths due to COVID-19 vaccines. Calculations were based on gender (male and female), age group, and vaccine group (Moderna, Pfizer, and Novartis), respectively