Natural Selection at Work: An Accelerated Evolutionary Computing Approach to Predictive Model Selection

We implement genetic algorithm based predictive model building as an alternative to the traditional stepwise regression. We then employ the Information Complexity Measure (ICOMP) as a measure of model fitness instead of the commonly used measure of R-square. Furthermore, we propose some modifications to the genetic algorithm to increase the overall efficiency

A Study of COVID-19 Mortality Under Varying Patient Frailty

For this study, we modeled the spread and mortality of COVID-19 throughout the city of Chicago. By incorporating group frailty into a classic SEIR infectious disease model, we were able to differentiate the population of Chicago by their response to COVID-19. Three age groups with different COVID-19-induced death rates were examined, and the model sought to showcase the multiplicative deviation of each age group death rate from the average disease-induced death rate. This adjustment for different death rates among age groups accounted for heterogeneity within the population, and sought to introduce a more accurate manner for modeling the spread of infectious diseases

Compartmental Modeling for the Neophyte: An Application of Berkeley Madonna

Compartmental modeling serves as a necessary framework in many fields, especially biomathematics and ecology. This article introduces readers to a user-friendly approach to constructing compartmental models and solving the resulting systems of differential equations to simulate real-world applications. The platform used is Berkeley Madonna, a software package that has an intuitive graphical interface which empowers users—even those with limited mathematical and programming backgrounds—to focus on modeling concepts rather than mathematical or programming intricacies. This makes Berkeley Madonna an ideal platform for students, educators, and researchers