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

Paying Our Dues: The Role of Professional Societies in the Evolution of Mathematical Biology Education.

Mathematical biology education provides key foundational underpinnings for the scholarly work of mathematical biology. Professional societies support such education efforts via funding, public speaking opportunities, Web presence, publishing, workshops, prizes, opportunities to discuss curriculum design, and support of mentorship and other means of sustained communication among communities of scholars. Such programs have been critical to the broad expansion of the range and visibility of research and educational activities in mathematical biology. We review these efforts, past and present, across multiple societies-the Society for Mathematical Biology (SMB), the Symposium on Biomathematics and Ecology Education and Research (BEER), the Mathematical Association of America (MAA), and the Society for Industrial and Applied Mathematics (SIAM). We then proceed to suggest ways that professional societies can serve as advocates and community builders for mathematical biologists at all levels, noting that education continues throughout a career and also emphasizing the value of educating new generations of students. Our suggestions include collecting and disseminating data related to biomath education; developing and maintaining mentoring systems and research communities; and providing incentives and visibility for educational efforts within mathematical biology

An Investigation of Gene Regulatory Network State Space Variability

Genes are segments of DNA that provide a blueprint for cells and organisms to effectively control processes and regulations within individuals. There have been many attempts to quantify these processes, as a greater understanding of how genes operate could have large impacts on both personalized and precision medicine. Current biological methods cannot easily reveal the details of gene interactions. Therefore, we use gene expression data to infer networks of interactions, which are called gene regulatory networks or GRNs. These methods are designed to bypass the need for large amounts of data and extensive knowledge about a network. In this work, we extend previous work by investigating additional ways to incorporate stochasticity into gene regulatory networks