Project Description

This project will apply state-of-the-art computational, systems biology, data science, and feedback control techniques to understand how infectious diseases are affected by social determinants of health (SDOH). Social determinants of health are societal conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems that shape health outcomes. It has been shown that SDOH can affect disease outcomes for infections like tuberculosis and HIV. However, these effects have not yet been included in the modeling efforts.

Since the SDOH embed, over time, in the biology of individuals, these disease influences manifest from the population, individual, tissue to cellular levels. Thus, a robust, quantitative understanding of these multi-scale interactions will require innovative multi-scale modeling approaches. The postdoc will develop novel multi-scale computational models of infectious diseases. Once the modeling framework is developed, we will explore how to estimate the model parameters from real data and design equitable feedback control algorithms that improve healthcare outcomes. The position will be supported by mentor expertise in cellular and tissue level infectious disease modeling (PIenaar) and epidemiological modeling and control (Paré).

Start Date

Feb - Aug 2025

Post Doc Qualifications

The ideal candidate will have strong experience in computational modeling and/or control, preferably applied to biological systems, excitement to develop and innovate new models and methods, excellent communication and collaborative skills, and independent drive.

Co-Advisors

Philip E. Paré, philpare@purdue.edu, ECE, https://sites.google.com/view/philpare

Bibliography

[1] D Butler-Jones and T Wong. “Infectious disease, social determinants and the need for intersectoral action.” Can Commun Dis Rep. 2016 Feb 18; 42(Suppl 1): S1-18–S1-20.

[2] Cody, J., Ellis-Connell, A., O’Connor, S., Pienaar, E.. 2023. “Mathematical modeling indicates that regulatory inhibition of CD8+ T cell cytotoxicity can limit efficacy of IL-15 immunotherapy in cases of high pre-treatment SIV viral load.” Public Library of Science (PLoS) Computational Biology. 19 (8), e1011425

[3] Petrucciani, A., Hoerter, A., Kotze, L., Du Plessis, N. & Pienaar, E. “In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models.” PLoS One 19, e0299107 (2024).

[4] P. E. Paré, C. L. Beck, and T. Başar, “Modeling, Estimation, and Analysis of Epidemics over Networks: An Overview,” Annual Reviews in Control: Special Issue on Systems & Control Research Efforts Against COVID-19 and Future Pandemics, 2020.

[5] A. R. Hota, J. Godbole, and P. E. Paré, “A Closed-Loop Framework for Inference, Prediction, and Control of SIR Epidemics on Networks,” IEEE Transactions on Network Science and Engineering, 2021.