A simple coarse-grained model to predict infectious disease outbreak statistics and the consequences of glycolysis inhibition on cell junction mechanics

Preventative medicine is the best way to keep individuals healthy and the cost of healthcare down. Thus, time and resources can be allocated elsewhere in the medical field if it is possible to prevent ailments that can be used for unpreventable outcomes. Sadly, viral infections are inevitable but occur randomly over a single lifetime. However, when averaging over a larger population, the long-term projected effects of viral infections can be conjectured. Thus, a deeper understanding of stochastic processes and systems can be helpful for diagnostic medicine and virology. This dissertation first highlights the effects of vascular viral infections have on tissue permeability in lung capillaries due to cell’s mechanical ineptitudes when deprived of ATP in Chapter I. Then, it focuses on a statistical mean field model of host population dynamics in Chapter II. Chapter III focuses on the statistics of viral and host population dynamics from Lotka-Volterra differential equations, and the application of the model with global disease statistics. Finally, Chapter IV highlights how the flu and COVID-19 fit the model in Chapter II.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biophysics & Quant Biology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Schwarz, Gregory Joseph

Contributors dc:contributor

• Dahmen, Karin
• Chemla, Yann
• Gruebele, Martin
• Kim, Sangjin

• Mechanobiology
• Actin, Traction Force
• Junction Tension
• Avalanche
• Mean Field Theory

Statement dc:rights

• Copyright 2024 Gregory Schwarz

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/127210

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F127210