000			01377 a2200169 4500
082			_a517.988 MAD
100			_aMadhab Bharman _93379
245			_aAnalysis and Simulations of Network-Based Nonlinear Epidemic Models
260			_bMathematic, _cAug 2024.
300			_a205p; _bxix:
500			_aThe study of epidemic dynamics is crucial for understanding and controlling the spread
502			_aFinally, our work contributes to the evolving field of epidemic modeling by integrating graph Laplacian diffusion into the framework.
520			_aThe study of epidemic dynamics is crucial for understanding and controlling the spread of infectious diseases. In recent years, there has been a growing interest in employ- ing graph theory and network science to model the complex interactions among in- dividuals and understand spatial disease spread in a population. This thesis explores the application of graph Laplacian diffusion in epidemic modeling of well-established epidemic models such as Susceptible-Infectious-Removed (SIR), Susceptible-Exposed- Infectious-Removed (SEIR), Susceptible-Asymptomatic-Infectious-Removed (SAIR), and more.
653			_aHopf bifurcation; Epidemic; Compartmental Model; Spatial- disease spread; Population mobility; Graph Laplacian; Local Sta- bilith;
700			_aMadhab Barman _eDr. Nachiketa Mishra _93380
942			_cTD
999			_c5289 _d5284