Advances in Epidemiological and Biological Systems Modeling

Dear Colleagues,

This aim of this issue is to explore in detail the application of differential equations (partial differential equations, delay differential equations, stochastic differential equations, and ordinary differential equations) in understanding and controlling infectious diseases. The focus will be on how these mathematical models describe and predict the dynamics of epidemics and pandemics, specifically on the spread and curtailing of various infectious diseases. Key themes include advancements in classic epidemiological models, such as SIR and SEIR, and the introduction of new methods to handle greater complexity, such as spatial dispersal, heterogeneous mixing patterns, agent-based models, and stochastic effects. This issue also welcomes models that address real-world challenges, such as limited healthcare resources and vaccination optimization. Additionally, we seek to highlight models that incorporate environmental and socioeconomic factors influencing disease transmission. Special consideration will be given to models that aid in understanding disease resurgence after initial control efforts. This issue intends to bridge theory and practice by demonstrating how these models can inform public health policy and intervention strategies. Contributions that advance theoretical understanding of stability and bifurcation in disease-free and endemic equilibria are also welcomed. The role of delay differential equations in capturing time-lagged effects in disease transmission dynamics will be examined, alongside the application of optimal control theory. Expected submissions include original research articles, review articles, case studies, simulation studies, as well as perspectives and commentaries.

Dr. Adquate Mhlanga
Guest Editor

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