Delay differential equations (DDEs) have become an indispensable tool in mathematical biology and the life sciences. Time delays often arise naturally in processes such as gestation, immune response, epidemiological transmission, and neural dynamics, reflecting the time lag between cause and effect. This minisymposium brings together researchers and practitioners to discuss recent advancements in the theory and application of DDEs. Topics include stability analysis, bifurcation theory, numerical methods, and their implications for understanding biological rhythms, collective dynamics, and various types of feedback mechanisms. By highlighting both theoretical innovations and real-world applications, the symposium aims to foster interdisciplinary collaboration and inspire novel insights into complex biological systems. Participants will gain a deeper appreciation of how delays shape the dynamics of living systems and learn about cutting-edge techniques to tackle these challenges.

1. Elisa Continelli（University of Padova, Italy)
   Flocking estimates for Cucker-Smale models with time delay, communication failures and non-universal interaction
2. Zhengkang Li（China University of Mining and Technology）
   Slow-fast dynamics of delayed FitzHugh-Nagumo model with traveling waves
3. Rui Xiao（School of Mathematics, China Universityof Mining and Technology）
   Directional switches in network-organized swarming systems with delay
4. Yasuhisa SAITO（Department of Mathematics, Shimane University, JAPAN）
   Superlinear Damping and Global Convergence for Delay Differential Equations
5. Yukihiko Nakata（Department of Mathematical Sciences, College of Science and Engineering, Aoyama Gakuin University)
   Stability of a coupled system of logistic equations with delay
6. Toru Ohira（Graduate School of Mathematics, Nagoya University）
   Resonance in Delay Differential Equations with Exact Solutions
7. Kota Ikeda（School of Interdisciplinary Mathematical Sciences, Meiji University）
   Analysis of traffic congestion phases in the OV model via a differential-difference equation
8. Sergyi Shelyag（Flinders University）
   Periodic solutions of a delay-differential equation with periodic coefficient

This mini symposium aims to delve into the crucial role of modelling and simulation in the understanding, prediction, and control of communicable diseases.By gathering researchers, public health officials, and practitioners, we can foster collaboration and share insights regarding innovative modelling approaches and their practical applications in epidemic response. 

The primary objective of this symposium is to enhance participants' understanding of the pivotal role that modelling and simulation play in public health and to catalyse interdisciplinary collaboration. By sharing innovative approaches and lessons learned from past outbreaks, we seek to inspire forward-thinking strategies to tackle both current and future communicable disease challenges.

This event will serve as a platform for knowledge exchange, highlighting the importance of robust modelling frameworks and data-driven decision-making in shaping effective public health policies. Participants will leave with actionable insights and a network of collaborators to further advance the field of communicable disease modelling.

1. Dr. Nitu Kumari（IIT Mandi）
   
   
2. Dr. Prashant Kumar Srivastava（IIT Patna）
   
   
3. Dr. Krishna Kiran Vamsi Das（SSSSIHL, Prasanthinilayam）
   
   

4. Dr. Anuj Kumar（Thapar Institute of Engineering and Technology, Patiala, India）

Cardiovascular flow modeling, simulation, and experimentation are essential in understanding the complex dynamics of blood circulation and addressing cardiovascular diseases. Mathematical models, particularly those based on computational fluid dynamics (CFD), simulate blood flow through arteries, veins, and the heart. These models incorporate factors such as vessel geometry, blood viscosity, and cardiac dynamics, helping researchers study conditions like atherosclerosis, hypertension, and aneurysms.

Simulations provide detailed insights into how blood flow behaves under various conditions, guiding medical device design (e.g., stents, heart valves) and surgical procedures. They also help predict the impact of different treatments on cardiovascular health. 

Experimental studies, such as in vitro flow experiments and animal models, complement simulations by validating theoretical models and providing real-world data. Together, modeling, simulation, and experiment enable more accurate predictions of cardiovascular behavior, improve treatment strategies, and aid in the development of personalized healthcare solutions.

1. Dr. Abdullah（Aligarh Muslim University, India）
   
2. Dr. Chitranjan Pandey（IIT Kanpur）
   
3. Dr. Priyanshu Soni（IIT BHU, India）
4. Dr. Abhra Bhattacharya（IIT BHU, India）
   
   

Cell-cell interactions, collective migration, and fate transitions are at the heart of life’s most intricate processes,from embryonic development to the progression of diseases like cancer. These dynamic behaviors drive tissue organization, repair, and adaptation, while their dysregulation underpins pathologies such as tumor progression and metastasis. Understanding how cells communicate, coordinate their movements, and navigate fate decisions is key to unravelling the molecular and mechanical forces shaping development and diseases.

Mathematical modeling and biophysical approaches offer powerful framework to decipher these complexities. By combining quantitative tools with experimental data, we can unravel the interplay of biochemical signaling, mechanical forces, and emergent population behaviors. These approaches illuminate how cells migrate collectively during development or metastasis, how fate decisions arise from dynamic gene regulatory networks, and how heterogeneity fosters adaptability in diverse contexts.

This mini-symposium will showcase cutting-edge mathematical and biophysical approaches to study these crucial cellular processes. Presentations will explore quantitative models of cell-cell interactions, migration, fate transitions, and heterogeneity, with a goal of advancing our understanding of cellular dynamics and informing the development of novel therapeutic strategies for diseases like cancer and tissue regeneration.

1. C. Venkata Sai Prasanna（Indian Institute of Science, Bengaluru, India）
   Matrix density and spatial history determines the domination of a  epithelial-to-mesenchymal transitioned niche during collective cancer  invasion.
2. Ushasi Roy（Indian Institute of Science Education and Research Pune, India）
   From Genes to Patterns: In-silico Spatiotemporal Dynamics in Tissues 
3. Biplab Bhattacherjee（RIKEN, Japan）Structure formation induced by non-reciprocal cell–cell interactions in a multicellular system. 
4. Vibishan B（Indian Institute of Science, Bengaluru, India）Emergence of metabolic strategies in a consumption-secretion model of a cancer cell community. 
5. Sayantri Ghosh（National Institute of Technology Durgapur, India）Resource Competition and Emergent Multistability in Gene Expression Dynamics 

Mathematical models provide cost-effective and time-efficient frameworks for analyzing and predicting population dynamics. Since no single model can fully capture the complexities of organismal or individual interactions, a variety of modeling approaches are essential to reflect the unique behaviors of different systems. Research in population dynamics has significant implications across diverse fields, including disease treatment, conservation, and policy development. This symposium highlights recent advancements in mathematical and computational models that seek to unravel the dynamics of competition and disease spread, as well as the associated risk factors. By integrating multiple modeling techniques, the symposium aims to deepen our understanding of ecological and epidemiological processes, fostering the development of more effective strategies for managing both public health and ecological challenges.

1. Norvin P. Bansilan（Institute of Mathematical Sciences, College of Arts and Sciences, University of the Philippines Los Baños）
   Clustering Philippine Jobs by Infectious Disease Spread Risk: A Machine Learning Approach 
2. Diane Carmeliza N. Cuaresma（Institute of Mathematical Sciences, College of Arts and Sciences, University of the Philippines Los Baños ）
   Disease Spread Model with Stochastic Transmission and Contact Rates 
   
3. Maica Krizna A. Gavina（Institute of Mathematical Sciences, College of Arts and Sciences, University of the Philippines Los Baños）
   Exploring Coexistence and Stability in Modified Lotka-Volterra Models
4. Allen L. Nazareno（Institute of Mathematical Sciences, College of Arts and Sciences, University of the Philippines Los Baños ）
   Modelling the Epidemiological and Economic Impact of Maternal Respiratory Syncytial Virus (RSV) Vaccination in Australia  
   

During the COVID-19 pandemic, compartmental models were widely used for generating scenario-based forecasts that considered various effects of interventions (e.g., lockdowns, vaccinations). While the pandemic has already ended, COVID-19 and other infectious diseases continue to burden countries worldwide, including the Philippines. It thus remains valuable to study these models to support efforts on disease surveillance and pandemic preparedness.

This minisymposium features talks on the recent developments in the analysis of COVID-19 and vector-borne disease (VBD) models. Two talks study the long-term dynamics of solutions of these models, both of which assume piecewise-constant transmission rates. This assumption attempts to capture the shifting mobility conditions due to changes in government-imposed restrictions for the COVID-19 model, and the varying population-level interventions for the VBD model. The third talk analyzes the co-infection dynamics of the two diseases. These talks cover properties of solutions, and simulations to illustrate the theoretical results.

The last two talks feature studies that link compartmental models with local case data in the Philippines. One talk evaluates the effectiveness of pandemic restrictions using a calibrated COVID-19 compartmental model with a piecewise-constant transmission rate. The second considers a COVID-19 compartmental model that employs a physics-informed neural network approach to construct a time-varying transmission rate.

1. Timothy Robin Y. Teng（Department of Mathematics, School of Science and Engineering, Ateneo de Manila University, Philippines）Asymptotic Properties of Solutions to an SVEIQR COVID-19 Model with Switching Transmission
2. Destiny S. Lutero（Institute of Mathematical Sciences, College of Arts and Sciences, University of the Philippines Los Baños, Philippines）Long-term Dynamics of Solutions to a Vector-borne Disease Model with Piecewise-constant Transmission Rates
3. Alexander J. Balsomo（Department of Mathematics, College of Arts and Sciences, West Visayas State University, Philippines）Modeling Co-Infection Dynamics of COVID-19 and Dengue: Insights from Mathematical Models and Epidemiological Interactions
4. Mark Anthony C. Tolentino（Department of Mathematics, School of Science and Engineering, Ateneo de Manila University, Philippines）Modelling the Effectiveness of COVID-19 Community Quarantine Protocols in the National Capital Region, Philippines
5. Jeric C. Briones（Department of Mathematics, School of Science and Engineering, Ateneo de Manila University, Philippines）Can Transmission Rates Vary? Using Physics-Informed Neural Networks (PINN) for Modeling COVID-19 Epidemiological Dynamics