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Modeling and Applications Group

(ModApp)

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Vision, Mission, Goals

Vision: The ModApp Group shall be a leading force in the performance and promotion of research on the use of mathematics in the natural sciences, social sciences, engineering and other disciplines.

 

Mission: As an academic group of the Institute of Mathematics, ModApp shall study mathematical models in the life and physical sciences, explore areas and create venues for the interdisciplinary use of mathematics in the natural sciences, social sciences, engineering, and other disciplines, and offer courses and services in promotion thereof.

 

Goals: to study and do research on mathematical models in the life and physical sciences from which can be gained insights on trends, causes and effects, solvability, stability, and, whenever possible, predictability; to initiate research collaborations with the natural and social sciences, engineering, and other disciplines; to participate in the discussion of public concerns that may benefit from ModApp expertise.

Research

ModApp is actively working on three research domains: (1) modeling biological systems; (2) chemical reaction network theory; and (3) applications of optimal control theory to biological models. 

Latest Publications

Analyzing the dynamics of COVID-19 transmission in select regions of the Philippines: A modeling approach to assess the impact of various tiers of community quarantines

Mata MAE, Escosio RAS, Rosero EVGA, Viernes JPT, Añonuevo LE, Hernandez BS, Addawe JM, Addawe RC, Pilar-Arceo CPC, Mendoza VMP & de los Reyes V, AA
Heliyon 10(21):e39330 (2024)

The COVID-19 pandemic has significantly impacted communities worldwide, and effective management strategies are critical to reduce transmission rates and minimize the impact of the disease. In this study, we modeled and analyzed the COVID-19 transmission dynamics and derived relevant epidemiological values for three regions of the Philippines, namely, the National Capital Region (NCR), Davao City, and Baguio City, under different community quarantine implementations. The unique features and differences of these regions-of-interest were accounted for in simulating the disease spread and in estimating key epidemiological parameters fitted to the reported COVID-19 cases. Results support the robustness of the model formulated and provides insights into the effect of the government's implemented intervention protocols. With a forecasting feature, this modeling framework is beneficial for science-based decision support, policy making, and assessment for recent and future pandemics wherever regions-of-interest.

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