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Epidemics Modelings: Some New Challenges
Stefanella Boatto1, Renata Stella Khouri1, Lucas Solerman1
1Departamento de Matemática Aplicada, Instituto de Matemática, CCMN, Universidade Federal de Rio de Janeiro, Brazil.
AIP Conference Proceedings
|April 8, 2020
Summary
Mathematical modeling aids understanding of disease spread in large populations. Complex networks in urban environments present challenges for accurate epidemic prediction and analysis.
Area of Science:
- Epidemiology
- Mathematical Biology
- Network Science
Background:
- Epidemics modeling is crucial for understanding disease dynamics in large populations.
- Mathematical models are essential for studying both neglected and emerging infectious diseases.
- Urban environments present complex, dynamic networks that challenge traditional modeling approaches.
Purpose of the Study:
- To explore the application of mathematical modeling in understanding epidemics.
- To address the challenges posed by complex network structures in urban epidemic studies.
- To enhance the prediction of disease spreading dynamics in cities.
Main Methods:
- Utilizing mathematical modeling to represent disease transmission.
- Analyzing static and dynamic network structures in urban settings.
- Developing models that capture key characteristics of epidemic spread.
Main Results:
- Identified the need for sophisticated models to handle urban complexity.
- Highlighted the importance of network heterogeneity and dynamics in disease spread.
- Demonstrated the potential of modeling for predicting epidemic trajectories.
Conclusions:
- Mathematical modeling is a vital tool for epidemic research, especially in complex urban systems.
- Accurate epidemic prediction requires accounting for intricate network properties.
- Further research into dynamic and non-homogeneous networks is essential for public health.
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