Opportunities and challenges in modeling emerging infectious diseases

Area of Science:

• Epidemiology
• Mathematical Biology
• Public Health

Background:

• Pathogen emergence includes novel viruses, spread to new populations, and drug resistance.
• Mathematical models are crucial for predicting disease cases, understanding transmission, and assessing control strategies.
• Data scarcity presents significant limitations for the effective use of these models.

Purpose of the Study:

• To highlight the capabilities and limitations of mathematical models in the context of pathogen emergence.
• To emphasize the growing potential of these models due to data and methodological advancements.
• To advocate for better integration of models with public health practices for improved response.

Main Methods:

• Review of the current state and limitations of mathematical modeling for emergent pathogens.
• Discussion of how increased data availability (genetics, ecology) and computational innovations enhance model utility.
• Exploration of the need for integrating infectious disease models into public health practice.

Main Results:

• Mathematical models offer valuable tools for forecasting and evaluating control of emergent pathogens.
• Data scarcity remains a key challenge, but is being addressed by new data sources and methods.
• Innovations are increasing the power of models to inform public health responses.

Conclusions:

• Integrating infectious disease models more closely with public health practice is essential.
• Developing readily available resources for model application can improve response timeliness and quality.
• Enhanced modeling capabilities hold significant promise for managing future pathogen emergence events.