Tooling-up for infectious disease transmission modelling.

Area of Science:

• Epidemiology
• Mathematical Biology
• Computational Science

Background:

• The field of infectious disease epidemiology has a rich history of methodological development.
• Early models focused on fundamental concepts like herd immunity, primarily using analytical approaches.
• Recent decades have seen a shift towards integrating complex data and advanced computational methods.

Purpose of the Study:

• To provide an overview and commentary on the evolution of infectious disease modeling.
• To identify key revolutions and their driving forces in the field.
• To speculate on future directions in infectious disease modeling.

Main Methods:

• Historical analysis of methodological advancements in infectious disease modeling.
• Review of key developments including analytical models, computational inference, and complex data integration.
• Commentary on the impact of data availability and technological tools.

Main Results:

• Identified three major revolutions in infectious disease modeling: 1) disease dynamics and heterogeneity, 2) advanced computing and inference, and 3) complexity and real-world application.
• Demonstrated how each revolution built upon previous knowledge, driven by new techniques, tools, and data.
• Highlighted the increasing sophistication of models in capturing real-world transmission heterogeneities.

Conclusions:

• The field's progress is marked by distinct revolutionary phases, each expanding the capacity to understand and predict infectious disease spread.
• The integration of novel datasets and advanced inference methods, like particle filtering, has been crucial for modeling real-world complexity.
• Future revolutions in infectious disease modeling will likely continue to be shaped by data-driven approaches and computational innovations.