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The Mathematics of Serocatalytic Models With Applications to Public Health Data
Everlyn Kamau1, Junjie Chen2, Sumali Bajaj3
1Francis I. Proctor Foundation, University of California San Francisco, San Francisco, California, USA.
Statistics in Medicine
|July 23, 2025
Summary
Serocatalytic models infer past infections from serological surveys, crucial for understanding disease spread when surveillance is limited. Accounting for epidemiological context is key for accurate insights into infectious disease patterns.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Biostatistics
Background:
- Serological surveys provide vital data for understanding historical infection patterns, especially in regions with limited disease surveillance.
- These surveys serve as a ground truth for assessing the true burden of infectious diseases.
Purpose of the Study:
- To provide a tutorial on a wide range of serocatalytic models for generating epidemiological insights.
- To explore the mathematical properties and intuition behind these models.
- To demonstrate applications using real-world data for diverse pathogens and scenarios.
Main Methods:
- Mathematical analysis of serocatalytic model properties.
- Application of models to real epidemiological data.
- Provision of practical implementation guidance with R and Stan code.
Main Results:
- Demonstrated the utility of serocatalytic models in inferring historical infection dynamics.
- Illustrated model applications across various pathogens and epidemiological contexts.
- Provided a reproducible framework for learning and applying serocatalytic modeling.
Conclusions:
- Serocatalytic models are essential tools for reconstructing past infection patterns from serological data.
- Understanding the specific epidemiological context is critical for the valid application and interpretation of these models.
- This work equips learners with the knowledge and tools to utilize serocatalytic modeling for infectious disease epidemiology.
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