An open source tool to infer epidemiological and immunological dynamics from serological data: serosolver.

Area of Science:

• Epidemiology
• Immunology
• Computational Biology

Background:

• Serological datasets present challenges in inferring past infections due to cross-reactive antibody responses.
• Understanding infection histories is crucial for characterizing multi-strain pathogen dynamics.

Purpose of the Study:

• To introduce a flexible, open-source R package for analyzing serological data.
• To provide a modeling framework for jointly inferring infection histories and immune responses.

Main Methods:

• Developed a general modeling framework linking latent infection dynamics with antibody kinetics.
• Utilized a mechanistic model of antibody production and waning over time.
• Created an R package to implement the proposed modeling framework.

Main Results:

• The package enables inference of key immunological parameters like antibody boosting, waning, and cross-reactivity.
• Demonstrated application in inferring epidemiological processes such as attack rates and age-stratified infection risk.
• Successfully applied the model to two case studies using real-world serological data.

Conclusions:

• The R package offers a flexible tool for biological and epidemiological insights from serological data.
• Facilitates a deeper understanding of pathogen exposure and immune responses.
• Applicable to a wide range of pathogens with complex infection histories.