Inferring time of infection from field data using dynamic models of antibody decay

Abstract

Studies of infectious disease ecology would benefit greatly from knowing when individuals were infected, but estimating this time of infection can be challenging, especially in wildlife. Time of infection can be estimated from various types of data, with antibody‐level data being one of the most promising sources of information. The use of antibody levels to back‐calculate infection time requires the development of a host‐pathogen system‐specific model of antibody dynamics, and a leading challenge in such quantitative serology approaches is how to model antibody dynamics in the absence of experimental infection data.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 21, 2023
Source ID
10.1111/2041-210x.14165

Entities

People

  • Angela H. Guglielmino
  • Benny Borremans
  • James O. Lloyd-Smith
  • Katherine Prager
  • Niel Hens
  • Renee L Galloway
  • Riley O Mummah

Organizations

  • Centers for Disease Control and Prevention
  • Defense Advanced Research Projects Agency
  • National Science Foundation
  • Strategic Environmental Research and Development Program
  • University of Antwerp
  • University of California, Los Angeles

Tags

Fields of Study

  • Biology

Readers

  • Immunology
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference