title: Understanding the dynamics of rapidly evolving pathogens through modeling the tempo of antigenic change: influenza as a case study.
abstract: Rapidly evolving pathogens present a major conceptual and mathematical challenge to our understanding of disease dynamics. For these pathogens, the simulation of disease dynamics requires the use of computational models that incorporate pathogen evolution. Currently, these models are limited by two factors. First, their computational complexity hinders their numerical analysis and the ease with which parameters can be statistically estimated. Second, their formulations are frequently not sufficiently general to allow for alternative immunological hypotheses to be considered. Here, we introduce a new modeling framework for rapidly evolving pathogens that lessens both of these limitations. At its core, the proposed framework differs from previous multi-strain models by modeling the tempo of antigenic change instead of the pathogen's genetic change. This shift in focus results in a new model of reduced computational complexity that can accommodate different immunological hypotheses. We demonstrate the utility of this antigenic tempo model in an application to influenza. We show that, under different parameterizations, the model can reproduce the qualitative findings of a diverse set of previously published flu models, despite being less computationally intensive. These advantages of the antigenic tempo model make it a useful alternative to address several outstanding questions for rapidly evolving pathogens.
authors.affiliation: Department of Biology, Duke University, Durham, NC 27708, USA. katia.koelle@duke.edu
authors.forename: Katia
authors.initials: K
authors.lastname: Koelle
authors.forename: Meredith
authors.initials: M
authors.lastname: Kamradt
authors.forename: Mercedes
authors.initials: M
authors.lastname: Pascual
mesh_terms: D000940:Antigenic Variation; D000956:Antigens, Viral / Q000235:genetics / Q000276:immunology*; D005075:Biological Evolution; D016000:Cluster Analysis; D003198:Computer Simulation; D019143:Evolution, Molecular; D006801:Humans; D053122:Influenza A Virus, H3N2 Subtype / Q000235:genetics / Q000276:immunology* / Q000472:pathogenicity; D007251:Influenza, Human / Q000453:epidemiology* / Q000276:immunology / Q000635:transmission; D008954:Models, Biological; D013269:Stochastic Processes
chemical_list: D000956:Antigens, Viral