An approach for modeling cross-immunity of two strains, with application to variants of Bartonella in terms of genetic similarity

Kwang Woo Ahn; Michael Kosoy; Kung-Sik Chan

doi:10.1016/j.epidem.2014.03.001

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An approach for modeling cross-immunity of two strains, with application to variants of Bartonella in terms of genetic similarity

Journal article

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An approach for modeling cross-immunity of two strains, with application to variants of Bartonella in terms of genetic similarity

Kwang Woo Ahn, Michael Kosoy and Kung-Sik Chan

Epidemics, Vol.7, pp.7-12

06/2014

DOI: 10.1016/j.epidem.2014.03.001

PMID: 24928664

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Abstract

•Proposed is a two-strain SIR model for cross-immunity with proportions.•The proposed model accounts for seasonality in the birth rate.•We found partial cross-immunity between some Bartonella variants. We developed a two-strain susceptible-infected-recovered (SIR) model that provides a framework for inferring the cross-immunity between two strains of a bacterial species in the host population with discretely sampled co-infection time-series data. Moreover, the model accounts for seasonality in host reproduction. We illustrate an approach using a dataset describing co-infections by several strains of bacteria circulating within a population of cotton rats (Sigmodon hispidus). Bartonella strains were clustered into three genetically close groups, between which the divergence is correspondent to the accepted level of separate bacterial species. The proposed approach revealed no cross-immunity between genetic clusters while limited cross-immunity might exist between subgroups within the clusters.

Bartonella

Conditional least squares

Cross-immunity

SIR model

Details

Title: Subtitle: An approach for modeling cross-immunity of two strains, with application to variants of Bartonella in terms of genetic similarity
Creators: Kwang Woo Ahn - Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA
Michael Kosoy - Centers for Disease Control and Prevention, Fort Collins, CO, USA
Kung-Sik Chan - Department of Statistics and Actuarial Science, The University of Iowa, Iowa City, IA, USA
Resource Type: Journal article
Publication Details: Epidemics, Vol.7, pp.7-12
DOI: 10.1016/j.epidem.2014.03.001
PMID: 24928664
NLM abbreviation: Epidemics
ISSN: 1755-4365
eISSN: 1878-0067
Publisher: Elsevier B.V
Language: English
Date published: 06/2014
Academic Unit: Statistics and Actuarial Science; Radiology
Record Identifier: 9983985829702771

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