NSF Award
1021896
The degree of bio-threat associated with newly detected pathogen variants (genotypes) that are genetically similar to some known pathogens may be assessed in terms of the cross-immunity between the two variants under study. Perfect (lack of) cross-immunity between the two variants suggests that the newly detected pathogen and the known variant are identical (distinct) epidemiologically. New epidemiological models will be developed for estimating cross-immunity in a two-strain system that may allow for variable birth rate of the natural hosts, possibility of vertical transmission and finite number of contacts per subject per unit time. Similar to many popular epidemiological models, the proposed epidemiological models stipulate that the dynamics of the state vector follow some nonlinear partial differential equation (PDE). New computationally efficient estimation methods are proposed for estimating a PDE model. The development of the proposed methodologies will be guided by analysis of a real monitoring longitudinal data on prevalence of various Bartonella variants (genotypes) in a natural population of rodents (cotton rats).<br/><br/><br/>The research team consists of two statisticians from two academic institutions and one epidemiologist from the CDC, who have worked closely together for a number of years. The proposed works will provide general tools for quantifying an epidemiological similarity between newly detected pathogen variant and known bacterial species, which contribute to the general problem on the assessment of bio-threat associated with newly detected variants. The proposed estimation methods can be generally applicable for estimating PDE models used in epidemiological studies, as well as in other fields, e.g. finance. A computer package implementing the proposed methods will be freely available to the public. The research team will continue to maintain the strong record of training PhD students in cross-disciplinary research.
