A Method of Profiling a Heterogeneous Mixed Population’s Dynamics: A Statistical and Informatics Framework

Abstract

This work presents how to profile the frequencies of occurrences staged at different given time or space with varying inferring factors for the population’s member identification and monitoring. We created two major design of the study; one is with equal time interval components of study and for an overlapping trajectory of components. Given a Space S there are many possible trajectories of a phenomenon of studies within a given space from numerous trajectories at different states say location or time. To uncover the dynamics of individual members, uncover lost important dynamics state/behaviors in a heterogeneous population of the phenomenon of studies becomes cumbersome considering the complication of distribution functions. This framework is designed to handle the problem of developmental interval measurements and monitoring where distribution changes as a function of time and it is cumbersome to use models with a distribution approach to capture the dynamic of population shift effectively. This method may be important in the real data for uncovering cell type studies in a given heterogeneous population and NGS-data with several resequencing reads trajectory study for lost reads. The method is a trajectory learning approaches-the work is to design a population-based framework for learning and uncovering dynamics of trajectory growth/increase in a heterogeneous population of any phenomenon of study. We wrote R code using default detection rates parameters in the different scenarios of the simulation study, using the open-source software JAGS via the R package ’R2jags’ on CRAN.Rproject.org/package=jagsUI. The software was used to synthesis various scenarios of the population with equal interval and overlapping trajectories. The demonstrated application on how effective our method will uncover population snapshot situation effect, proliferation rate, death rate-decline/disappearances shift in population composition abundance contents (population transitions)-under close assumption, under relaxing close assumption and under the steady-state assumption of contents of composition shifts. This statistical/informatics framework can be applied to a biological population(population of a biological cell of heterogeneous types, income dynamics of increasing population, capture and recapture problems, income, and social composition variety in populations. Triage cases and COVID-19 geographical spread trajectory within selected intervals.