Special Issue "Bioinformatics and Computational Biology"

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (31 July 2017).

Special Issue Editor

Prof. Dr. Brigitte Servatius
Website
Guest Editor
Mathematics Department, Worcester Polytechnic Institute, Worcester, MA 01609-2280, USA
Interests: Combinatorics; Rigiditiy of Structures; Geometric Foundations of Computer Aided Design; Symmetry and Duality; The History and Philosophy of Mathematics
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Special Issue Information

Dear Colleagues,

Biology, biotechnology, computer science, mathematical sciences, and data science interactively contribute to progress in bioinformatics. In the laboratory and through clinical research, massive data is produced. A mathematical model of a biological process may lead to computer simulation and the model may be validated by comparison of real data to simulation data. The human genome project and the start of the personal human genome project got a lot of press coverage and exemplify very well the need for development and maintenance of a robust cyber-infrastructure to enable transformative biological research.

We seek papers on innovative approaches to the application of mathematics and computer science to problems in biology and papers that address the critical properties of the cyber-infrastructure for continued progress in biological research.

Prof. Dr. Brigitte Servatius
Guest Editor

Manuscript Submission Information

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Keywords

  • bioinformatics
  • neuroinformatics
  • computational biology
  • statistical genetics
  • functional genomics
  • molecular medicine

Published Papers (1 paper)

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Research

Open AccessArticle
Deterministic Seirs Epidemic Model for Modeling Vital Dynamics, Vaccinations, and Temporary Immunity
Mathematics 2017, 5(1), 7; https://doi.org/10.3390/math5010007 - 17 Jan 2017
Cited by 3
Abstract
In this paper, the author proposes a new SEIRS model that generalizes several classical deterministic epidemic models (e.g., SIR and SIS and SEIR and SEIRS) involving the relationships between the susceptible S, exposed E, infected I, and recovered R individuals [...] Read more.
In this paper, the author proposes a new SEIRS model that generalizes several classical deterministic epidemic models (e.g., SIR and SIS and SEIR and SEIRS) involving the relationships between the susceptible S, exposed E, infected I, and recovered R individuals for understanding the proliferation of infectious diseases. As a way to incorporate the most important features of the previous models under the assumption of homogeneous mixing (mass-action principle) of the individuals in the population N, the SEIRS model utilizes vital dynamics with unequal birth and death rates, vaccinations for newborns and non-newborns, and temporary immunity. In order to determine the equilibrium points, namely the disease-free and endemic equilibrium points, and study their local stability behaviors, the SEIRS model is rescaled with the total time-varying population and analyzed according to its epidemic condition R0 for two cases of no epidemic (R0 ≤ 1) and epidemic (R0 > 1) using the time-series and phase portraits of the susceptible s, exposed e, infected i, and recovered r individuals. Based on the experimental results using a set of arbitrarily-defined parameters for horizontal transmission of the infectious diseases, the proportional population of the SEIRS model consisted primarily of the recovered r (0.7–0.9) individuals and susceptible s (0.0–0.1) individuals (epidemic) and recovered r (0.9) individuals with only a small proportional population for the susceptible s (0.1) individuals (no epidemic). Overall, the initial conditions for the susceptible s, exposed e, infected i, and recovered r individuals reached the corresponding equilibrium point for local stability: no epidemic (DFE X ¯ D F E ) and epidemic (EE X ¯ E E ). Full article
(This article belongs to the Special Issue Bioinformatics and Computational Biology)
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