Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Discrete-time Fourier transform01:26

Discrete-time Fourier transform

1.1K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
1.1K
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

683
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
683
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

671
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
671
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

898
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
898
Reproductive Cloning01:27

Reproductive Cloning

32.7K
Reproductive cloning is the process of producing a genetically identical copy—a clone—of an entire organism. While clones can be produced by splitting an early embryo—similar to what happens naturally with identical twins—cloning of adult animals is usually done by a process called somatic cell nuclear transfer (SCNT).
Somatic Cell Nuclear Transfer
In SCNT, an egg cell is taken from an animal and its nucleus is removed, creating an enucleated egg. Then a somatic...
32.7K
Discrete Fourier Transform01:15

Discrete Fourier Transform

876
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
876

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A celebration of Fred Brauer's legacy in mathematical biology.

Journal of mathematical biology·2023
Same author

Impact of tetravalent dengue vaccination with screening, ADE, and altered infectivity on single-serotype dengue and Zika transmission.

Journal of mathematical biology·2023
Same author

Modeling anthrax-rabies interactions in zebra-jackal cycles.

Journal of theoretical biology·2020
Same author

Can scavengers save zebras from anthrax? A modeling study.

Infectious Disease Modelling·2020
Same author

Cost analysis of vaccination in tick-mouse transmission of Lyme disease.

Journal of theoretical biology·2020
Same author

Coinfection, Altered Vector Infectivity, and Antibody-Dependent Enhancement: The Dengue-Zika Interplay.

Bulletin of mathematical biology·2020

Related Experiment Video

Updated: Jan 25, 2026

Ultrasonography in Experimental Reproductive Investigations on Rats
07:59

Ultrasonography in Experimental Reproductive Investigations on Rats

Published on: December 2, 2017

15.0K

Invasion reproductive numbers for discrete-time models.

Omomayowa Olawoyin1, Christopher Kribs1

  • 1Department of Mathematics, University of Texas at Arlington, 411 South Nedderman Drive Box 19408, Arlington, TX 76019, USA.

Infectious Disease Modelling
|April 25, 2019
PubMed
Summary

This study introduces invasion reproductive numbers (IRNs) for discrete-time models, revealing how event sequencing impacts pathogen competition and coinfection dynamics. The findings are crucial for understanding viral interactions.

Keywords:
CoinfectionCompetitive exclusionDiscrete-time modelInvasion reproductive number

More Related Videos

Time-Resolved Fluorescence Imaging and Analysis of Cancer Cell Invasion in the 3D Spheroid Model
07:42

Time-Resolved Fluorescence Imaging and Analysis of Cancer Cell Invasion in the 3D Spheroid Model

Published on: January 30, 2021

6.9K
Assessment of Ovarian Cancer Spheroid Attachment and Invasion of Mesothelial Cells in Real Time
14:25

Assessment of Ovarian Cancer Spheroid Attachment and Invasion of Mesothelial Cells in Real Time

Published on: May 20, 2014

17.8K

Related Experiment Videos

Last Updated: Jan 25, 2026

Ultrasonography in Experimental Reproductive Investigations on Rats
07:59

Ultrasonography in Experimental Reproductive Investigations on Rats

Published on: December 2, 2017

15.0K
Time-Resolved Fluorescence Imaging and Analysis of Cancer Cell Invasion in the 3D Spheroid Model
07:42

Time-Resolved Fluorescence Imaging and Analysis of Cancer Cell Invasion in the 3D Spheroid Model

Published on: January 30, 2021

6.9K
Assessment of Ovarian Cancer Spheroid Attachment and Invasion of Mesothelial Cells in Real Time
14:25

Assessment of Ovarian Cancer Spheroid Attachment and Invasion of Mesothelial Cells in Real Time

Published on: May 20, 2014

17.8K

Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Infectious Disease Modeling

Background:

  • Invasion reproductive numbers (IRNs) are vital for analyzing pathogen interactions in continuous-time models.
  • Discrete-time models, common in epidemiology, lack exploration of IRNs.

Purpose of the Study:

  • To extend the concept of IRNs to discrete-time models.
  • To investigate the impact of event sequencing on basic reproductive numbers (BRNs) and IRNs in coinfection models.

Main Methods:

  • Developed discrete-time SIS models for two interacting pathogens with coinfection.
  • Calculated BRNs and IRNs, analyzing the effect of event sequencing.

Main Results:

  • The basic reproductive number (BRN) remains unaffected by event order.
  • Invasion reproductive numbers (IRNs) are sensitive to the sequence of events.
  • Predicted pathogen copersistence under cross-immunity, differing from continuous-time models.

Conclusions:

  • Event sequencing significantly influences IRNs and pathogen competition dynamics in discrete-time models.
  • The developed framework allows for novel analyses of coinfection in discrete epidemiological systems.