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Related Concept Videos

Influenza01:27

Influenza

Influenza is an acute, highly communicable viral disease that affects the respiratory tract and is responsible for seasonal epidemics worldwide. Influenza A is the most prevalent type associated with widespread outbreaks and is subtyped based on two surface glycoproteins: hemagglutinin (H) and neuraminidase (N), as in H1N1. These glycoproteins are essential for viral infectivity, transmission, and immune recognition. Transmission occurs primarily through respiratory droplets and contaminated...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
Viral Recombination00:57

Viral Recombination

Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
Leaky Scanning02:28

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...
Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:

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Related Experiment Video

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Modeling Dysplastic and Functional Lung Alveolar Repair after Influenza Infection
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A lattice model for influenza spreading.

Antonella Liccardo1, Annalisa Fierro

  • 1Physics Department, Università degli Studi di Napoli "Federico II", Napoli, Italy. liccardo@na.infn.it

Plos One
|May 30, 2013
PubMed
Summary

This study models influenza spread using a dynamic contact network. Accurate contact pattern data significantly improves predictions of epidemic spreading, highlighting its importance in disease analysis.

Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Network Science

Background:

  • Influenza transmission dynamics are complex and influenced by individual contact patterns.
  • Existing models often simplify the dynamic nature of social contacts.
  • Understanding age-specific contact networks is crucial for accurate epidemic forecasting.

Purpose of the Study:

  • To develop a stochastic SIR model incorporating a dynamic, age-structured contact network.
  • To validate the model against real-world influenza A (H1N1) epidemiological data.
  • To assess the impact of contact pattern accuracy on epidemic spread predictions.

Main Methods:

  • Construction of a D-dimensional lattice model representing dynamic individual contacts.
  • Incorporation of age-structured population dynamics and nearest-neighbor interactions.

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  • Parameter calibration using Polymod survey data on age-dependent daily contacts.
  • Model validation against Italian epidemiological data for influenza A (H1N1).

    • Main Results:

    • The model, when calibrated with accurate contact patterns, shows good agreement with epidemiological data.
    • The stochastic SIR model successfully predicts epidemiological parameters for influenza A (H1N1).
    • The study demonstrates the significant predictive power of average individual contact patterns in epidemic analysis.

    Conclusions:

    • Dynamic contact networks, accurately represented by models, are essential for understanding infectious disease spread.
    • The fidelity of contact pattern data directly influences the reliability of epidemic models.
    • Average contact patterns encode substantial information critical for analyzing epidemic dynamics.