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Related Concept Videos

Prevalence and Incidence01:08

Prevalence and Incidence

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In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health...
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Translation01:31

Translation

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Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of...
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Translation01:31

Translation

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Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...
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Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Initiation of Translation02:33

Initiation of Translation

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Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...
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Termination of Translation01:44

Termination of Translation

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The large ribosomal subunit has several important structures essential to translation. These include the peptidyl transferase center (PTC) - which is the site where the peptide bond is formed - and a large, internal, water-filled tube through which the nascent polypeptide moves. This latter structure is called the Peptide Exit Tunnel, and it begins at the PTC and spans the body of the large ribosomal subunit. During translation, as the nascent polypeptide chain is synthesized, it passes through...
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Translating surveillance data into incidence estimates.

Y Bourhis1, T Gottwald2, F van den Bosch1,3

  • 11 Rothamsted Research, Department of Biointeraction and Crop Protection , Harpenden AL5 2JQ, UK.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|May 21, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new model to accurately estimate disease incidence, even during epidemic growth. The model accounts for varying infection rates and asymptomatic periods, improving disease monitoring accuracy.

Keywords:
disease surveillancesampling theoryspatial epidemiology

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Disease Modeling

Background:

  • Disease incidence estimation is crucial for public health surveillance.
  • Current methods underestimate incidence when disease prevalence changes between monitoring rounds.
  • Asymptomatic periods complicate accurate disease detection and estimation.

Purpose of the Study:

  • To develop a novel incidence estimation model that accounts for epidemic growth and varying sampling.
  • To incorporate the challenge of asymptomatic periods into disease incidence estimation.
  • To provide a practical and accurate tool for infectious disease monitoring.

Main Methods:

  • Development of a new mathematical model for disease incidence estimation.
  • Inclusion of parameters for epidemic growth and asymptomatic periods.
  • Application and validation using stochastic spatial epidemic simulations.

Main Results:

  • The developed model accurately estimates disease incidence, even with dynamic epidemic growth.
  • An approximation of the model demonstrates high accuracy for practical applications.
  • The model and its approximation show consistency across diverse simulation scenarios.

Conclusions:

  • The new model offers improved accuracy for disease incidence estimation in dynamic epidemics.
  • The model provides a valuable tool for infectious disease forecasting and control.
  • An online application of the model is available for practical use in disease monitoring.