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Related Concept Videos

Viral Recombination00:57

Viral Recombination

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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.
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Retrovirus Life Cycles01:10

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Retroviruses have a single-stranded RNA genome that undergoes a special form of replication. Once the retrovirus has entered the host cell, an enzyme called reverse transcriptase synthesizes double-stranded DNA from the retroviral RNA genome. This DNA copy of the genome is then integrated into the host’s genome inside the nucleus via an enzyme called integrase. Consequently, the retroviral genome is transcribed into RNA whenever the host’s genome is transcribed, allowing the...
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Related Experiment Video

Updated: Jun 19, 2025

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
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Modeling zoonotic and vector-borne viruses.

Seth D Judson1, David W Dowdy2

  • 1Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

Current Opinion in Virology
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Summary
This summary is machine-generated.

Models for emerging viruses like Ebola and COVID-19 have advanced, aiding scientists and policymakers. Further work is needed to translate these crucial viral disease models into actionable public health decisions.

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Area of Science:

  • Epidemiology
  • Mathematical Modeling
  • Public Health

Background:

  • Recent epidemics, including Ebola virus disease (2013-2016) and coronavirus disease 2019 (COVID-19), have accelerated the development of predictive models for emerging zoonotic and vector-borne viruses.
  • These models are critical tools for understanding disease dynamics and informing public health responses to outbreaks.

Purpose of the Study:

  • To review the primary objectives and methodologies of existing models for emerging viral diseases.
  • To provide guidance for scientists and decision-makers utilizing these models.
  • To identify current challenges and future directions in viral disease modeling.

Main Methods:

  • A comprehensive review of scientific literature on mathematical and statistical models for emerging zoonotic and vector-borne viruses.
  • Analysis of model characteristics, including scope (retrospective vs. prospective), data utilization (spatial, temporal), and methodological approaches (statistical, mechanistic).
  • Exploration of advancements such as hybrid/ensemble models and artificial intelligence in disease modeling.

Main Results:

  • Model applications range from understanding past outbreaks to forecasting future trends, employing diverse data sources and statistical or mechanistic approaches.
  • Emerging techniques like hybrid/ensemble modeling and artificial intelligence present new avenues for enhancing predictive accuracy and scope.
  • Significant challenges persist in translating model outputs into practical, actionable decisions for public health interventions, especially in high-risk regions.

Conclusions:

  • Continued development and refinement of models are essential for effective management of emerging viral threats.
  • Addressing the gap between model development and real-world application requires identifying specific viral disease modeling needs, improving model validation processes, and fostering collaboration with policymakers.
  • Integrating diverse modeling approaches and emerging technologies can enhance preparedness for future viral disease outbreaks.