Virus-cell Interaction

Overview

Virus-cell interaction is the study of the molecular and cellular events through which a virus engages a host cell, encompassing attachment, entry, replication, assembly, and release, as well as the cellular responses these processes provoke. Because viruses cannot reproduce independently, they exploit host receptors, pathways, and machinery at each stage, and the specific interactions involved determine cell tropism, the efficiency of infection, and the outcome for the cell. These interactions underlie viral pathogenesis, shaping how infection causes cellular damage, alters function, or establishes persistence, and they trigger host defenses such as innate immune signaling that the virus may in turn antagonize. Investigating virus-cell interaction identifies the cellular components a virus depends on, which can serve as targets for antiviral therapy, and reveals markers of infection useful for diagnosis. This knowledge informs the development of interventions across the field, including antiviral drugs that block specific steps of the viral life cycle, vaccines that elicit responses against entry or replication, and immune-based strategies; it also became central to understanding and responding to emerging threats such as pandemic coronaviruses. Insights from these interactions further support the engineering of viruses for beneficial uses, including gene delivery and oncolytic approaches to cancer. By clarifying how viruses and cells influence one another, the study of virus-cell interaction advances both virology and the design of treatments and preventive measures.

Research published in this journal

5 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 5 articles above have been cited 12 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

• Changes in Solo and Partnered Sexual Behaviors following the First COVID-19 Wave: Data from an International Study of 26 Countries
  D. Hensel et al. · 2023 · International Journal of Sexual Health
• Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study
  2022 · JMIR Formative Research
• Usage of Country Evaluations for Sustainable Supplier Selection
  2022 · Progress in IS
• Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study
  2022 · JMIR Formative Research
• Investigating the Effect of Economic and Social Factors on the Spread of Covid-19 Epidemic in the World by Factor Analysis Method
  2021 · Health Research Journal
• ‘Exploring socioeconomic status as a global determinant of COVID-19 prevalence, using statistical, exploratory data analytic, and supervised machine learning techniques.’ (Preprint)
  2021 ·
• Investigating the Effect of Economic and Social Factors on the Spread of Covid-19 Epidemic in the World by Factor Analysis Method
  2021 · Health Research Journal
• Supervised machine learning based prediction of 2021 cross-national COVID-19 prevalence rates using historical infection data and socioeconomic indicators. (Preprint)
  Luke Winston et al. · 2021 ·

A sample of recent works citing this journal's research on Virus-cell Interaction, linking to each citing work.

This page summarises published research for orientation; it is not medical or professional advice.

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