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Modelling the Effect of Viruses on Insect Survival: Using a Second-Order Phase Transition Model to Describe

Vladislav Soukhovolsky1, Anton Kovalev2, Olga Tarasova3,4

  • 1V.N. Sukachev Institute of Forest, Siberian Branch of Russian Academy of Sciences, Krasnoyarsk 660036, Russia.

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|October 28, 2025
PubMed
Summary
This summary is machine-generated.

This study uses theoretical physics models to quantify how virus dose affects insect survival. The findings show these models accurately predict insect mortality, simplifying toxicity testing for various agents.

Keywords:
doseinsectslethal timemodelmortalitysecond-order phase transitionstemporal dynamicsviruses

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Area of Science:

  • Theoretical Physics
  • Insect Pathology
  • Ecotoxicology

Background:

  • Understanding virus-insect interactions is crucial for pest management.
  • Quantifying the dose-response relationship of viral pathogens is essential for ecological and agricultural applications.

Purpose of the Study:

  • To apply second-order phase transition models to quantify virus effects on insect survival.
  • To assess the accuracy of these models in describing experimental data.
  • To explore the potential for reducing experimental effort in toxicity assessments.

Main Methods:

  • Utilized two model approaches based on second-order phase transition theory.
  • Analyzed temporal dynamics of larval survival (time-effect curves).
  • Examined the relationship between survival proportion and virus dose (dose-effect curves).

Main Results:

  • Second-order phase transition models accurately described experimental data (R² ≈ 0.95).
  • Models effectively characterized the impact of nucleopolyhedrovirus (NPV) and cypovirus (CPV) on tested insect species.
  • Demonstrated a relationship between dose-time and dose-effect model parameters, enabling estimation from single measurements.

Conclusions:

  • The proposed models provide an accurate and efficient method for assessing virus efficacy against insect pests.
  • The models can be generalized to evaluate a wide range of toxicants on different populations.
  • This approach significantly reduces the labor and cost associated with toxicity testing.