Infectivity of plant viruses: A case example of the generalized Poisson model for tobacco mosaic virus
Source abstract
Abstract
This study proposes a generalized Poisson model (GPM), formulated as a stretched exponential (Weibull) function, to describe the infectivity of plant viruses, specifically tobacco mosaic virus and tomato bushy stunt virus. The GPM addresses the dimensional mismatch between virus concentration, typically measured in volumetric units (three-dimensional), and lesion formation observed on leaf surfaces (two-dimensional). By translating virus concentration into a surface-based representation, the model captures the lesion formation patterns observed under various experimental conditions. It is shown that the dimensionless form of GPM, through a normalization of lesion number and virus concentrations, converges onto a single GPM curve, suggesting that plant virus infectivity can be a simple probabilistic matter and is approximated by the well-known Weibull probability distribution.
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Research context
OpenAlex topics: Plant Virus Research Studies · Evolution and Genetic Dynamics · Bacteriophages and microbial interactions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical, Electronics and Computer Engineering, Gifu University 1 , Gifu 501-1193,
Department of Informatics Engineering, Universidade Nacional Timor Lorosa’e 2 , Hera,
References (28)
Show source-provided references
1. Global dimensions of plant virus diseases: Current status and future perspectives10.1146/annurev-virology-092818-015606 ↗
2. Plant virology in the 21st century in China: Recent advances and future directions10.1111/jipb.13580 ↗
3. Vector acquisition and co-inoculation of two plant viruses influences transmission, infection, and replication in new hosts10.1038/s41598-022-24880-5 ↗
4. How do plant viruses induce disease? Interactions and interference with host components10.1099/vir.0.034603-0 ↗
5. Tobacco mosaic virus virulence and avirulence10.1098/rstb.1999.0416 ↗
6. Comparison of models for quantification of tomato brown rugose fruit virus based on a bioassay using a local lesion host10.3390/plants11243443 ↗
7. Within-host spatiotemporal dynamics of plant virus infection at the cellular level10.1371/journal.pgen.1004186 ↗
8. The roles of movement and coat proteins in the transport of tobamoviruses between plant cells10.3389/fpls.2025.1580554 ↗
9. Tobacco mosaic virus infection spreads cell to cell as intact replication complexes10.1073/pnas.0401221101 ↗
10. Unraveling the mechanisms of virus-induced symptom development in plants10.3390/plants12152830 ↗
11. The cell biology of tobacco mosaic virus replication and movement10.3389/fpls.2013.00012 ↗
12. Quantifying plant viruses: Evolution from bioassay to infectivity dilution curves along the model of tobamoviruses10.3390/v16030440 ↗
13. Concentration of tobacco mosaic virus in tomato plants throughout the growing season of two glasshouse crops and a field crop
14. Interpreting relationships between the concentrations of plant viruses and numbers of local lesions10.1099/00221287-4-1-53 ↗
15. The statistical analysis of plant virus assays: A transformation to include lesion numbers with small means10.1099/00221287-13-1-91 ↗
16. A mathematical model for the infectivity-dilution curve of tobacco mosaic virus: Theoretical considerations10.1016/0042-6822(67)90271-1 ↗
17. Association of logistic and poisson models of infection with some physical characteristics of a single component plant virus10.1016/0166-0934(90)90142-3 ↗
18. Modeling bacterial growth responses10.1007/BF01584189 ↗
19. A compressed logistic equation on bacteria growth: Inferring time-dependent growth rate10.1088/1478-3975/ac8c15 ↗
20. A stretched logistic equation for pandemic spreading10.1016/j.chaos.2020.110113 ↗
21. Analyzing the COVID-19 transmission dynamics with the stretched logistic model: Origin of anomalous time-dependent infection rates10.1063/5.0248156 ↗
22. Relationship between plant virus concentration and infectivity: A ‘growth curve’ model10.1016/0166-0934(87)90084-x ↗
23. 1988 · 33110.1016/0166-0934(88)90028-6 ↗
24. Photoinduced effects and metastability in amorphous semiconductors and insulators10.1080/00018739500101576 ↗
25. An in-depth review of the Weibull model with a focus on various parameterizations10.3390/math12010056 ↗
26. The two-parameter Odd Lindley Weibull lifetime model with properties and applications10.5539/ijsp.v7n4p57 ↗
27. The Weibull-exponential distribution: Its properties and applications10.3923/jas.2015.1305.1311 ↗
28. Reliability analysis: Failure data analysis with extended Weibull distribution10.1080/03610910701236081 ↗