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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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An R-Based Landscape Validation of a Competing Risk Model
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Comparison of disease progress curves.

C A Gilligan1

  • 1Department of Applied Biology, University of Cambridge, Pembroke Street, Cambridge, CB2 3DX.

The New Phytologist
|April 20, 2021
PubMed
Summary

This study introduces a new analysis method to assess disease control strategies in botanical epidemics. It reveals that treatments primarily delay disease onset or reduce host carrying capacity, rarely affecting epidemic rate parameters.

Area of Science:

  • Plant Pathology
  • Epidemiology
  • Quantitative Biology

Background:

  • Botanical epidemics pose significant threats to crop yields.
  • Understanding disease dynamics is crucial for effective control.
  • Current methods for analyzing disease control effects can be limited.

Purpose of the Study:

  • To describe and validate a novel analytical method for comparing nonlinear disease progress models.
  • To assess the impact of various disease control measures on epidemic dynamics.
  • To differentiate between treatments that slow/delay epidemics versus those reducing host carrying capacity.

Main Methods:

  • Applied nonlinear models (logistic and monomolecular) to analyze published disease progress curves.
  • Compared model parameters fitted separately versus with constrained common parameters.
Keywords:
Epidemiologydisease controlmodellingnonlinear model

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  • Utilized residual mean squares to test the significance of treatment effects.
  • Examined host-pathogen systems including Phytophthora infestans, Fusarium oxysporum, Puccinia recondita, Sclerotium rolfsii.

    • Main Results:

    • The analytical method effectively distinguished between different disease control strategies.
    • Treatments significantly impacted epidemic dynamics by reducing carrying capacity or delaying onset.
    • Few control methods demonstrably altered the rate parameters of the logistic or monomolecular models.
    • Genetic, chemical, and cultural control methods were evaluated.

    Conclusions:

    • The developed method is sensitive for evaluating disease control efficacy.
    • Disease management strategies can be categorized by their primary mechanism of action (delay vs. capacity reduction).
    • Further research may focus on identifying treatments that specifically target epidemic rate parameters.