Logistic growth curve fitting (Plantae)

Knowledge graph centered on Logistic growth curve fitting (Plantae) with 18 nodes and 41 connections. Top connected: climate change, Linum lewisii, Silene, drought stress, HOBO sensor environmental monitoring.

Method synopsis

Fitting logistic growth models to disease prevalence time series to extract key epidemiological parameters for subsequent environmental correlation analysis.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — A multi-year case study highlighting the influence of hydrological conditions on epidemic dynamics in a natural plant pathosystem (2025), Oikos. Implementations in other papers (listed below) may differ.

1. Fit logistic growth models
   Logistic equations were fit using the Gauss-Newton algorithm in the nls() function in R version 4.2.0 to explore epidemics that displayed expected sigmoidal growth, excluding epidemics from 2023 and those with insufficient temporal coverage
   Quantities: Multiple epidemic curves per site per yearDuration: Post-collection analysis phaseConditions: Computational analysis
   Equipment: R statistical software version 4.2.0

Typical Equipment

• R statistical software version 4.2.0

Output Measurements

• maximum prevalence
• epidemic growth rate
• epidemic timing

Papers Using This Protocol (1)

Species Studied With This Protocol