# Generalised logistic function Effect of varying parameter $\nu$ . A = 0, all other parameters are 1.

The generalized logistic function or curve, also known as Richards' curve, originally developed for growth modelling, is an extension of the logistic or sigmoid functions, allowing for more flexible S-shaped curves:

$Y(t)=A+{K-A \over (C+Qe^{-Bt})^{1/\nu }}$ where $Y$ = weight, height, size etc., and $t$ = time.

It has five parameters:

• $A$ : the lower asymptote;
• $K$ : the upper asymptote when $C=1$ . If $A=0$ and $C=1$ then $K$ is called the carrying capacity;
• $B$ : the growth rate;
• $\nu >0$ : affects near which asymptote maximum growth occurs.
• $Q$ : is related to the value $Y(0)$ • $C$ : typically takes a value of 1. Otherwise, the upper asymptote is $A+{K-A \over C^{\,1/\nu }}$ The equation can also be written:

$Y(t)=A+{K-A \over (C+e^{-B(t-M)})^{1/\nu }}$ where $M$ can be thought of as a starting time, $t_{0}$ (at which $Y(t_{0})=A+{K-A \over (C+1)^{1/\nu }}$ )

Including both $Q$ and $M$ can be convenient:

$Y(t)=A+{K-A \over (C+Qe^{-B(t-M)})^{1/\nu }}$ this representation simplifies the setting of both a starting time and the value of Y at that time.

The general model is sometimes named a "Richards' curve" after F. J. Richards, who proposed the general form for the family of models in 1959.

The logistic, with maximum growth rate at time $M$ , is the case where $Q=\nu =1$ .

## Generalised logistic differential equation

A particular case of the generalised logistic function is:

$Y(t)={K \over (1+Qe^{-\alpha \nu (t-t_{0})})^{1/\nu }}$ which is the solution of the Richards' differential equation (RDE):

$Y^{\prime }(t)=\alpha \left(1-\left({\frac {Y}{K}}\right)^{\nu }\right)Y$ with initial condition

$Y(t_{0})=Y_{0}$ where

$Q=-1+\left({\frac {K}{Y_{0}}}\right)^{\nu }$ provided that ν > 0 and α > 0.

The classical logistic differential equation is a particular case of the above equation, with ν =1, whereas the Gompertz curve can be recovered in the limit $\nu \rightarrow 0^{+}$ provided that:

$\alpha =O\left({\frac {1}{\nu }}\right)$ In fact, for small ν it is

$Y^{\prime }(t)=Yr{\frac {1-\exp \left(\nu \ln \left({\frac {Y}{K}}\right)\right)}{\nu }}\approx rY\ln \left({\frac {Y}{K}}\right)$ The RDE models many growth phenomena, including the growth of tumours. In oncology its main biological features are similar to those of the Logistic curve model.

The RDE models are widely used to describe infection trajectory in epidemiological modeling; see  for COVID-19 application.

## Gradient of generalized logistic function

When estimating parameters from data, it is often necessary to compute the partial derivatives of the logistic function with respect to parameters at a given data point $t$ (see ). For the case where $C=1$ ,

{\begin{aligned}\\{\frac {\partial Y}{\partial A}}&=1-(1+Qe^{-B(t-M)})^{-1/\nu }\\\\{\frac {\partial Y}{\partial K}}&=(1+Qe^{-B(t-M)})^{-1/\nu }\\\\{\frac {\partial Y}{\partial B}}&={\frac {(K-A)(t-M)Qe^{-B(t-M)}}{\nu (1+Qe^{-B(t-M)})^{{\frac {1}{\nu }}+1}}}\\\\{\frac {\partial Y}{\partial \nu }}&={\frac {(K-A)\ln(1+Qe^{-B(t-M)})}{\nu ^{2}(1+Qe^{-B(t-M)})^{\frac {1}{\nu }}}}\\\\{\frac {\partial Y}{\partial Q}}&=-{\frac {(K-A)e^{-B(t-M)}}{\nu (1+Qe^{-B(t-M)})^{{\frac {1}{\nu }}+1}}}\\\\{\frac {\partial Y}{\partial M}}&=-{\frac {(K-A)QBe^{-B(t-M)}}{\nu (1+Qe^{-B(t-M)})^{{\frac {1}{\nu }}+1}}}\\\end{aligned}} ## Special cases

The following functions are specific cases of Richards' curves:

## Footnotes

1. ^ Fekedulegn, Desta; Mairitin P. Mac Siurtain; Jim J. Colbert (1999). "Parameter Estimation of Nonlinear Growth Models in Forestry" (PDF). Silva Fennica. 33 (4): 327–336. Archived from the original (PDF) on 2011-09-29. Retrieved 2011-05-31.