egf_model

Sets flags defining a top level nonlinear model of epidemic growth
to be estimated by <code>egf</code>.

Maximum likelihood estimation of nonlinear mixed effects models of
epidemic growth using Template Model Builder ('TMB'). Enables
joint estimation for collections of disease incidence time series,
including time series that describe multiple epidemic waves.
Supports a set of widely used phenomenological models: exponential,
logistic, Richards (generalized logistic), subexponential,
and Gompertz. Provides methods for interrogating model objects
and several auxiliary functions, including one for computing basic
reproduction numbers from fitted values of the initial exponential
growth rate.
Preliminary versions of this software were applied
in Ma et al. (2014) <doi:10.1007/s11538-013-9918-2> and
in Earn et al. (2020) <doi:10.1073/pnas.2004904117>.

Mikael Jagan

epigrowthfit

Nonlinear Mixed Effects Models of Epidemic Growth

Ben Bolker

Jonathan Dushoff

David Earn

Junling Ma

egf_model function

<dl><dt>curve</dt>
<dd>a character string specifying a model for expected cumulative
 incidence as a function of time.</dd><dt>excess</dt>
<dd>a logical flag. If <code>TRUE</code>, then a constant baseline mortality
 rate is estimated.</dd><dt>family</dt>
<dd>a character string specifying a family of discrete probability
 distributions assigned to observations, which are
 the first order differences of observed cumulative incidence.</dd><dt>day_of_week</dt>
<dd>an integer flag. If positive, then day of week effects are
 estimated as offsets relative to the indicated day of week
 (1=Sunday, 2=Monday, and so on).</dd></dl>

Arguments

Define a Top Level Nonlinear Model — egf_model

<dl>

<dt>curve</dt>
<dd>a character string specifying a model for expected cumulative
 incidence as a function of time.</dd>

<dt>excess</dt>
<dd>a logical flag. If <code>TRUE</code>, then a constant baseline mortality
 rate is estimated.</dd>

<dt>family</dt>
<dd>a character string specifying a family of discrete probability
 distributions assigned to observations, which are
 the first order differences of observed cumulative incidence.</dd>

<dt>day_of_week</dt>
<dd>an integer flag. If positive, then day of week effects are
 estimated as offsets relative to the indicated day of week
 (1=Sunday, 2=Monday, and so on).</dd>

</dl>

egf_model: Define a Top Level Nonlinear Model

Description

Usage

Value

Arguments

See Also

Examples