Age-Structured Population Dynamics Model

Description

Implements the "spop" class for age-structured population dynamics modelling. For more information, see class description.

Author(s)

Kamil Erguler, Associate Research Scientist, EEWRC, The Cyprus Institute k.erguler@cyi.ac.cy

References

• Kamil Erguler, Stephanie E Smith-Unna, Joanna Waldock, Yiannis Proestos, George K Christophides, Jos Lelieveld, Paul E Parham. Large-scale modelling of the environmentally-driven population dynamics of temperate Aedes albopictus (Skuse). PLOS ONE, 2016

• Kamil Erguler, Nastassya L Chandra, Yiannis Proestos, Jos Lelieveld, George K Christophides, Paul E Parham. A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology. PLOS ONE, 2017

See Also

Useful links:

• https://github.com/kerguler/albopictusR

Add batch

Description

Introduce a batch of individuals with a given age

Usage

add(x) <- value

## S4 replacement method for signature 'spop,data.frame'
add(x) <- value

Arguments

Read dead

Description

Read the number of dead individuals after each iteration

Usage

dead(x)

## S4 method for signature 'spop'
dead(x)

Arguments

Read developed

Description

Read the number of individuals designated to complete their development

Usage

developed(x)

## S4 method for signature 'spop'
developed(x)

Arguments

Read devtable

Description

Read the number, age, and development cycles of individuals completing their development after each iteration

Usage

devtable(x)

## S4 method for signature 'spop'
devtable(x)

Arguments

Gamma probability of death or development

Description

Gamma-distributed probability of death or development happening in the next iteration

Usage

gamma_dist_prob(xr, mn, std)

Arguments

Iterate population

Description

Iterate the population for one day

Usage

iterate(x) <- value

## S4 replacement method for signature 'spop,data.frame'
iterate(x) <- value

Arguments

Negative binomial probability of death or development

Description

Negative binomial-distributed probability of death or development happening in the next iteration

Usage

nbinom_dist_prob(xr, mn, std)

Arguments

Iterate population without incrementing age or development

Description

Iterate the population for one day keeping age and development fixed

Usage

perturb(x) <- value

## S4 replacement method for signature 'spop,data.frame'
perturb(x) <- value

Arguments

Read size

Description

Read the total number of individuals

Usage

size(x)

## S4 method for signature 'spop'
size(x)

Arguments

An S4 class to represent an age-structured population

Description

• spop implements the deterministic and stochastic age-structured population dynamics models described in Erguler et al. 2016 and 2017

• add introduces a batch of individuals with a given age, completed development cycles, and degree of development (default: 0)

• iterate iterates the population for one day and calculates (overwrites) the number of dead individuals and the number of individuals designated to complete their development

• devtable reads the number, age, and development cycle of individuals designated to complete their development

• developed reads the total number of individuals designated to complete their development

• dead reads the number of dead individuals after each iteration

• size reads the total number of individuals

Details

This is an R implementation of the age-structured population dynamics models described in Erguler et al. 2016 and 2017. The spop class records the number and age of individuals and implements two processes to exit from the population: development and death. The two processes act upon the population sequentially; survival is imposed prior to development. If the population survives for one day, then, it is allowed to grow and complete its development. Survival and development are defined either with an age-independent daily probability, or an age-dependent gamma- or negative binomial-distributed probability.

• stochastic: a logical value indicating a deterministic or a stochastic population dynamics

• prob: a character string indicating the basis of age-dependent survival or development (gamma: gamma-distributed, nbinom: negative binomial-distributed)

Examples

# Generate a population with stochastic dynamics
s <- spop(stochastic=TRUE)
# Add 1000 20-day-old individuals
add(s) <- data.frame(number=1000,age=20)

# Iterate one day without death and assume development in 20 (+-5) days (gamma-distributed)
iterate(s) <- data.frame(dev_mean=20,dev_sd=5,death=0)
print(developed(s))

# Iterate another day assuming no development but age-dependent survival
# Let each individual survive for 20 days (+-5) (gamma-distributed)
iterate(s) <- data.frame(death_mean=20,death_sd=5,dev=0)
print(dead(s))
# Note that the previous values of developed and dead will be overwritten by this command

# Generate a deterministic population and observe the difference
s <- spop(stochastic=FALSE)
add(s) <- data.frame(number=1000,age=20)

iterate(s) <- data.frame(dev_mean=20,dev_sd=5,death=0)
print(developed(s))

iterate(s) <- data.frame(death_mean=20,death_sd=5,dev=0)
print(dead(s))