Abstract
This article describes the sPop packages implementing the deterministic and stochastic versions of an age-structured discrete-time population dynamics model. The packages enable mechanistic modelling of a population by monitoring the age and development stage of each individual. Survival and development are included as the main effectors and they progress at a user-defined pace: follow a fixed-rate, delay for a given time, or progress at an age-dependent manner. The model is implemented in C, Python, and R with a uniform design to ease usage and facilitate adoption. Early versions of the model were previously employed for investigating climate-driven population dynamics of the tiger mosquito and the chikungunya disease spread by this vector. The sPop packages presented in this article enable the use of the model in a range of applications extending from vector-borne diseases towards any age-structured population including plant and animal populations, microbial dynamics, host-pathogen interactions, infectious diseases, and other time-dependent epidemiological processes.
Highlights
The age-structured population dynamics model Heterogeneity is inherent in most naturally occurring populations
The sPop packages provide a flexible number of age and development categories, include both deterministic and stochastic dynamics, and offer high-speed simulations to facilitate parameter inference
The same principles apply for the survival process, where we provide the mean and the standard deviation of the gamma-distribution for each age- development group as calculated by the death function
Summary
See referee reports data[4], the degree day methodology[5], and age- or stage-structured population dynamics modelling[6] are common approaches for investigating such phenomena. A common work-around is to introduce predetermined intermittent stages to account for the different characteristics of each stage and the time it takes to pass from one to another This approach has been extensively used in various contexts, e.g. to model animal development[7], insect life cycle[8,9], disease transmission[10,11], and economic surplus[12]. Numerous packages including popbio[13], demogR14, and bayesPop[15] have been implemented to facilitate modelling and analysis of age- and stage-structured projection matrix models. The sPop packages provide a flexible number of age and development categories, include both deterministic and stochastic dynamics, and offer high-speed simulations to facilitate parameter inference
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