sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 3; peer review: 2 approved]

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 develop...

Full description

Bibliographic Details
Main Author: Kamil Erguler
Format: Article
Language:English
Published: F1000 Research Ltd 2020-09-01
Series:F1000Research
Online Access:https://f1000research.com/articles/7-1220/v3
id doaj-b19558af4b1c4f7cb513fe9f7e7f8e80
record_format Article
spelling doaj-b19558af4b1c4f7cb513fe9f7e7f8e802020-11-25T04:09:19ZengF1000 Research LtdF1000Research2046-14022020-09-01710.12688/f1000research.15824.329565sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 3; peer review: 2 approved]Kamil Erguler0The Cyprus Institute, Climate and Atmosphere Research Center (CARE-C), 20 Konstantinou Kavafi Street, 2121, Aglantzia, Nicosia, CyprusThis 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.https://f1000research.com/articles/7-1220/v3
collection DOAJ
language English
format Article
sources DOAJ
author Kamil Erguler
spellingShingle Kamil Erguler
sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 3; peer review: 2 approved]
F1000Research
author_facet Kamil Erguler
author_sort Kamil Erguler
title sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 3; peer review: 2 approved]
title_short sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 3; peer review: 2 approved]
title_full sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 3; peer review: 2 approved]
title_fullStr sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 3; peer review: 2 approved]
title_full_unstemmed sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 3; peer review: 2 approved]
title_sort spop: age-structured discrete-time population dynamics model in c, python, and r [version 3; peer review: 2 approved]
publisher F1000 Research Ltd
series F1000Research
issn 2046-1402
publishDate 2020-09-01
description 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.
url https://f1000research.com/articles/7-1220/v3
work_keys_str_mv AT kamilerguler spopagestructureddiscretetimepopulationdynamicsmodelincpythonandrversion3peerreview2approved
_version_ 1724422415832842240