sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 1; referees: 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...

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Main Author: Kamil Erguler
Format: Article
Language:English
Published: F1000 Research Ltd 2018-08-01
Series:F1000Research
Online Access:https://f1000research.com/articles/7-1220/v1
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spelling doaj-2520a7f0682f41e196ce3334ccd88b502020-11-25T03:06:26ZengF1000 Research LtdF1000Research2046-14022018-08-01710.12688/f1000research.15824.117272sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 1; referees: 2 approved]Kamil Erguler0Energy, Environment and Water Research Center, The Cyprus Institute, Nicosia, 2121, 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-delayed epidemiological processes.https://f1000research.com/articles/7-1220/v1
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 1; referees: 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 1; referees: 2 approved]
title_short sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 1; referees: 2 approved]
title_full sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 1; referees: 2 approved]
title_fullStr sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 1; referees: 2 approved]
title_full_unstemmed sPop: Age-structured discrete-time population dynamics model in C, Python, and R [version 1; referees: 2 approved]
title_sort spop: age-structured discrete-time population dynamics model in c, python, and r [version 1; referees: 2 approved]
publisher F1000 Research Ltd
series F1000Research
issn 2046-1402
publishDate 2018-08-01
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-delayed epidemiological processes.
url https://f1000research.com/articles/7-1220/v1
work_keys_str_mv AT kamilerguler spopagestructureddiscretetimepopulationdynamicsmodelincpythonandrversion1referees2approved
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