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Authors
Oluwasegun A. Somefun (0000-0002-5171-8026), Kayode F. Akingbade, Folasade M. Dahunsi
Citation
Somefun et al., (2021). NLSIG-COVID19Lab: A modern logistic-growth tool (nlogistic-sigmoid) for descriptively modelling the dynamics of the COVID-19 pandemic process. Journal of Open Source Software, 6(60), 3002, https://doi.org/10.21105/joss.03002
@article{Somefun2021,
doi = {10.21105/joss.03002},
url = {https://doi.org/10.21105/joss.03002},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {60},
pages = {3002},
author = {Oluwasegun A. Somefun and Kayode F. Akingbade and Folasade M. Dahunsi},
title = {`NLSIG-COVID19Lab`: A modern logistic-growth tool (nlogistic-sigmoid) for descriptively modelling the dynamics of the COVID-19 pandemic process},
journal = {Journal of Open Source Software}
}
Tags
COVID-19 logistic function machine learning neural networks optimization regression epidemiology
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This work is licensed under a Creative Commons Attribution 4.0 International License.
