Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble

Otoo Joseph; Bosson-Amedenu Senyefia; Nyarko Christiana Cynthia; Osei-Asibey Eunice; Boateng Ernest Yeboah

doi:doi:10.11648/j.ijssam.20200502.12

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Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble

Otoo Joseph, Bosson-Amedenu Senyefia, Nyarko Christiana Cynthia, Osei-Asibey Eunice, Boateng Ernest Yeboah

Published in International Journal of Systems Science and Applied Mathematics (Volume 5, Issue 2)

Received: 4 July 2020 Accepted: 20 July 2020 Published: 28 July 2020

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Abstract

The novel coronavirus has unsettled many nations and has created severe uncertainty in its spread. In this paper, we present the performance of ensemble models and single forecast models in the projection of COVID-19 confirmed cases in nine countries. Data consisting of two (2) health indicators (new COVID-19 and cumulative COVID-19 confirmed cases) were collated on May 10, 2020 from the Humanitarian Data Exchange (HDX). Forecasting models with the minimum Mean Square Error (MSE) and Root Mean Square Error (RMSE) were selected. Our findings showed that ETS (A, N, N) was the best model fit for China, Spain, South Korea and Ghana in terms of single COVID-19 confirmed cases. On the other hand, INGARCH (1, 1) was the best fit model for the remaining countries. Regarding cumulative COVID-19 confirmed cases, INGARCH (1, 1) was fit for each of the nine countries. Again, we found that single forecasting models outperform hybrid models when the number of data points does not meet a certain threshold, and when the data has no seasonality; suggesting further that hybrid forecast models perform efficiently in complex time series dataset. Results from the 10 days forecast indicate that for most countries, with the exception of Ghana and India, new covid-19 confirmed cases will drop. The study suggest for future works to expand the training dataset by augmenting additional data onto the available data and then apply hybrid forecasting models to the dataset.

Published in	International Journal of Systems Science and Applied Mathematics (Volume 5, Issue 2)
DOI	10.11648/j.ijssam.20200502.12
Page(s)	20-26
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

COVID-19, Coronavirus, Ensemble, Forecasting, Multi-Model, Time Series

References

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[4]	Dehesh T. Mardani-Fard H. A, Dehesh P. Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models. medRxiv preprint; 2020.
[5]	Zhang, G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175; 2020.
[6]	Wang YH. Nonlinear neural network forecasting model for stock index option price: hybrid GJR-GARCH approach. Expert Systems with Applications 2009; 36: 564–70.
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[8]	Ferland, R., Latour, A., & Oraichi, D. Integer-valued GARCH process. Journal of Time Series Analysis, 27, 923–942; (2006).
[9]	Fokianos, K., Rahbek, A., & Tjøstheim, D. Poisson Autoregression. Journal of the American Statistical Association, 104, 1430–1439; (2009).
[10]	Cui, Y., Li, Q., & Zhu, F. Flexible bivariate Poisson integer-valued GARCH model. Annals of the Institute of Statistical Mathematics. doi: 10.1007/s10463-019-00732-4; (2019).
[11]	Fong, S. J., Li, G., Dey, N., & Crespo, R. G. (2020). Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-nCoV Novel Coronavirus Outbreak. International Journal of Interactive Multimedia and Artificial Intelligence, 6 (1), 132-140. doi: 10.9781/ijimai.2020.02.002.
[12]	Araujo, M. B., & New, M. (2006). Ensemble forecasting of species distributions. TRENDS in Ecology and Evolution, 22 (1), 42-47. doi: 10.1016/j.tree.2006.09.010.
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Otoo Joseph, Bosson-Amedenu Senyefia, Nyarko Christiana Cynthia, Osei-Asibey Eunice, Boateng Ernest Yeboah. (2020). Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble. International Journal of Systems Science and Applied Mathematics, 5(2), 20-26. https://doi.org/10.11648/j.ijssam.20200502.12

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ACS Style

Otoo Joseph; Bosson-Amedenu Senyefia; Nyarko Christiana Cynthia; Osei-Asibey Eunice; Boateng Ernest Yeboah. Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble. Int. J. Syst. Sci. Appl. Math. 2020, 5(2), 20-26. doi: 10.11648/j.ijssam.20200502.12

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AMA Style

Otoo Joseph, Bosson-Amedenu Senyefia, Nyarko Christiana Cynthia, Osei-Asibey Eunice, Boateng Ernest Yeboah. Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble. Int J Syst Sci Appl Math. 2020;5(2):20-26. doi: 10.11648/j.ijssam.20200502.12

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@article{10.11648/j.ijssam.20200502.12,
  author = {Otoo Joseph and Bosson-Amedenu Senyefia and Nyarko Christiana Cynthia and Osei-Asibey Eunice and Boateng Ernest Yeboah},
  title = {Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble},
  journal = {International Journal of Systems Science and Applied Mathematics},
  volume = {5},
  number = {2},
  pages = {20-26},
  doi = {10.11648/j.ijssam.20200502.12},
  url = {https://doi.org/10.11648/j.ijssam.20200502.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20200502.12},
  abstract = {The novel coronavirus has unsettled many nations and has created severe uncertainty in its spread. In this paper, we present the performance of ensemble models and single forecast models in the projection of COVID-19 confirmed cases in nine countries. Data consisting of two (2) health indicators (new COVID-19 and cumulative COVID-19 confirmed cases) were collated on May 10, 2020 from the Humanitarian Data Exchange (HDX). Forecasting models with the minimum Mean Square Error (MSE) and Root Mean Square Error (RMSE) were selected. Our findings showed that ETS (A, N, N) was the best model fit for China, Spain, South Korea and Ghana in terms of single COVID-19 confirmed cases. On the other hand, INGARCH (1, 1) was the best fit model for the remaining countries. Regarding cumulative COVID-19 confirmed cases, INGARCH (1, 1) was fit for each of the nine countries. Again, we found that single forecasting models outperform hybrid models when the number of data points does not meet a certain threshold, and when the data has no seasonality; suggesting further that hybrid forecast models perform efficiently in complex time series dataset. Results from the 10 days forecast indicate that for most countries, with the exception of Ghana and India, new covid-19 confirmed cases will drop. The study suggest for future works to expand the training dataset by augmenting additional data onto the available data and then apply hybrid forecasting models to the dataset.},
 year = {2020}
}

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TY  - JOUR
T1  - Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble
AU  - Otoo Joseph
AU  - Bosson-Amedenu Senyefia
AU  - Nyarko Christiana Cynthia
AU  - Osei-Asibey Eunice
AU  - Boateng Ernest Yeboah
Y1  - 2020/07/28
PY  - 2020
N1  - https://doi.org/10.11648/j.ijssam.20200502.12
DO  - 10.11648/j.ijssam.20200502.12
T2  - International Journal of Systems Science and Applied Mathematics
JF  - International Journal of Systems Science and Applied Mathematics
JO  - International Journal of Systems Science and Applied Mathematics
SP  - 20
EP  - 26
PB  - Science Publishing Group
SN  - 2575-5803
UR  - https://doi.org/10.11648/j.ijssam.20200502.12
AB  - The novel coronavirus has unsettled many nations and has created severe uncertainty in its spread. In this paper, we present the performance of ensemble models and single forecast models in the projection of COVID-19 confirmed cases in nine countries. Data consisting of two (2) health indicators (new COVID-19 and cumulative COVID-19 confirmed cases) were collated on May 10, 2020 from the Humanitarian Data Exchange (HDX). Forecasting models with the minimum Mean Square Error (MSE) and Root Mean Square Error (RMSE) were selected. Our findings showed that ETS (A, N, N) was the best model fit for China, Spain, South Korea and Ghana in terms of single COVID-19 confirmed cases. On the other hand, INGARCH (1, 1) was the best fit model for the remaining countries. Regarding cumulative COVID-19 confirmed cases, INGARCH (1, 1) was fit for each of the nine countries. Again, we found that single forecasting models outperform hybrid models when the number of data points does not meet a certain threshold, and when the data has no seasonality; suggesting further that hybrid forecast models perform efficiently in complex time series dataset. Results from the 10 days forecast indicate that for most countries, with the exception of Ghana and India, new covid-19 confirmed cases will drop. The study suggest for future works to expand the training dataset by augmenting additional data onto the available data and then apply hybrid forecasting models to the dataset.
VL  - 5
IS  - 2
ER  -

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Author Information

Otoo Joseph

Department of Statistics and Actuarial Science, University of Ghana, Legon, Accra, Ghana
Bosson-Amedenu Senyefia

Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
Nyarko Christiana Cynthia

Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
Osei-Asibey Eunice

Department of Mathematical Sciences, University of Mines and Technology, Tarkwa, Ghana
Boateng Ernest Yeboah

Department of Basic Sciences, School of Basic and Biomedical Sciences, University of Health and Allied Sciences, Ho, Ghana

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APA Style

Otoo Joseph, Bosson-Amedenu Senyefia, Nyarko Christiana Cynthia, Osei-Asibey Eunice, Boateng Ernest Yeboah. (2020). Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble. International Journal of Systems Science and Applied Mathematics, 5(2), 20-26. https://doi.org/10.11648/j.ijssam.20200502.12

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ACS Style

Otoo Joseph; Bosson-Amedenu Senyefia; Nyarko Christiana Cynthia; Osei-Asibey Eunice; Boateng Ernest Yeboah. Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble. Int. J. Syst. Sci. Appl. Math. 2020, 5(2), 20-26. doi: 10.11648/j.ijssam.20200502.12

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AMA Style

Otoo Joseph, Bosson-Amedenu Senyefia, Nyarko Christiana Cynthia, Osei-Asibey Eunice, Boateng Ernest Yeboah. Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble. Int J Syst Sci Appl Math. 2020;5(2):20-26. doi: 10.11648/j.ijssam.20200502.12

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@article{10.11648/j.ijssam.20200502.12,
  author = {Otoo Joseph and Bosson-Amedenu Senyefia and Nyarko Christiana Cynthia and Osei-Asibey Eunice and Boateng Ernest Yeboah},
  title = {Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble},
  journal = {International Journal of Systems Science and Applied Mathematics},
  volume = {5},
  number = {2},
  pages = {20-26},
  doi = {10.11648/j.ijssam.20200502.12},
  url = {https://doi.org/10.11648/j.ijssam.20200502.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20200502.12},
  abstract = {The novel coronavirus has unsettled many nations and has created severe uncertainty in its spread. In this paper, we present the performance of ensemble models and single forecast models in the projection of COVID-19 confirmed cases in nine countries. Data consisting of two (2) health indicators (new COVID-19 and cumulative COVID-19 confirmed cases) were collated on May 10, 2020 from the Humanitarian Data Exchange (HDX). Forecasting models with the minimum Mean Square Error (MSE) and Root Mean Square Error (RMSE) were selected. Our findings showed that ETS (A, N, N) was the best model fit for China, Spain, South Korea and Ghana in terms of single COVID-19 confirmed cases. On the other hand, INGARCH (1, 1) was the best fit model for the remaining countries. Regarding cumulative COVID-19 confirmed cases, INGARCH (1, 1) was fit for each of the nine countries. Again, we found that single forecasting models outperform hybrid models when the number of data points does not meet a certain threshold, and when the data has no seasonality; suggesting further that hybrid forecast models perform efficiently in complex time series dataset. Results from the 10 days forecast indicate that for most countries, with the exception of Ghana and India, new covid-19 confirmed cases will drop. The study suggest for future works to expand the training dataset by augmenting additional data onto the available data and then apply hybrid forecasting models to the dataset.},
 year = {2020}
}

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TY  - JOUR
T1  - Covid-19 Projections: Single Forecast Model Against Multi-Model Ensemble
AU  - Otoo Joseph
AU  - Bosson-Amedenu Senyefia
AU  - Nyarko Christiana Cynthia
AU  - Osei-Asibey Eunice
AU  - Boateng Ernest Yeboah
Y1  - 2020/07/28
PY  - 2020
N1  - https://doi.org/10.11648/j.ijssam.20200502.12
DO  - 10.11648/j.ijssam.20200502.12
T2  - International Journal of Systems Science and Applied Mathematics
JF  - International Journal of Systems Science and Applied Mathematics
JO  - International Journal of Systems Science and Applied Mathematics
SP  - 20
EP  - 26
PB  - Science Publishing Group
SN  - 2575-5803
UR  - https://doi.org/10.11648/j.ijssam.20200502.12
AB  - The novel coronavirus has unsettled many nations and has created severe uncertainty in its spread. In this paper, we present the performance of ensemble models and single forecast models in the projection of COVID-19 confirmed cases in nine countries. Data consisting of two (2) health indicators (new COVID-19 and cumulative COVID-19 confirmed cases) were collated on May 10, 2020 from the Humanitarian Data Exchange (HDX). Forecasting models with the minimum Mean Square Error (MSE) and Root Mean Square Error (RMSE) were selected. Our findings showed that ETS (A, N, N) was the best model fit for China, Spain, South Korea and Ghana in terms of single COVID-19 confirmed cases. On the other hand, INGARCH (1, 1) was the best fit model for the remaining countries. Regarding cumulative COVID-19 confirmed cases, INGARCH (1, 1) was fit for each of the nine countries. Again, we found that single forecasting models outperform hybrid models when the number of data points does not meet a certain threshold, and when the data has no seasonality; suggesting further that hybrid forecast models perform efficiently in complex time series dataset. Results from the 10 days forecast indicate that for most countries, with the exception of Ghana and India, new covid-19 confirmed cases will drop. The study suggest for future works to expand the training dataset by augmenting additional data onto the available data and then apply hybrid forecasting models to the dataset.
VL  - 5
IS  - 2
ER  -

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