Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases

Frasiah Wambui Kariuki; Anthony Kibira Wanjoya; Bonface Miya Malenje

doi:doi:10.11648/j.ajtas.20231206.11

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Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases

Frasiah Wambui Kariuki^*, Anthony Kibira Wanjoya, Bonface Miya Malenje

Published in American Journal of Theoretical and Applied Statistics (Volume 12, Issue 6)

Received: 3 October 2023 Accepted: 26 October 2023 Published: 7 November 2023

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Abstract

A crucial focus of public health surveillance systems is to provide reliable forecasts of epidemiological time series. This work utilized data collected through a national public health surveillance system in Thailand to evaluate and compare the performance of a seasonal autoregressive integrated moving average and an Integer generalized autoregressive conditionally heteroscedastic model for modeling and forecasting case occurrence of dengue. The comparison uses weekly reported cases of dengue hemorrhagic fever in Amnat Charoen province Thailand, from January 1st, 2006, to October 7th, 2017 (612 weeks). The results from the in-sample evaluation using the root mean square error and mean absolute error as well as a visual inspection of predicted values show that the two approaches are adequate tools for use in epidemiological surveillance as there is no significant difference in their forecast accuracy for in-sample performance. The incorporation of the weather variables improves the predictive performance of the models and from the model coefficients the study findings reveal that there is a positive relationship between temperature and rainfall and the occurrence of dengue. Overall, the findings in this study support the usefulness of the two approaches as effective tools practitioners can utilize for monitoring and for providing early warning signals of potential outbreaks of epidemics.

Published in	American Journal of Theoretical and Applied Statistics (Volume 12, Issue 6)
DOI	10.11648/j.ajtas.20231206.11
Page(s)	150-160
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), 2023. Published by Science Publishing Group

Keywords

Infectious Disease Modeling, Dengue Cases, Count Time Series, SARIMA, INGARCH, Forecasting

References

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APA Style

Kariuki, F. W., Wanjoya, A. K., Malenje, B. M. (2023). Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases. American Journal of Theoretical and Applied Statistics, 12(6), 150-160. https://doi.org/10.11648/j.ajtas.20231206.11

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ACS Style

Kariuki, F. W.; Wanjoya, A. K.; Malenje, B. M. Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases. Am. J. Theor. Appl. Stat. 2023, 12(6), 150-160. doi: 10.11648/j.ajtas.20231206.11

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AMA Style

Kariuki FW, Wanjoya AK, Malenje BM. Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases. Am J Theor Appl Stat. 2023;12(6):150-160. doi: 10.11648/j.ajtas.20231206.11

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@article{10.11648/j.ajtas.20231206.11,
  author = {Frasiah Wambui Kariuki and Anthony Kibira Wanjoya and Bonface Miya Malenje},
  title = {Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {12},
  number = {6},
  pages = {150-160},
  doi = {10.11648/j.ajtas.20231206.11},
  url = {https://doi.org/10.11648/j.ajtas.20231206.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20231206.11},
  abstract = {A crucial focus of public health surveillance systems is to provide reliable forecasts of epidemiological time series. This work utilized data collected through a national public health surveillance system in Thailand to evaluate and compare the performance of a seasonal autoregressive integrated moving average and an Integer generalized autoregressive conditionally heteroscedastic model for modeling and forecasting case occurrence of dengue. The comparison uses weekly reported cases of dengue hemorrhagic fever in Amnat Charoen province Thailand, from January 1st, 2006, to October 7th, 2017 (612 weeks). The results from the in-sample evaluation using the root mean square error and mean absolute error as well as a visual inspection of predicted values show that the two approaches are adequate tools for use in epidemiological surveillance as there is no significant difference in their forecast accuracy for in-sample performance. The incorporation of the weather variables improves the predictive performance of the models and from the model coefficients the study findings reveal that there is a positive relationship between temperature and rainfall and the occurrence of dengue. Overall, the findings in this study support the usefulness of the two approaches as effective tools practitioners can utilize for monitoring and for providing early warning signals of potential outbreaks of epidemics.
},
 year = {2023}
}

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TY  - JOUR
T1  - Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases
AU  - Frasiah Wambui Kariuki
AU  - Anthony Kibira Wanjoya
AU  - Bonface Miya Malenje
Y1  - 2023/11/07
PY  - 2023
N1  - https://doi.org/10.11648/j.ajtas.20231206.11
DO  - 10.11648/j.ajtas.20231206.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 150
EP  - 160
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20231206.11
AB  - A crucial focus of public health surveillance systems is to provide reliable forecasts of epidemiological time series. This work utilized data collected through a national public health surveillance system in Thailand to evaluate and compare the performance of a seasonal autoregressive integrated moving average and an Integer generalized autoregressive conditionally heteroscedastic model for modeling and forecasting case occurrence of dengue. The comparison uses weekly reported cases of dengue hemorrhagic fever in Amnat Charoen province Thailand, from January 1st, 2006, to October 7th, 2017 (612 weeks). The results from the in-sample evaluation using the root mean square error and mean absolute error as well as a visual inspection of predicted values show that the two approaches are adequate tools for use in epidemiological surveillance as there is no significant difference in their forecast accuracy for in-sample performance. The incorporation of the weather variables improves the predictive performance of the models and from the model coefficients the study findings reveal that there is a positive relationship between temperature and rainfall and the occurrence of dengue. Overall, the findings in this study support the usefulness of the two approaches as effective tools practitioners can utilize for monitoring and for providing early warning signals of potential outbreaks of epidemics.

VL  - 12
IS  - 6
ER  -

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Author Information

Frasiah Wambui Kariuki

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

Contact Email
Anthony Kibira Wanjoya

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Bonface Miya Malenje

Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

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APA Style

Kariuki, F. W., Wanjoya, A. K., Malenje, B. M. (2023). Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases. American Journal of Theoretical and Applied Statistics, 12(6), 150-160. https://doi.org/10.11648/j.ajtas.20231206.11

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ACS Style

Kariuki, F. W.; Wanjoya, A. K.; Malenje, B. M. Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases. Am. J. Theor. Appl. Stat. 2023, 12(6), 150-160. doi: 10.11648/j.ajtas.20231206.11

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AMA Style

Kariuki FW, Wanjoya AK, Malenje BM. Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases. Am J Theor Appl Stat. 2023;12(6):150-160. doi: 10.11648/j.ajtas.20231206.11

Copy | Download

@article{10.11648/j.ajtas.20231206.11,
  author = {Frasiah Wambui Kariuki and Anthony Kibira Wanjoya and Bonface Miya Malenje},
  title = {Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {12},
  number = {6},
  pages = {150-160},
  doi = {10.11648/j.ajtas.20231206.11},
  url = {https://doi.org/10.11648/j.ajtas.20231206.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20231206.11},
  abstract = {A crucial focus of public health surveillance systems is to provide reliable forecasts of epidemiological time series. This work utilized data collected through a national public health surveillance system in Thailand to evaluate and compare the performance of a seasonal autoregressive integrated moving average and an Integer generalized autoregressive conditionally heteroscedastic model for modeling and forecasting case occurrence of dengue. The comparison uses weekly reported cases of dengue hemorrhagic fever in Amnat Charoen province Thailand, from January 1st, 2006, to October 7th, 2017 (612 weeks). The results from the in-sample evaluation using the root mean square error and mean absolute error as well as a visual inspection of predicted values show that the two approaches are adequate tools for use in epidemiological surveillance as there is no significant difference in their forecast accuracy for in-sample performance. The incorporation of the weather variables improves the predictive performance of the models and from the model coefficients the study findings reveal that there is a positive relationship between temperature and rainfall and the occurrence of dengue. Overall, the findings in this study support the usefulness of the two approaches as effective tools practitioners can utilize for monitoring and for providing early warning signals of potential outbreaks of epidemics.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Comparative Study of INGARCH and SARIMA in Modeling and Forecasting Dengue Cases
AU  - Frasiah Wambui Kariuki
AU  - Anthony Kibira Wanjoya
AU  - Bonface Miya Malenje
Y1  - 2023/11/07
PY  - 2023
N1  - https://doi.org/10.11648/j.ajtas.20231206.11
DO  - 10.11648/j.ajtas.20231206.11
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 150
EP  - 160
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20231206.11
AB  - A crucial focus of public health surveillance systems is to provide reliable forecasts of epidemiological time series. This work utilized data collected through a national public health surveillance system in Thailand to evaluate and compare the performance of a seasonal autoregressive integrated moving average and an Integer generalized autoregressive conditionally heteroscedastic model for modeling and forecasting case occurrence of dengue. The comparison uses weekly reported cases of dengue hemorrhagic fever in Amnat Charoen province Thailand, from January 1st, 2006, to October 7th, 2017 (612 weeks). The results from the in-sample evaluation using the root mean square error and mean absolute error as well as a visual inspection of predicted values show that the two approaches are adequate tools for use in epidemiological surveillance as there is no significant difference in their forecast accuracy for in-sample performance. The incorporation of the weather variables improves the predictive performance of the models and from the model coefficients the study findings reveal that there is a positive relationship between temperature and rainfall and the occurrence of dengue. Overall, the findings in this study support the usefulness of the two approaches as effective tools practitioners can utilize for monitoring and for providing early warning signals of potential outbreaks of epidemics.

VL  - 12
IS  - 6
ER  -

Copy | Download