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), 2024. Published by Science Publishing Group |
Infectious Disease Modeling, Dengue Cases, Count Time Series, SARIMA, INGARCH, Forecasting
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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
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
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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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}
}
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 -
Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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