Prediction of zoonosis incidence in human using seasonal auto regressive integrated moving average (SARIMA).
Abstract— Zoonosis refers to the transmission of infectious diseases from animal to human. The increasing number of zoonosis incidence makes the great losses to lives, including humans and animals, and also the impact in social economic. It motivates development of a system that can predict the fu...
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| Format: | Article |
| Language: | English |
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2009
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| Online Access: | http://scholars.utp.edu.my/id/eprint/3228/ http://scholars.utp.edu.my/id/eprint/3228/1/Erna_IJCSIS2009.pdf |
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| author | Adhistya, Erna Dayang R.A. Rambli, Rohaya Dominic P, Dhanapal Durai |
| author_facet | Adhistya, Erna Dayang R.A. Rambli, Rohaya Dominic P, Dhanapal Durai |
| author_sort | Adhistya, Erna |
| building | UTP Institutional Repository |
| collection | Online Access |
| description | Abstract— Zoonosis refers to the transmission of infectious
diseases from animal to human. The increasing number of
zoonosis incidence makes the great losses to lives, including humans and animals, and also the impact in social economic. It motivates development of a system that can predict the future number of zoonosis occurrences in human. This paper analyses and presents the use of Seasonal Autoregressive Integrated Moving Average (SARIMA) method for developing a forecasting model that able to support and provide prediction number of zoonosis human incidence. The dataset for model development was collected on a time series data of human Salmonellosis occurrences in United States which comprises of fourteen years of monthly data obtained from a study published by Centers for Disease Control and Prevention (CDC). Several trial models of
SARIMA were compared to obtain the most appropriate model.
Then, diagnostic tests were used to determine model validity. The result showed that the SARIMA(9,0,14)(12,1,24)12 is the fittest model. While in the measure of accuracy, the selected model achieved 0.062 of Theil’s U value. It implied that the model was highly accurate and a close fit. It was also indicated the capability of final model to closely represent and made prediction based on the tuberculosis historical dataset. |
| first_indexed | 2025-11-13T07:29:20Z |
| format | Article |
| id | oai:scholars.utp.edu.my:3228 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-13T07:29:20Z |
| publishDate | 2009 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:32282017-01-19T08:25:14Z http://scholars.utp.edu.my/id/eprint/3228/ Prediction of zoonosis incidence in human using seasonal auto regressive integrated moving average (SARIMA). Adhistya, Erna Dayang R.A. Rambli, Rohaya Dominic P, Dhanapal Durai QA75 Electronic computers. Computer science Abstract— Zoonosis refers to the transmission of infectious diseases from animal to human. The increasing number of zoonosis incidence makes the great losses to lives, including humans and animals, and also the impact in social economic. It motivates development of a system that can predict the future number of zoonosis occurrences in human. This paper analyses and presents the use of Seasonal Autoregressive Integrated Moving Average (SARIMA) method for developing a forecasting model that able to support and provide prediction number of zoonosis human incidence. The dataset for model development was collected on a time series data of human Salmonellosis occurrences in United States which comprises of fourteen years of monthly data obtained from a study published by Centers for Disease Control and Prevention (CDC). Several trial models of SARIMA were compared to obtain the most appropriate model. Then, diagnostic tests were used to determine model validity. The result showed that the SARIMA(9,0,14)(12,1,24)12 is the fittest model. While in the measure of accuracy, the selected model achieved 0.062 of Theil’s U value. It implied that the model was highly accurate and a close fit. It was also indicated the capability of final model to closely represent and made prediction based on the tuberculosis historical dataset. 2009-09 Article PeerReviewed application/pdf en http://scholars.utp.edu.my/id/eprint/3228/1/Erna_IJCSIS2009.pdf Adhistya, Erna and Dayang R.A. Rambli, Rohaya and Dominic P, Dhanapal Durai (2009) Prediction of zoonosis incidence in human using seasonal auto regressive integrated moving average (SARIMA). IJCSIS, 5 (1). pp. 103-110. |
| spellingShingle | QA75 Electronic computers. Computer science Adhistya, Erna Dayang R.A. Rambli, Rohaya Dominic P, Dhanapal Durai Prediction of zoonosis incidence in human using seasonal auto regressive integrated moving average (SARIMA). |
| title | Prediction of zoonosis incidence in human using seasonal auto regressive integrated moving average (SARIMA). |
| title_full | Prediction of zoonosis incidence in human using seasonal auto regressive integrated moving average (SARIMA). |
| title_fullStr | Prediction of zoonosis incidence in human using seasonal auto regressive integrated moving average (SARIMA). |
| title_full_unstemmed | Prediction of zoonosis incidence in human using seasonal auto regressive integrated moving average (SARIMA). |
| title_short | Prediction of zoonosis incidence in human using seasonal auto regressive integrated moving average (SARIMA). |
| title_sort | prediction of zoonosis incidence in human using seasonal auto regressive integrated moving average (sarima). |
| topic | QA75 Electronic computers. Computer science |
| url | http://scholars.utp.edu.my/id/eprint/3228/ http://scholars.utp.edu.my/id/eprint/3228/1/Erna_IJCSIS2009.pdf |