Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data
IntroductionRabies, a deadly zoonotic viral disease, accounts for over 50,000 fatalities globally each year. This disease predominantly plagues developing nations, with Thailand being no exception. In the current global landscape, concerted efforts are being mobilized to curb human mortalities attri...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Veterinary Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fvets.2023.1294049/full |
| _version_ | 1797448651463720960 |
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| author | Veerasak Punyapornwithaya Veerasak Punyapornwithaya Veerasak Punyapornwithaya Weerapong Thanapongtharm Chalita Jainonthee Chalita Jainonthee Pornpiroon Chinsorn Onpawee Sagarasaeranee Roderick Salvador Orapun Arjkumpa |
| author_facet | Veerasak Punyapornwithaya Veerasak Punyapornwithaya Veerasak Punyapornwithaya Weerapong Thanapongtharm Chalita Jainonthee Chalita Jainonthee Pornpiroon Chinsorn Onpawee Sagarasaeranee Roderick Salvador Orapun Arjkumpa |
| author_sort | Veerasak Punyapornwithaya |
| collection | DOAJ |
| description | IntroductionRabies, a deadly zoonotic viral disease, accounts for over 50,000 fatalities globally each year. This disease predominantly plagues developing nations, with Thailand being no exception. In the current global landscape, concerted efforts are being mobilized to curb human mortalities attributed to animal-transmitted rabies. For strategic allocation and optimization of resources, sophisticated and accurate forecasting of rabies incidents is imperative. This research aims to determine temporal patterns, and seasonal fluctuations, and project the incidence of canine rabies throughout Thailand, using various time series techniques.MethodsMonthly total laboratory-confirmed rabies cases data from January 2013 to December 2022 (full dataset) were split into the training dataset (January 2013 to December 2021) and the test dataset (January to December 2022). Time series models including Seasonal Autoregressive Integrated Moving Average (SARIMA), Neural Network Autoregression (NNAR), Error Trend Seasonality (ETS), the Trigonometric Exponential Smoothing State-Space Model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and Seasonal and Trend Decomposition using Loess (STL) were used to analyze the training dataset and the full dataset. The forecast values obtained from the time series models applied to the training dataset were compared with the actual values from the test dataset to determine their predictive performance. Furthermore, the forecast projections from January 2023 to December 2025 were generated from models applied to the full dataset.ResultsThe findings revealed a total of 4,678 confirmed canine rabies cases during the study duration, with apparent seasonality in the data. Among the models tested with the test dataset, TBATS exhibited superior predictive accuracy, closely trailed by the SARIMA model. Based on the full dataset, TBATS projections suggest an annual average of approximately 285 canine rabies cases for the years 2023 to 2025, translating to a monthly average of 23 cases (range: 18–30). In contrast, SARIMA projections averaged 277 cases annually (range: 208–214).DiscussionThis research offers a new perspective on disease forecasting through advanced time series methodologies. The results should be taken into consideration when planning and conducting rabies surveillance, prevention, and control activities. |
| first_indexed | 2024-03-09T14:13:28Z |
| format | Article |
| id | doaj.art-26bcfebe832e43a7a2d27b70be06cb6f |
| institution | Directory Open Access Journal |
| issn | 2297-1769 |
| language | English |
| last_indexed | 2024-03-09T14:13:28Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Veterinary Science |
| spelling | doaj.art-26bcfebe832e43a7a2d27b70be06cb6f2023-11-29T05:09:22ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692023-11-011010.3389/fvets.2023.12940491294049Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance dataVeerasak Punyapornwithaya0Veerasak Punyapornwithaya1Veerasak Punyapornwithaya2Weerapong Thanapongtharm3Chalita Jainonthee4Chalita Jainonthee5Pornpiroon Chinsorn6Onpawee Sagarasaeranee7Roderick Salvador8Orapun Arjkumpa9Research Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, ThailandVeterinary Public Health and Food Safety Centre for Asia Pacific, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, ThailandDepartment of Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, ThailandDepartment of Livestock Development, Bangkok, ThailandResearch Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, ThailandVeterinary Public Health and Food Safety Centre for Asia Pacific, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, ThailandCompanion Disease Control Division, Bureau of Disease Control and Veterinary Services, Department of Livestock Development, Bangkok, ThailandCompanion Disease Control Division, Bureau of Disease Control and Veterinary Services, Department of Livestock Development, Bangkok, ThailandCollege of Veterinary Science and Medicine, Central Luzon State University, Science City of Muñoz, Nueva Ecija, PhilippinesThe 4th Regional Livestock Office, Department of Livestock Development, Khon Kaen, ThailandIntroductionRabies, a deadly zoonotic viral disease, accounts for over 50,000 fatalities globally each year. This disease predominantly plagues developing nations, with Thailand being no exception. In the current global landscape, concerted efforts are being mobilized to curb human mortalities attributed to animal-transmitted rabies. For strategic allocation and optimization of resources, sophisticated and accurate forecasting of rabies incidents is imperative. This research aims to determine temporal patterns, and seasonal fluctuations, and project the incidence of canine rabies throughout Thailand, using various time series techniques.MethodsMonthly total laboratory-confirmed rabies cases data from January 2013 to December 2022 (full dataset) were split into the training dataset (January 2013 to December 2021) and the test dataset (January to December 2022). Time series models including Seasonal Autoregressive Integrated Moving Average (SARIMA), Neural Network Autoregression (NNAR), Error Trend Seasonality (ETS), the Trigonometric Exponential Smoothing State-Space Model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (TBATS), and Seasonal and Trend Decomposition using Loess (STL) were used to analyze the training dataset and the full dataset. The forecast values obtained from the time series models applied to the training dataset were compared with the actual values from the test dataset to determine their predictive performance. Furthermore, the forecast projections from January 2023 to December 2025 were generated from models applied to the full dataset.ResultsThe findings revealed a total of 4,678 confirmed canine rabies cases during the study duration, with apparent seasonality in the data. Among the models tested with the test dataset, TBATS exhibited superior predictive accuracy, closely trailed by the SARIMA model. Based on the full dataset, TBATS projections suggest an annual average of approximately 285 canine rabies cases for the years 2023 to 2025, translating to a monthly average of 23 cases (range: 18–30). In contrast, SARIMA projections averaged 277 cases annually (range: 208–214).DiscussionThis research offers a new perspective on disease forecasting through advanced time series methodologies. The results should be taken into consideration when planning and conducting rabies surveillance, prevention, and control activities.https://www.frontiersin.org/articles/10.3389/fvets.2023.1294049/fullrabiesconfirmed casestime series modelforecastingThailand |
| spellingShingle | Veerasak Punyapornwithaya Veerasak Punyapornwithaya Veerasak Punyapornwithaya Weerapong Thanapongtharm Chalita Jainonthee Chalita Jainonthee Pornpiroon Chinsorn Onpawee Sagarasaeranee Roderick Salvador Orapun Arjkumpa Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data Frontiers in Veterinary Science rabies confirmed cases time series model forecasting Thailand |
| title | Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data |
| title_full | Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data |
| title_fullStr | Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data |
| title_full_unstemmed | Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data |
| title_short | Time series analysis and forecasting of the number of canine rabies confirmed cases in Thailand based on national-level surveillance data |
| title_sort | time series analysis and forecasting of the number of canine rabies confirmed cases in thailand based on national level surveillance data |
| topic | rabies confirmed cases time series model forecasting Thailand |
| url | https://www.frontiersin.org/articles/10.3389/fvets.2023.1294049/full |
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