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Sahisnu, JS, Natalia, F, Ferdinand, FV, Sudirman, S and Ko, C-S (2020) Vaccine prediction system using ARIMA method. ICIC Express Letters, Part B: Applications, 11 (6). pp. 567-575. ISSN 2185-2766
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Abstract
Indonesia is a country that runs health programs in the form of primary compulsory immunizations for children aged 0-11 months. According to the Health Law, Number 36 of 2009 states that every child has the right to receive primary immunization by the provisions to prevent the occurrence of diseases that can be avoided through immunization. The Indonesian government are also obliged to provide complete immunization to every baby and child by the implementation of immunization contained in the Minister of Health Regulation Number 42 of 2013. The purpose of this study is to predict vaccine stock for immunization needs, and the government can use the application to determine vaccine stock requirements for each clinic so that there is no shortage or excess stock. This prediction can ensure that immunization coverage is well distributed. We can help parties who organize primary immunization activities by making predictions and forecasting results based on the R application. In addition, the application can provide information in the form of predictive analysis. The method used in measuring predictions is ARIMA (Auto-Regressive Integrated Moving Average) to calculate the prediction of immunization.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Inventory forecasting; ARIMA; Immunization; Time series; Data visualization; Vaccine-preventable disease |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine |
| Divisions: | Computer Science and Mathematics |
| Publisher: | ICIC International |
| Date of acceptance: | 31 March 2020 |
| Date of first compliant Open Access: | 9 April 2021 |
| Date Deposited: | 21 May 2020 09:07 |
| Last Modified: | 04 Sep 2021 07:17 |
| DOI or ID number: | 10.24507/icicelb.11.06.567 |
| URI: | https://ljmu-9.eprints-hosting.org/id/eprint/12975 |
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