Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3496
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dc.contributor.authorArachchi, K. A. N. L. K.-
dc.contributor.authorPeiris, T. S. G-
dc.date.accessioned2023-07-30T05:38:15Z-
dc.date.available2023-07-30T05:38:15Z-
dc.date.issued2022-09-15-
dc.identifier.citationArachchi K. A. N. L. K. and Peiris T. S. G. (2022). Modeling and Forecasting of the Weekly Incidence of Dengue in Colombo District of Sri Lanka. Proceedings of SLIIT International Conference on Advancements in Sciences and Humanities, (11) October, Colombo, 220 - 225.en_US
dc.identifier.issn2783-8862-
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/3496-
dc.description.abstractThis study was designed to develop a time series model for the weekly incidence of dengue in the Colombo district of Sri Lanka. Weekly occurrence of dengue fever counts from January 2015 to August 2020 in the Epidemiological Report by the Ministry of Health was used for the study . ARIMA (2,1,0) with the addition of AR (16) was identified as the most effective model. The model was trained using data from January 2015 to December 2019. The balance data was used to validate the model. The residuals of the model satisfied the randomness and constant variance, but the residuals significantly deviated from the normality. The results showed that the forecasted figures were consistent with the observed series. However, a noticeable percentage error was observed sequentially in the late 2020s. Those errors could be attributable to the fact that there was an underreporting of dengue fever cases due to social and operational shocks of the Covid-19 Pandemic.en_US
dc.language.isoenen_US
dc.publisherFaculty of Humanities and Sciences, SLIITen_US
dc.relation.ispartofseriesPROCEEDINGS OF THE SLIIT INTERNATIONAL CONFERENCE ON ADVANCEMENTS IN SCIENCES AND HUMANITIES [SICASH];-
dc.subjectARIMAen_US
dc.subjectDengueen_US
dc.subjectTime series analysisen_US
dc.titleModeling and Forecasting of the Weekly Incidence of Dengue in Colombo District of Sri Lankaen_US
dc.typeArticleen_US
Appears in Collections:Proceedings of the SLIIT International Conference on Advancements in Sciences and Humanities2022 [SICASH]

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