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Modeling Dengue Fever Cases by Using GSTAR(1;1) Model with Outlier Factor
Mukhaiyar U.a, Huda N.M.a, Novita Sari R.R.K.a, Pasaribu U.S.a
a Statistics Research Division, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Bandung, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.Dengue fever is an endemic disease transmitted through the Aedes Aegypti mosquitos. Dengue virus can be transmitted from human hosts who have been infected by the virus to the mosquitoes to be transmitted back to other humans. So that, it is possible for the virus to be transmitted to several surrounding locations. Aedes Aegypti is one of the dengue mosquitoes that likes a warm climate and not too wet or dry. In addition, many un-expected factors can cause a significant increase in the number of dengue fever cases. So that the number of dengue fever cases can increase significantly far different from other data. An observation data that has different characteristics from others is called outlier. The existence of outliers can indicate individuals or groups that have very different behavior from the most of the individuals of the dataset. Outlier data in a data set are often encountered in various kinds of data analysis. Frequently, outliers are removed to improve accuracy of the estimators. But sometimes the presence of an outlier has a certain meaning, which explanation can be lost if the outlier is removed. In this paper, modeling dengue fever cases using GSTAR(1;1) with outlier factors was firstly proposed.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Aedes aegypti,Data set,Dengue fevers,Dengue virus,Observation data,Warm climates[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]The authors thank to Hibah RISTEK DIKTI 2018 2019 for supporting funds. We also thank to the Health Oce of West Kalimantan Province for the data.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1742-6596/1366/1/012122[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]