{"_buckets": {"deposit": "5736207e-2501-4473-9b94-3699f4ae4c89"}, "_deposit": {"id": "1691", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "1691"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/1691", "sets": ["user-yueco"]}, "author_link": [], "communities": ["yueco"], "control_number": "1691", "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "ARIMA MODELLING FOR MALARIA INFECTION IN KACHIN STATE (Theingi Aye, 2019)", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "This study attempts to model and to forecast malaria infection of Kachin State which had been affected malaria highest risk areas at 2004 in Myanmar. This study utilized monthly time series data from January, 2011 to December, 2016 and employed the well-known Box-Jenkins Seasonal ARIMA Modeling procedures. The objectives of this study are to study Malaria incidence in Kachin State, to examine the best fitted ARIMA model and to forecast the incidence of Malaria infection in Kachin State based on the best fitted model. Following the Box and Jenkins methodology, the time series modeling involves transformation of the data to achieve stationary followed by identification of appropriate models, estimation of model parameters, diagnostic checking of the assumption model and finally forecasting of future data values. SARIMA (1,0,0) x (1,1, 0)12 was defined the best model to predict the future Malaria infection in Kachin state and forecasted the future values using the fitted model. The results of this paper indicate that over 50% of malaria incidence in Kachin State is decreased in 2017. That is why malaria incidence in Kachin State may be eliminated in 2020 although the Kachin State is not included in 2020 targeted areas for malaria elimination in Myanmar. There is also observed that the SARIMA model is capable of representing with relative precision the number of malaria infection in the next year."}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-05-05"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Theingi Aye (MAS - 25).pdf", "filesize": [{"value": "1324 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 1324000.0, "url": {"url": "https://meral.edu.mm/record/1691/files/Theingi Aye (MAS - 25).pdf"}, "version_id": "e69965e2-c4a0-441a-9669-10a9bd4516e7"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_journal_title": "Yangon University of Economics"}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "Yangon University of Economics", "subitem_supervisor(s)": [{"subitem_supervisor": "Prof.Dr. Maw Maw Khin"}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Theingi Aye"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Other"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Thesis"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2019-12-01"}, "item_title": "ARIMA MODELLING FOR MALARIA INFECTION IN KACHIN STATE (Theingi Aye, 2019)", "item_type_id": "21", "owner": "1", "path": ["1582965763232"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000001691", "pubdate": {"attribute_name": "Deposit date", "attribute_value": "2020-03-05"}, "publish_date": "2020-03-05", "publish_status": "0", "recid": "1691", "relation": {}, "relation_version_is_last": true, "title": ["ARIMA MODELLING FOR MALARIA INFECTION IN KACHIN STATE (Theingi Aye, 2019)"], "weko_shared_id": -1}