eprintid: 9423
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/94/23
datestamp: 2023-11-09 16:21:24
lastmod: 2023-11-09 16:21:24
status_changed: 2023-11-09 16:15:05
type: article
metadata_visibility: show
creators_name: Thiruchelvam, L.
creators_name: Asirvadam, V.S.
creators_name: Dass, S.C.
creators_name: Daud, H.
creators_name: Gill, B.S.
title: Dengue incidence prediction using model variables with registered case feedback
ispublished: pub
keywords: Computer vision; Forecasting; Identification (control systems); Mean square error; Religious buildings; Robotics; Surface reconstruction, Dengue; Ensemble modeling; Lag-time; Linear least squares; Single models, Least squares approximations
note: cited By 0; Conference of 9th International Conference on Robotic, Vision, Signal Processing and Power Applications, RoViSP 2016 ; Conference Date: 2 February 2016 Through 3 February 2016; Conference Code:184869
abstract: This study discussed building of localized dengue incidence prediction models for districts of Selangor. System identification with Linear Least Square estimation method is used to build a number of model orders with varied lag-time and the most accurate model is selected for each district. Model accuracy is measured using Mean Square Error (MSE) value, with smaller MSE value, represents better accuracy. The flow of study is started with identification of significant weather variables. It was found that all three weather variables namely mean temperature, relative humidity and rainfall are significant predictors. Further inclusion of dengue incidences feedback data into the model was found to enhance the model accuracy. Model accuracy is further tested by comparing between single and ensemble model of few districts. Ensemble model is built using dengue prediction model of its district together with its neighbouring districts, and was found to be better predictor in two out three districts. Therefore, it was concluded that ensemble models predict better in some cases, and single models are better in other cases, depending on rate of human movement between neighbouring districts. © Springer Science+Business Media Singapore 2017.
date: 2017
publisher: Springer Verlag
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992708855&doi=10.1007%2f978-981-10-1721-6_18&partnerID=40&md5=958f97238055f5643a5b5df28f5e61c8
id_number: 10.1007/978-981-10-1721-6₁₈
full_text_status: none
publication: Lecture Notes in Electrical Engineering
volume: 398
pagerange: 163-172
refereed: TRUE
isbn: 9789811017193
issn: 18761100
citation:   Thiruchelvam, L. and Asirvadam, V.S. and Dass, S.C. and Daud, H. and Gill, B.S.  (2017) Dengue incidence prediction using model variables with registered case feedback.  Lecture Notes in Electrical Engineering, 398.  pp. 163-172.  ISSN 18761100