TY  - CONF
SP  - 541
ID  - scholars9083
TI  - K-step ahead prediction models for dengue occurrences
KW  - Atmospheric humidity; Forecasting; Rain; Regression analysis
KW  -  Akaike information criterion; Cumulative rainfall; Dengue Incidences; Estimation models; Linear regression models; Mean temperature; Prediction model; Well patterns
KW  -  Image processing
N1  - cited By 1; Conference of 5th IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2017 ; Conference Date: 12 September 2017 Through 14 September 2017; Conference Code:132915
N2  - The paper proposed prediction model to study dengue occurrence in Malaysia, focusing on a region of Petaling district, in the state of Selangor. A number of different linear regression models were compared using model orders of lag time, and best model is selected using Akaike Information Criterion (AIC) value. First, dengue estimation models were built for Petaling district using weather variables of mean temperature, relative humidity, cumulative rainfall, and dengue feedback data. The best estimation model is then used to build dengue prediction models, using the k-steps ahead prediction (with one and multiple-step ahead predictions). One-step ahead prediction model was found to capture well pattern of dengue incidences. This information is believed to help health authorities in providing a reminder alarm to the public through medias, on precautions specifically against mosquitoes bites, especially when dengue occurrences is expected to be high. © 2017 IEEE.
AV  - none
EP  - 546
A1  - Thiruchelvam, L.
A1  - Asirvadam, V.S.
A1  - Dass, S.C.
A1  - Daud, H.
A1  - Gill, B.S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041428384&doi=10.1109%2fICSIPA.2017.8120671&partnerID=40&md5=24e188ff9ea685e598fdf8839c57c2d7
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781509055593
Y1  - 2017///
ER  -