Inclusion of Climate Variables for Dengue Prediction Model: Preliminary Analysis

Abstract

This study aimed to build best dengue cases prediction model for Petaling district, in Selangor. Linear Least Square estimation method is used to build the models and Mean Square Error (MSE) and Akaike Information Criterion (AIC) value is used as tool of comparison between models. Prior to model development, the respective variables are first normalized, using 0–1 normalization procedure. Next, significant predictors are identified from weather variables namely mean temperature, relative humidity, and rainfall. Thirdly, feedback data was included and identified if could yield better prediction models. Few model orders of lag time are built simultaneously, and the most accurate prediction model was selected for Petaling district. Study found dengue prediction models including all three climate variables of mean temperature, relative humidity, cumulative rainfall and together with previous dengue cases to have the lowest MSE and AIC values. This is aligned with previous studies which selected model with climate and previous dengue cases models as best model fit. Thus, study proposed future studies to incorporate all three climate variables and previous dengue cases while developing dengue prediction models.