Assessing the impact of EI Niño southern oscillation index and land surface temperature fluctuations on dengue fever outbreaks using ARIMAX(p)-PARX(p)-NBARX(p) models

Abstract

The dengue infectious disease remnants a human health problem in tropical and subtropical countries. In an Auto Regressive model to assess the role of climatic parameter El Nino Southern Oscillation and land surface mean monthly temperture on dengue outbreaks of the Karachi region over the monthly time interval January 2001 to December 2016, subsequent to stabilization of variance, we are able to apply and predict an Auto Regressive Integrated Moving Average Exogenous-Transfer Function model by using the order selection criteria namely Final Prediction Error and Akaike’s information. The results confirmed that ARIMAX (2,1,2) has fitted model, although an Auto Regressive model predicts a smaller decline in dengue data series than the auto Poisson Regression model. Additionally, we developed an alternative model for the Poisson Autoregressive Exogenous model in order (p) and Negative Binomial Auto Regressive Exogenous model, deliver the best fit as compared to the Poisson Auto Regressive Exogenous model whereas indicated by the deviances. The Pearson test showed a strong positive association between temperature and dengue, while ENSO inverse indication. High dengue outbreaks are detected in the months of September, October, and November. This comparative study exposed a significant relationship among monthly dengue and climatic variation by Auto Regressive Integrated Moving Average Exogenous (ARIMAX), Poisson and Negative-Binomial Autoregressive Exogenous (PARX-NBARX) models, while smallest values of AIC (3.89), Negative Binomial Auto Regressive Exogenous, are preferred more accurate model for the next 12 months forecasting. This study has provided useful information for the development of dengue predictions and future warning systems.