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
- 35 Downloads
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 Niño 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.
KeywordsEl Niño southern oscillation (ENSO) Auto-regressive (AR) model Poisson auto regressive exogenous (PARX) Negative binomial auto regressive exogenous (NBARX)
We thank the dengue survival cells, meteorological department, Karachi and National oceanic and Atmospheric Administration website (www.ncdc.noaa.gov/teleconnections/enso/), for providing the data used in this work. We also thanks sincerely to the Higher Education commission(HEC) for providing the National Research Project for university (NRPU) grants to carry out second author’s PhD research work under the project (NRPU/#20-4039/R&D/HEC/14/697). Three anonymous reviewers and Editor are also thanked for their critical and valuable comments to improve the manuscript as presented. Some results of this study will be part of the Ph.D. thesis of the second author to be submitted at the Mathematical Sciences Research Centre Federal Urdu University Arts, Sciences & Technology, Karachi, Pakistan.
- Aribodor D, Ugwuanyi I, Aribodor O (2016) Challenges to achieving malaria elimination in Nigeria. American Journal of Public Health Research 4(1):38–41Google Scholar
- Cao, Y. (2017). Statistical modelling of mosquito abundance and West Nile virus risk with weather conditionsGoogle Scholar
- Eunice A (2018) Statistical Modeling of Malaria Incidences in Apac District, Uganda, JKUATGoogle Scholar
- Hilbe JM (2011) Negative binomial regression. Cambridge University PressGoogle Scholar
- Hussain M, Abbas S, Ansari M (2012) Arabian seawater temperature fluctuations in the twentieth century. J Basic App Sci 8(1):105–109Google Scholar
- Rahim, H. A., Ibrahim, F., Taib, M., Rahim, R. A., & Sam, Y. M. (2008). Monitoring haemoglobin status in dengue patients using ARMAX model. Paper presented at the Information Technology, 2008. ITSim 2008. International Symposium onGoogle Scholar
- Rampelotto P, Rigozo N, da Rosa M, Prestes A, Frigo E, Echer MS, Nordemann D (2012) Variability of rainfall and temperature (1912–2008) parameters measured from Santa Maria (29° 41′ S, 53° 48′ W) and their connections with ENSO and solar activity. J Atmos Sol Terr Phys 77:152-160CrossRefGoogle Scholar
- Rusch HL, Perry J (2011) Dengue and the Landscape: a threat to public health. National Center for Case Study Teaching In Science, New York, pp 1–4Google Scholar
- Sadiq, B., & Brown, P. (2017). Assessing the Impact of Climatic Variables on Malaria Cases among Pregnant Women in South-Western NigeriaGoogle Scholar
- Zack Guido, 2011, Extreme Events Southwest Climate Change Network, University of Arizona Google Scholar
- Zalina MD, Desa MNM, Nguyen V, Kassim AHM (2002) Selecting a probability distribution for extreme rainfall series in Malaysia. Water Sci Technol 45(2):63–68Google Scholar