Modelling Dengue Fever (DF) and Dengue Haemorrhagic Fever (DHF) Outbreak Using Poisson and Negative Binomial Model

Authors: W. Y. Wan Fairos, W. H. Wan Azaki, L. Mohamad Alias, Y. Bee Wah

Abstract:

Dengue fever has become a major concern for health authorities all over the world particularly in the tropical countries. These countries, in particular are experiencing the most worrying outbreak of dengue fever (DF) and dengue haemorrhagic fever (DHF). The DF and DHF epidemics, thus, have become the main causes of hospital admissions and deaths in Malaysia. This paper, therefore, attempts to examine the environmental factors that may influence the recent dengue outbreak. The aim of this study is twofold, firstly is to establish a statistical model to describe the relationship between the number of dengue cases and a range of explanatory variables and secondly, to identify the lag operator for explanatory variables which affect the dengue incidence the most. The explanatory variables involved include the level of cloud cover, percentage of relative humidity, amount of rainfall, maximum temperature, minimum temperature and wind speed. The Poisson and Negative Binomial regression analyses were used in this study. The results of the analyses on the 915 observations (daily data taken from July 2006 to Dec 2008), reveal that the climatic factors comprising of daily temperature and wind speed were found to significantly influence the incidence of dengue fever after 2 and 3 weeks of their occurrences. The effect of humidity, on the other hand, appears to be significant only after 2 weeks.

Keywords: Dengue Fever, Dengue Hemorrhagic Fever, Negative Binomial Regression model, Poisson Regression model.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332568

References:

[1] Dengue Bulletin, World Health Organization. Vol. 25. 2001. Available: http:www.searo.who.int/LinkFiles/Dengue_Bulletin_Volume_25_Dengu e-Bulletin_Vol25.pdf.
[2] Ministry of Health (MOH), Malaysia;Weekly Press Release on Dengue Epidemic Situation, 2009. http://www.moh.gov.my
[3] H. Nor Azura, S. Naomie, A. Rahman, "Simulation of Dengue Outbreak Prediction", UTM Postgraduate Annual Research Seminar. 2006.
[4] K.V.Schreiber, "An Investigation Of Relationships Between Climate And Dengue Using A Water Budgeting Technique", International Journal of Biometeorology 2001. 45:81-89.
[5] B. Andrick,, B. Clark,, K. Nygaard, A. Logar, M. Penaloza, R. Welch, "Infectious disease and climate change: detecting contributing factors and predicting future outbreaks", Geoscience and Remote Sensing, 1997. IGARSS apos; 97. 1997.
[6] G. Chowell, F. Stinchez, "Climate-Based Descriptive Models of Dengue Fever: The 2002 Epidemic in Colima, Mexico", Journal of Enviromental Health, June 2006; pp 41-44.
[7] T. Annelise, D. Xavier, P. Dussart, "Dengue Spatial and Temporal Patterns, French Guiana", Emerging Infectious Diseases Bulletin, Centre of Communicable Disease. (2004).
[8] M.H. Yang, C.D. Ferreira, (2008) "Assessing The Effects Of Vector Control On Dengue Transmission", Journal of Applied Mathematics and Computation 198. 2008. pg 401-413.
[9] M.R. Haliza. "Perception, Knowledge and Behavioural Aspects of dengue Control in Urban Communities in Kuala Lumpur", paper presented in Workshop on Behavioural Interventions in Dengue Control in Malaysia, USM. 2000.
[10] F. Xiuju, C. Liew, N. Lee-Ching, "Time-Series Infectious Disease Data Analysis Using SVM and Genetic Algorithm", Singapore National Environment Agency, 2007 IEEE Congress on Evolutionary Computation (CEC 2007).
[11] GenStat 11th Edition (Trial Version); Available: http://www.vsni.co.uk/software/GenStat
[12] D.G. Kleinbaum, L.L. Kupper, K.E. Muller, A. Nizam, "Applied Regression Analysis and Other Multivariate Methods". Duxbury Press. 3rd ed. 1998.
[13] S.P. Miaou, and H. Lum, Modeling Vehicle Accidents and Highway Geometric Design Relationships. Accident Analysis and Prevention 25(6): 689-709. 1993.
[14] W.H. Green. "Econometric Analysis 5th Ed", Prentice Hall. 2003.
[15] A. Pedan, "Analysis of Count Data Using the SAS® System", SAS Proceedings, SUGI 26; 247-26. 2001.
[16] S. Parodi, E. Bottarelli, "Poisson Regression Model in Epidemiology - An Introduction", Ann. Fac. Medic. Vet. di Parma (Vol. XXVI, 2006) pg: 25-44.
[17] R.S. Pindyck, D.L. Rubenfeld "Econometric Models and Econometric Forecast 4th Ed", McGraw Hill Publication. 1998.
[18] N.E. Breslow, Extra-Poisson Variation in Log-Linear Models. Journal of the Royal Statistical Society.33(1):38-44. 1984.
[19] C. Cameron, and P. Trivedi, "Essentials of Count Data Regression", A Companion to Theoretical Econometrics, 331-348, Blackwell. 1999.
[20] Y. Nagao, U. Thavara, P. Chhitnumsuo, "Climatic And Social Risk Factors For Aedes Manifestation In Rural Thailand", Tropical Medicine And International Health Vol 8 (7), 650-659. 2003.
[21] M.A. Naragdao, S. Weerasinghe, J.R. Guernsey,"Time-Related Associations between Local Meteorological Factors and Dengue Hemorrhagic Fever Hospital Admissions in Iloilo Province, Philippines
[Abstract]", International Symposium on Environmental Assessment (ISEA) 2002.
[22] P. Arcari, N. Tapper, S. Pfueller. "Regional Variability in Relationships between Climate And Dengue/DHF in Indonesia", Singaporean Journal of Tropical Geography. 28 (3): 251-272. 2007.
[23] Johansson M.A, Dominici F., Glass G.E., "Local and Global Effects af Climate on Dengue Transmission in Puerto Rico", PLoS Negl Trop Dis.;3(2). 2009