Modelling Dengue Fever (DF) and Dengue Haemorrhagic Fever (DHF) Outbreak Using Poisson and Negative Binomial Model
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.
[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
[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
@article{"International Journal of Medical, Medicine and Health Sciences:55848", author = "W. Y. Wan Fairos and W. H. Wan Azaki and L. Mohamad Alias and Y. Bee Wah", title = "Modelling Dengue Fever (DF) and Dengue Haemorrhagic Fever (DHF) Outbreak Using Poisson and Negative Binomial Model", 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.", volume = "4", number = "2", pages = "51-6", }
