Using PCA, Poisson and Negative Binomial Model to Study the Climatic Factor and Dengue Fever Outbreak in Lahore


 Dengue Fever, Principal component analysis, Negative Binomial Model.

How to Cite

Syed Afrozuddin Ahmed, Junaid Saghir Siddiqi, Sabah Quaiser, & Shahid Kamal. (2015). Using PCA, Poisson and Negative Binomial Model to Study the Climatic Factor and Dengue Fever Outbreak in Lahore. Journal of Basic & Applied Sciences, 11, 8–16.


Various studies have reported that global warming causes unstable climate and many serious impact to physical environment and public health. The increasing incidence of dengue incidence is now a priority health issue and become a health burden of Pakistan. The study aims to understand, explore and compare the climatic factors of Karachi and Lahore that causes the emergence or increasing rate of dengue fever incidence that effects the population and its health. Principal component analysis (PCA) is performed for the purpose of finding if there is/are any general environmental factor/structure which could be considered as Pakistani climate. We developed an early warning model for the prediction of dengue outbreak in Lahore. This has been done by using Poisson regression and Negative binomial regression model. For this purpose we use daily, weekly and monthly data of Lahore. The negative binomial model with lag (28) for Lahore daily data for climatic variable is best model. Lahore daily and weekly maximum temperature effect negatively and for the past 28 days it is estimated to negatively influence on the dengue occurrence by 26.1% times. Daily wind speed is effecting negatively by 14.7% times and minimum temperature effect positively for the past 28 days by 86.7%times.


World Health Organization. Dengue: Guidelines for diagnosis, treatment, prevention and control. New edition. France 2009.

Farrar J, Focks D, Gubler D, Barrera R, Guzman MG,Simmons C, et al. Towards a global dengue research agenda. Trop Med Int Health 2007; 12: 695-9.

Weaver SC, Vasilakis N. Molecular evolution of dengue viruses: contributions of phylogenetics to understanding the history and epidemiology of the preeminent arboviral disease. Infect Genet Evol 2009; 9: 523-540.

Gubler DJ, Kuno G. Dengue and dengue hemorrhagic fever New York, NY: CAB International 1997.

Afrozuddin SA, Junaid SS. Principal Component Analysis to Explore Climatic Variability that facilitates the Emergence of Dengue Outbreak in Karach. Will be Published in 21st issue (January 2015) of the Pakistan Journal of Meteorology 2014.

Hii YL, Rocklo¨v J, Ng N, Tang CS, Pang FY, et al. Climate variability and increase in incidence and magnitude of dengue incidence in Singapore. Global Health Action 2009; 2.

Su GL. Correlation of climatic factors and dengue incidence in Metro Manila, Philippines. Ambio 2008; 37: 292-4.[292:COCFAD

Arcari P, Tapper N, Pfueller S. Regional Variability in Relationships between Climate and Dengue/DHF in Indonesia. Singaporean Journal of Tropical Geography 2007; 28(3): 251-272.

Johansson MA, Dominici F, Glass GE. Local and Global Effects of Climate on Dengue Transmission in Puerto Rico. PLoS Negl Trop Dis 2009; 3(2). World Academy of Science, Engineering and Technology 2010; 38: 908.

Reiter P. Climate change and mosquito-borne disease. Environmental Health Prospectives 2001; 109: 141-61.

Kuhn K, Campbell-Lendrum D, Haines A, Cox J. Using climate to predict infectious disease epidemics. Geneva: World Health Organization 2005.

Chowell G, Stinchez F. Climate-Based Descriptive Models of Dengue Fever: The 2002 Epidemic in Colima, Mexico. Journal of Environmental Health 2006; pp. 41-44.

Barbazan P, Guiserix M, Boonyuan W, Tuntaprasart W, Pontier D, Gonzalez JP. Modelling the effect of temperature on transmission of dengue. Med Vet Entomol 2010; 24: 66-73.

Brunkard JM, Cifuentes E, Rothenberg SJ. Assessing the role of temperature, precipitation and ENSO in dengue re-emergence on the Texas – Mexico border region. Salud Pública de México 2008; 50: 227-34.

Amarakoon AMD, Chen AA, Rawlins SC, Taylor MA. Dengue epidemics - its association with precipitation and temperature, and its seasonality in some Caribbean countries. West Indian Medical Journal 2004; 53(Supp 2): 60.

Chan YC, Salahuddin NI, Khan J, Tan HC, Seah CL, Li J. Dengue haemorrhagic fever outbreak in Karachi, Pakistan, 1994. Trans R Soc Trop Med Hyg 1995; 89: 619-20.

Hakim ST, Saleem M, Nadeem SG. An Experiencewith Dengue in Pakistan: An Expanding Problem. Ibnosina J Med BS 2011; 3(1): 3-8.

Humayoun MA, Waseem T, Jawa AA, Hashmi MS, Akram J. Multiple dengue serotypes and high frequency of dengue hemorrhagic fever at two tertiary care hospitals in Lahore during the 2008 dengue virus outbreak in Punjab, Pakistan. Int J Infect Dis 2010; 14S3: e54-e59.

Jahan F. Dengue Fever (DF) in Pakistan. Asia Pac Fam Med 2011; 10(1): 1.

Riaz MM, Mumtaz K, Khan MS, Patel J, Tariq M, Hilal H, Siddiqui SA, Shezad F. Out break of Dengue Fever in Karachi 2006: a clinical perspective. J Pak Med Assoc 2009; 59(6): 9-4.


Afrozuddin SA. Random Effects in Generalized Linear Models. Unplished Thesis for the degree of M.Phil, in the Department of Mathematical Statistics and Operational Research, University of Exeter, Exeter, England 1993.

Afrozuddin SA. Normaility Assumptions in Generalized Linear Models. Second International Seminar on Islamic Economics. Organized by department of Statistics, University of Peshawar, Pakistan 1995; 127-142.

Hardin JW, Hilbe JM. Generalized Linear Models and Extensions. A Stata Press Publication. Texas, USA 2012.

Bradley EL. The equivalence of maximum likelihood and weighted least squares in the exponential family. Journal of the American Statistical Association 1973; 68: 199-200.

Knudsen DC. Generalizing Poisson regression: Including a priori information using the methods of offsets. The Professional Geographer 1992; 44: 202-208.

Bock RD, Lieberman M. Fitting a response model for n dichotomously scored items. Psychometrika 1970; 35: 179-197.

Lovett A, Flowerdew R. Analysis of count data using Poisson regression. The Professional Geographer 1989; 41: 190-198.

MacDonald J, Lattimore P. Count models in criminology. In Piquero A, Weisburd D, Eds. Handbook of Quantitative Criminology. Springer 2010.

David L. Linear algebra and its applications. Addison-Wesley. New York.

Will T. Introduction to the singular value Decomposition. Davidson College 1999. will/svd/index.html

A tutorial on Principal component analysis by Jonathon Shlens 1-13.

Edwards J. A user guide to Principal Components. Wiley series in Probability and Mathematical Statistics 1991.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2015 Journal of Basic & Applied Sciences