Abstract:
Background: Understanding the dynamical behavior of dengue transmission is essential in designing control
strategies. Mathematical models have become an important tool in describing the dynamics of a vector borne
disease. Classical compartmental models are well–known method used to identify the dynamical behavior of spread
of a vector borne disease. Due to use of fixed model parameters, the results of classical compartmental models do not
match realistic nature. The aim of this study is to introduce time in varying model parameters, modify the classical
compartmental model by improving its predictability power.
Results: In this study, per–capita vector density has been chosen as the time in varying model parameter. The dengue
incidences, rainfall and temperature data in urban Colombo are analyzed using Fourier mathematical analysis tool.
Further, periodic pattern of the reported dengue incidences and meteorological data and correlation of dengue
incidences with meteorological data are identified to determine climate data–driven per–capita vector density
parameter function. By considering that the vector dynamics occurs in faster time scale compares to host dynamics, a
two dimensional data–driven compartmental model is derived with aid of classical compartmental models. Moreover,
a function for per–capita vector density is introduced to capture the seasonal pattern of the disease according to the
effect of climate factors in urban Colombo.
Conclusions: The two dimensional data–driven compartmental model can be used to predict weekly dengue
incidences upto 4 weeks. Accuracy of the model is evaluated using relative error function and the model can be used
to predict more than 75% accurate data.
Keywords: Dengue, IR model, Seasonal pattern, Fourier analysis
