Abstract
BackgroundUnderstanding 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.ResultsIn 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.ConclusionsThe 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.
Highlights
Dengue is a mosquito–borne tropical viral disease that has rapidly spread during the past few decades and has become one of the major public health issues
The classical SIR model was introduced by Kermack and McKendrick [24], depending on the fact that any population can be divided into three compartments susceptible, infected and recovered, each containing individuals that are identical in terms of their status with respect to the disease
Since vectors’ life cycle is 1–2 weeks and the infected period ends with their death, the vector population (Nv) is divided into two compartments, susceptible vectors (Sv) and infected vectors (Iv)
Summary
Dengue is a mosquito–borne tropical viral disease that has rapidly spread during the past few decades and has become one of the major public health issues. Sri Lanka is a tropical country which has been affected by dengue for over two decades and the infection has Though the first dengue vaccine was licensed in 2015, vaccine performance is dependent on serostatus [8]. For countries with limited resources like Sri Lanka, it is necessary to identify the dynamics of the dengue spread thoroughly to determine more efficient control strategies. Understanding the trend of spread of the disease is vital in prevention of dengue in Sri Lanka. Classical compartmental models are well–known method used to identify the dynamical behavior of spread of a vector borne disease. The aim of this study is to introduce time in varying model parameters, modify the classical compartmental model by improving its predictability power
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
