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. 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. 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.

中文翻译：

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斯里兰卡科伦坡市区登革热发病率分析与预测

在设计控制策略时，了解登革热传播的动态行为至关重要。数学模型已成为描述媒介传播疾病动态的重要工具。经典的区室模型是用于识别媒介传播疾病传播动力学行为的众所周知的方法。由于使用了固定的模型参数，因此经典隔离模型的结果与实际情况不符。这项研究的目的是在不同的模型参数中引入时间，通过提高其可预测性来修改经典的车厢模型。在这项研究中，选择人均矢量密度作为模型参数变化时的时间。使用傅立叶数学分析工具分析了科伦坡市区的登革热发病率，降雨量和温度数据。进一步，确定登革热发病率和气象数据的周期性规律以及登革热发病率与气象数据的相关性，以确定气候数据驱动的人均矢量密度参数函数。与主机动力学相比，通过考虑矢量动力学发生在较快的时间尺度上，借助经典的间隔模型导出了二维数据驱动的间隔模型。此外，根据气候因素在科伦坡市区的影响，引入了人均矢量密度函数以捕获疾病的季节性模式。二维数据驱动的隔室模型可用于预测长达4周的每周登革热发病率。使用相对误差函数评估模型的准确性，并且该模型可用于预测超过75％的准确数据。