Analysis and forecast of dengue incidence in urban Colombo, Sri Lanka.

Q1 Mathematics
Kkwh Erandi, Ssn Perera, A C Mahasinghe
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引用次数: 3

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.

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斯里兰卡科伦坡城市登革热发病率分析与预测
背景:了解登革热传播的动态行为对设计控制策略至关重要。数学模型已成为描述媒介传播疾病动力学的重要工具。经典的区室模型是公认的用于确定媒介传播疾病传播动力学行为的方法。由于采用固定的模型参数,经典的隔室模型的计算结果与实际情况不相符。本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正经典的隔间模型。结果:本研究选择人均向量密度作为变模型参数中的时间。采用傅立叶数学分析工具对科伦坡市区登革热发病率、降雨量和气温数据进行分析。此外,确定登革热发病率报告与气象数据的周期模式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媒介密度参数函数。考虑到矢量动力学比宿主动力学发生的时间尺度更快,在经典区室模型的基础上推导了二维数据驱动的区室模型。此外,根据科伦坡城市气候因素的影响,引入了人均病媒密度函数来捕捉疾病的季节性模式。结论:二维数据驱动的区室模型可用于预测4周内登革热的周发病率。利用相对误差函数对模型的精度进行了评价,模型的预测准确率达到75%以上。
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来源期刊
Theoretical Biology and Medical Modelling
Theoretical Biology and Medical Modelling MATHEMATICAL & COMPUTATIONAL BIOLOGY-
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: Theoretical Biology and Medical Modelling is an open access peer-reviewed journal adopting a broad definition of "biology" and focusing on theoretical ideas and models associated with developments in biology and medicine. Mathematicians, biologists and clinicians of various specialisms, philosophers and historians of science are all contributing to the emergence of novel concepts in an age of systems biology, bioinformatics and computer modelling. This is the field in which Theoretical Biology and Medical Modelling operates. We welcome submissions that are technically sound and offering either improved understanding in biology and medicine or progress in theory or method.
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