越南地區氣象與登革熱流行之相關分析

Weather-based predictive models for Dengue fever epidemics in Vietnam

阮氏萍明(Thi-Binh-Minh Nguyen)

指導教授 ： 王玉純

中原大學/工學院/土木工程研究所/碩士(2013年)

https://doi.org/10.6840/cycu201300967

摘要

登革熱是越南地區重要的傳染病之一，本研究主旨為利用時間及空間統計模式，探討2000年~2007年越南地區登革熱通報個案數與當地氣候因子及社會經濟因子之相關性。
本研究第一部份採用ARIMA來分析登革熱發生與五個氣象因子的相關性(逐月降雨量、月平均溫度、最高溫度月均值、最低溫度月均值以及相對濕度)。此外，我們亦考慮流行指數(Population Index)及月流行指數(Monthly Population Index)對通報病例數的影響，所有的模式分別依照八個地區(Northeast area, Northwest area, Red River Delta area, North Central area, South Central area, Highlands area, Southeast area and Nine Dragon Delta area)各別進行。本研究證實最高溫度與越南多數地區的登革熱流行具有顯著相關性，並可應用模式預測未來登革熱之流行。
本研究第二部分採用空間統計-地理加權迴歸(Geographically Weighted Regression，GWR)探討2006年及2007年地區性社會經濟因子與通報數之相關性，空間統計分別由三個層級(國家、區域及省分)來分析。研究成果呈現地區性社會經濟因子與登革熱流行具相關性，迴歸強度由判定係數(R-Square value)及Moran’s Index決定，本研究證實族群人口數及族群密度為登革熱流行之強相關因子，其中越南北部地區受到族群密度影響較大，越南南部則以人口數影響較大。

關鍵字

GWR ； 越南 ； 溫度 ； ARIMA ； 登革熱 ； 社會經濟因子

並列摘要

Dengue fever is one of the most dangerous infectious diseases in Vietnam. This study aims to analyze the associations between climatic factors and socio-economics variables and reported dengue fever cases using temporal and spatial models in Vietnam from 2000 to 2007.
This study first used Autoregressive Integrated Moving Average model (ARIMA) to determine autocorrelations between denge fever incidence and five weather factors, such as rainfall, mean temperature, maximum temperature, minimum temperature and relative humidity in eight areas in Vietnam. We further used ARIMA by including weather variables, Population Index and Monthly Population Index to forecast number of dengue cases in Vietnam. This study identified maximum temperature has the most significant influence on dengue epidemics in almost all areas (6 over 8 areas when running model between single variable and number of dengue cases) and it can be used effectively to predict dengue fever epidemics.
This study further conducted a spatial analysis by Geographically Weighted Regression (GWR) to model the associations between socio-economics factors and reported dengue fever cases from 2006 to 2007 in whole Vietnam. The study was carried out in three levels. First model analyzed the associations between socio-economics covariates and dengue fever epidemics in eight areas in Vietnam (regional level; eight areas: Northeast, Northwest,Red River Delta, North Central, South Central, Highlands, Southeast, Nine Dragon Delta). Second model determined the relationships between social factors and dengue epidemics in 64 provinces in Vietnam (provincial level). Last model presented effects of socio-economics conditions on reported dengue cases in provinces in each area of Vietnam. The strength of regression is explained by determinant coefficient (R-Square value) and significant level is determined by Moran’s Index through spatial autocorrelation test (or Moran’s Index test). We found influence of socio-economics variables on dengue epidemics in each regional area was clearer than in whole Vietnam (in national scale, we obtained dispersed/clustered patterns in many models and had R-square value < 0.5). Population and population density were two factors that had the strongest impact on dengue fever outbreaks. While Northern part of Vietnam witnessed the absolute victory of population density, Southern part of Vietnam saw the power of population.

並列關鍵字

Vietnam ； GWR ； ARIMA ； temperature ； Dengue fever ； socio-economics data

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