Abstract
Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.
Highlights
The geographical and seasonal distributions of many infectious diseases are linked to climate; there exists the possibility of using seasonal climate forecasts as predictive indicators for disease outbreaks [1]
This study showed that high influenza incidence is normally associated with the areas with less relative humidity
The relationship was statistically supported by the multiple regression model established in this study indicating that there is a strong statistical association between influenza and rainy days, temperature difference and relative humidity
Summary
The geographical and seasonal distributions of many infectious diseases are linked to climate; there exists the possibility of using seasonal climate forecasts as predictive indicators for disease outbreaks [1]. The existence of seasonal patterns and the climatic factor sensitivities of infectious diseases have been established; an important concern is the boundary at which changes in disease patterns will occur under global climate change conditions [2]. The effect of climate variability on infectious diseases is determined by the transmission cycles of each pathogen. The transmission cycle requirements of a vector or non-human host are typically more susceptible to external environmental influences than those diseases which only involve a pathogen and humans [3,4]. The factors of climate variability—minimum temperature, maximum temperature, humidity and rainfall—are major causes of seasonal diseases that impact socio-economics and public health [5]. There may be great potential in applying the rapidly advancing science of seasonal
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