A spatio-temporal climate-based model of early dengue fever warning in southern Taiwan |
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Authors: | Hwa-Lung Yu Shang-Jen Yang Hsin-Ju Yen George Christakos |
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Institution: | (1) Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei, Taiwan;(2) Department of Geography, San Diego State University, San Diego, CA, USA |
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Abstract: | Dengue Fever (DF) has been identified by the World Health organization (WHO) as one of the most serious vector-borne infectious
diseases in tropical and sub-tropical areas. During 2007, in particular, there were over 2,000 DF cases in Taiwan, which was
the highest number of cases in the recorded history of Taiwan epidemics. Most DF studies have focused mainly on temporal DF
patterns and its close association with climatic covariates, whereas they have understated spatial DF patterns (spatial dependence
and clustering) and composite space–time effects. The present study proposes a spatio-temporal DF prediction approach based
on stochastic Bayesian Maximum Entropy (BME) analysis. Core and site-specific knowledge bases are considered, including climate
and health datasets under conditions of uncertainty, space–time dependence functions, and a Poisson regression model of climatic
variables contributing to DF occurrences in southern Taiwan during 2007. The results show that the DF outbreaks in the study
area are highly influenced by climatic conditions. Furthermore, the analysis can provide the required “one-week-ahead” outbreak
warnings based on spatio-temporal predictions of DF distributions. Therefore, the proposed approach can provide the Taiwan
Disease Control Agency with a valuable tool to timely identify, control, and even efficiently prevent DF spreading across
space–time. |
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