https://scholars.lib.ntu.edu.tw/handle/123456789/387666
Title: | Impact of meteorological factors on the spatiotemporal patterns of dengue fever incidence | Authors: | Chien, L.-C. Yu, H.-L. HWA-LUNG YU |
Keywords: | Climatic effect; Dengue fever; Distributed lag nonlinear effects; Spatiotemporal modeling; Temporal delayed effect | Issue Date: | 2014 | Journal Volume: | 73 | Start page/Pages: | 46-56 | Source: | Environment International | Abstract: | Dengue fever is one of the most widespread vector-borne diseases and has caused more than 50. million infections annually over the world. For the purposes of disease prevention and climate change health impact assessment, it is crucial to understand the weather-disease associations for dengue fever. This study investigated the nonlinear delayed impact of meteorological conditions on the spatiotemporal variations of dengue fever in southern Taiwan during 1998-2011. We present a novel integration of a distributed lag nonlinear model and Markov random fields to assess the nonlinear lagged effects of weather variables on temporal dynamics of dengue fever and to account for the geographical heterogeneity. This study identified the most significant meteorological measures to dengue fever variations, i.e., weekly minimum temperature, and the weekly maximum 24-hour rainfall, by obtaining the relative risk (RR) with respect to disease counts and a continuous 20-week lagged time. Results show that RR increased as minimum temperature increased, especially for the lagged period 5-18. weeks, and also suggest that the time to high disease risks can be decreased. Once the occurrence of maximum 24-hour rainfall is >. 50. mm, an associated increased RR lasted for up to 15. weeks. A temporary one-month decrease in the RR of dengue fever is noted following the extreme rain. In addition, the elevated incidence risk is identified in highly populated areas. Our results highlight the high nonlinearity of temporal lagged effects and magnitudes of temperature and rainfall on dengue fever epidemics. The results can be a practical reference for the early warning of dengue fever. © 2014 Elsevier Ltd. |
URI: | http://www.scopus.com/inward/record.url?eid=2-s2.0-84905190212&partnerID=MN8TOARS http://scholars.lib.ntu.edu.tw/handle/123456789/387666 |
DOI: | 10.1016/j.envint.2014.06.018 | SDG/Keyword: | Climate change; Markov processes; Nonlinear analysis; Rain; Climatic effects; Delayed effects; Dengue fevers; Nonlinear effect; Spatio-temporal models; Risk assessment; rain; rain; climate effect; dengue fever; disease control; epidemic; health impact; health risk; Markov chain; nonlinearity; risk assessment; spatiotemporal analysis; climate change; meteorology; temporal analysis; article; autumn; controlled study; dengue; environmental factor; environmental impact; environmental temperature; geographic distribution; human; incidence; meteorological phenomena; Poisson distribution; priority journal; seasonal variation; spatiotemporal analysis; spring; summer; Taiwan; temperature measurement; winter; Article; average temperature; dengue; environmental monitoring; environmental temperature; epidemic; incidence; infection risk; maximum 1 hour rainfall; maximum 24 hour rainfall; maximum temperature; meteorological phenomena; meteorology; minimum temperature; nonlinear system; population density; prediction; probability; risk factor; sensitivity analysis; spatiotemporal analysis; total rainfall; weather; dengue; temperature; epidemiology; Taiwan; Dengue; Humans; Incidence; Rain; Taiwan; Temperature [SDGs]SDG3 [SDGs]SDG13 |
Appears in Collections: | 生物環境系統工程學系 |
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