Authors: Chenlu Li*, Beijing Normal University, Xiaoxu Wu, Beijing Normal University, Duoying Ji, Beijing Normal University, Jianing Liu, Beijing Normal University, Jie Yin, Beijing Normal University, Zhiyi Guo, Beijing Normal University
Topics: Human-Environment Geography
Keywords: dysentery, climate factors, climate risk window, model, projection
Session Type: Poster
Presentation File: No File Uploaded
Dysentery, an acute infectious disease still prevalent in many parts of the world, especially in developing counties, is caused by a group of bacteria known as Shigella. Because of the sensitivity of dysentery to climate change, the relationship between dysentery incidence and climate factors has become a growing research interest. Previous studies have mainly focused on identifying key climate factors and examining the relationship between dysentery incidence and climate change. However, there has been little research on modeling and projecting the occurrence of dysentery based on key climate factors. Here we selected Binyang County in China, a subtropical monsoon climate region where epidemics are typical, as the study area. We used heat maps to extract climate risk windows (with minimum temperatures of 24 °C–26 °C, precipitation amounts of 160–380 mm, and relative humidities of 69%–85%) for dysentery transmission. We then developed a climate-dysentery model and validated its reliability. Finally, based on climate risk windows and the developed model, three earth system models (BNU-ESM, IPSL-CM5A-MR, and MIROC-ESM) were used to project future occurrence periods and incidence of dysentery under future climate condition. The projected results showed that May to August were high-incidence periods, and the occurrence of dysentery exhibited an upward trend in the future. Accordingly, we provided two practical recommendations for defeating dysentery: seasonal control in the study area, and advocacy of prevention in potentially pandemic regions. This study hopes to provide a theoretical basis for developing a dysentery warning system from the perspective of climate change.
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