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变化模式:非洲高地的疟疾动态与降雨变异性

Shifting patterns: malaria dynamics and rainfall variability in an African highland.

作者信息

Pascual M, Cazelles B, Bouma M J, Chaves L F, Koelle K

机构信息

Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109-1048, USA.

出版信息

Proc Biol Sci. 2008 Jan 22;275(1631):123-32. doi: 10.1098/rspb.2007.1068.

Abstract

The long-term patterns of malaria in the East African highlands typically involve not only a general upward trend in cases but also a dramatic increase in the size of epidemic outbreaks. The role of climate variability in driving epidemic cycles at interannual time scales remains controversial, in part because it has been seen as conflicting with the alternative explanation of purely endogenous cycles exclusively generated by the nonlinear dynamics of the disease. We analyse a long temporal record of monthly cases from 1970 to 2003 in a highland of western Kenya with both a time-series epidemiological model (time-series susceptible-infected-recovered) and a statistical approach specifically developed for non-stationary patterns. Results show that multiyear cycles of malaria outbreaks appear in the 1980s, concomitant with the timing of a regime shift in the dynamics of cases; the cycles become more pronounced in the 1990s, when the coupling between disease and rainfall is also stronger as the variance of rainfall increased at the frequencies of coupling. Disease dynamics and climate forcing play complementary and interacting roles at different temporal scales. Thus, these mechanisms should not be viewed as alternative and their interaction needs to be integrated in the development of future predictive models.

摘要

东非高地疟疾的长期模式通常不仅包括病例总体呈上升趋势,还包括疫情爆发规模的急剧增加。气候变率在年际时间尺度上驱动疫情周期的作用仍存在争议,部分原因是它被视为与疾病非线性动力学单独产生的纯内源性周期这一替代解释相冲突。我们使用时间序列流行病学模型(时间序列易感-感染-康复模型)和专门为非平稳模式开发的统计方法,分析了肯尼亚西部一个高地1970年至2003年的月度病例长期记录。结果表明,疟疾疫情的多年周期出现在20世纪80年代,与病例动态的 regime shift时间一致;这些周期在20世纪90年代变得更加明显,当时随着降雨方差在耦合频率上增加,疾病与降雨之间的耦合也更强。疾病动态和气候强迫在不同时间尺度上发挥着互补和相互作用的作用。因此,不应将这些机制视为替代机制,而需要在未来预测模型的开发中整合它们的相互作用。

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