Sci Rep:中科院微生物所刘文军研究组建立流感病毒与气候因...

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2015年12月11日，国际权威学术刊物自然出版集团旗下子刊《Scientific Reports》在线发表了中国科学院微生物研究所刘文军研究组题为Identification of climate factors related to human infection with avian influenza A H7N9 and H5N1 viruses in China的研究论文，论文中研究人员构建了流感病毒与气候因子模型。刘文军研究员为论文的通讯作者。

人类感染H7N9和H5N1高致病性流感病毒的病例呈散发性分布，目前针对环境因子与上述两种病毒发病率的相关性研究较少。鉴于此，科研人员分析该类病毒的环境影响因素，并分别对H7N9和H5N1的感染病例进行了比较分析。

结果表明，流感病毒爆发和传播与温度、湿度等气候因子相关。时空分析发现，H5N1感染病例主要集中在华南、中部以及西北地区，而H7N9的感染病例主要集中在东部和南部沿海地区。H5N1流感病毒感染主要集中在12月、1月和2月三个月份，而H7N9无明显月份分布差异。除了时空特性的差异，两种病毒的适宜气候因子也不相同。H5N1感染的适宜温度为5-10℃，风速1-2 km/h;而H7N9的适宜温度为10-15℃;风速2-3 km/h;另外两株病毒对应的适宜湿度均为60-80%。

通过整合气候因子对人类感染两株高致病禽流感病毒的分析，对于预测可能爆发的疫情和制定预防措施提供了有利依据。该研究拓展了对气候变化与人类疾病关联规律的基本认识，明确气候因子影响人类疾病的阈值，揭示气候变化影响传染性疾病的动态时空传播和进化机制。该研究将进一步探索气候变化直接、间接影响典型传染性疾病的突发性，并揭示气候变化影响传染性疾病的传播机制。

H5N1和H7N9通过PCA分析的预测结果

原文链接：

Identification of climate factors related to human infection with avian influenza A H7N9 and H5N1 viruses in China

原文摘要：

Human influenza infections display a strongly seasonal pattern. However, whether H7N9 and H5N1 infections correlate with climate factors has not been examined. Here, we analyzed 350 cases of H7N9 infection and 47 cases of H5N1 infection. The spatial characteristics of these cases revealed that H5N1 infections mainly occurred in the South, Middle, and Northwest of China, while the occurrence of H7N9 was concentrated in coastal areas of East and South of China. Aside from spatial-temporal characteristics, the most adaptive meteorological conditions for the occurrence of human infections by these two viral subtypes were different. We found that H7N9 infections correlate with climate factors, especially temperature (TEM) and relative humidity (RHU), while H5N1 infections correlate with TEM and atmospheric pressure (PRS). Hence, we propose a risky window (TEM 4–14 °C and RHU 65–95%) for H7N9 infection and (TEM 2–22 °C and PRS 980-1025 kPa) for H5N1 infection. Our results represent the first step in determining the effects of climate factors on two different virus infections in China and provide warning guidelines for the future when provinces fall into the risky windows. These findings revealed integrated predictive meteorological factors rooted in statistic data that enable the establishment of preventive actions and precautionary measures against future outbreaks.

来源：生物帮