Talamantes Jorge, Behseta Sam, Zender Charles S
Department of Physics and Geology, 62 SCI, California State University, Bakersfield, 9001 Stockdale Highway, Bakersfield, CA 93311, USA.
Ann N Y Acad Sci. 2007 Sep;1111:73-82. doi: 10.1196/annals.1406.028. Epub 2007 Mar 8.
Coccidioidomycosis (Valley Fever) is a fungal infection found in the southwestern United States, northern Mexico, and some places in Central and South America. The fungi that cause it (Coccidioides immitis and Coccidioides posadasii) are normally soil dwelling, but, if disturbed, become airborne and infect the host when their spores are inhaled. It is thus natural to surmise that weather conditions, which foster the growth and dispersal of Coccidioides, must have an effect on the number of cases in the endemic areas. This article reviews our attempts to date at quantifying this relationship in Kern County, California (where C. immitis is endemic). We have examined the effect on incidence resulting from precipitation, surface temperature, and wind speed. We have performed our studies by means of a simple linear correlation analysis, and by a generalized autoregressive moving average model. Our first analysis suggests that linear correlations between climatic parameters and incidence are weak; our second analysis indicates that incidence can be predicted largely by considering only the previous history of incidence in the county-the inclusion of climate- or weather-related time sequences improves the model only to a relatively minor extent. Our work therefore suggests that incidence fluctuations (about a seasonally varying background value) are related to biological and/or anthropogenic reasons, and not so much to weather or climate anomalies.
球孢子菌病（谷热）是一种真菌感染，在美国西南部、墨西哥北部以及中美洲和南美洲的一些地区都有发现。引发该病的真菌（粗球孢子菌和波萨达斯球孢子菌）通常生长在土壤中，但如果受到扰动，就会散播到空气中，当人体吸入其孢子时便会受到感染。因此，很自然地可以推测，有利于球孢子菌生长和传播的天气条件必然会对疫区的病例数量产生影响。本文回顾了我们迄今为止在加利福尼亚州克恩县（粗球孢子菌的疫区）量化这种关系的尝试。我们研究了降水、地表温度和风速对发病率的影响。我们通过简单的线性相关分析以及广义自回归移动平均模型进行了研究。我们的第一项分析表明，气候参数与发病率之间的线性相关性较弱；第二项分析表明，仅考虑该县以往的发病率历史记录就能很大程度上预测发病率——纳入与气候或天气相关的时间序列对模型的改进程度相对较小。因此，我们的研究表明，发病率的波动（围绕季节性变化的背景值）与生物和/或人为因素有关，而与天气或气候异常的关系不大。
