Fluctuations in Climate and Incidence of Coccidioidomycosis in Kern County, California
A Review
Article first published online: 10 OCT 2007
DOI: 10.1196/annals.1406.028
Issue

Annals of the New York Academy of Sciences
Volume 1111, Coccidioidomycosis Sixth International Symposium pages 73–82, September 2007
Additional Information
How to Cite
TALAMANTES, J., BEHSETA, S. and ZENDER, C. S. (2007), Fluctuations in Climate and Incidence of Coccidioidomycosis in Kern County, California. Annals of the New York Academy of Sciences, 1111: 73–82. doi: 10.1196/annals.1406.028
Publication History
- Issue published online: 10 OCT 2007
- Article first published online: 10 OCT 2007
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Keywords:
- Valley Fever;
- coccidioidomycosis;
- Coccidioides immitis;
- Coccidioides posadasii;
- disease statistical modeling;
- GARMA modeling;
- climate and health
Abstract: 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.

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