Statistical modeling of valley fever data in Kern County, California

Abstract

Coccidioidomycosis (valley fever) is a fungal infection found in the southwestern US, northern Mexico, and some places in Central and South America. The fungus that causes it (Coccidioides immitis) is normally soildwelling but, if disturbed, becomes air-borne and infects the host when its spores are inhaled. It is thus natural to surmise that weather conditions that foster the growth and dispersal of the fungus must have an effect on the number of cases in the endemic areas. We present here an attempt at the modeling of valley fever incidence in Kern County, California, by the implementation of a generalized auto regressive moving average (GARMA) model. We show that the number of valley fever cases can be predicted mainly by considering only the previous history of incidence rates in the county. The inclusion of weather-related time sequences improves the model only to a relatively minor extent. This suggests that fluctuations of incidence rates (about a seasonally varying background value) are related to biological and/or anthropogenic reasons, and not so much to weather anomalies.

Fluctuations in Climate and Incidence of Coccidioidomycosis in Kern County, California

“…Studies to date have demonstrated a strong incidence–climate connection in Arizona, 15 but not in California 17 . Perhaps this is because the latter studies use wind and not PM 10 as a proxy for spore abundance in the atmosphere.…”

“…We used 9 years (Jan 1995–Dec 2003) of weekly mean atmospheric conditions and incidence data to construct a statistical model which allows a degree of prediction 17 . We presented a generalized autoregressive moving average (GARMA) model that maximizes the accuracy of prediction while minimizing the number of input variables 19 .…”

“…We used 9 years (Jan 1995-Dec 2003) of weekly mean atmospheric conditions and incidence data to construct a statistical model which allows a degree of prediction. 17 We presented a generalized autoregressive moving average (GARMA) model that maximizes the accuracy of prediction while minimizing the number of input variables. 19 In other words, we maximize the predictive skill of the model while keeping only the most important input parameters, and removing from the input those variables that do not contribute (or contribute minimally) to the prediction task.…”

“…They can be divided into two groups: (i) in Arizona, where C. posadasii is endemic, a strong connection has been reported [13][14][15] between climatic patterns and coccidioidomycosis incidence, whereas (ii) in Kern County, California, where C. immitis is endemic, only a weak connection has been found. 16,17 In this article, we endeavor to give an overview of our work with incidence and weather data in Kern County. For the purpose of comparison, in this article we discuss some salient points in connection to the works pertaining to Arizona and provide a summary of our own work.…”

What’s Behind the Increasing Rates of Coccidioidomycosis in Arizona and California?

“…In studies performed in Kern County, California, precipitation 8 to 20 months prior played only a small role in incidence of coccidioidomycosis in that area [29]. In a follow-up study, Talamantes et al [30] found that recent increases in the incidence rate of coccidioidomycosis in Kern County were more likely related to host factors than to environmental factors. Currently, California [31] and Arizona [25] are experiencing drought conditions.…”