Table 1 Previously published models linking weather or other covariates to Valley fever incidence or Coccidioides exposure.
From: Understanding the exposure risk of aerosolized Coccidioides in a Valley fever endemic metropolis
Area of interest | Years | Model type | Response variable | Weather predictors considered | Other variables considered | Significant weather predictors | Significant other variables | Author conclusions | Citation |
|---|---|---|---|---|---|---|---|---|---|
Maricopa, AZ | Aug 2015, Oct 2016, Fall 2017, Jan 2018–June 2019 | Univariate binomial mixed effects model | Daily Coccidioides presence absence data | Wind speed, temperature, visibility, precipitation | Soil moisture, PM10 | Wind speed | None | Coccidioides aerosolization increases in response to high gusts, high temps and low soil moisture | This study |
California (14 counties) | 2000–2020 | Multiple (ensemble) | Clinical cases | Temperature, precipitation | Soil texture, elevation, % impervious surface | Lagged precipitation, lagged temperature | Dependent on model | Drought can decrease Valley fever cases in the short term but increase cases in the years following the drought conditions | |
Maricopa, Pima, Pinal Counties, AZ | 2013–2018 | General additive model (time series) | Clinical cases | Wind speed, mean max temperature, and total monthly precipitation with 2-month lag on all variables | PM10 with 2-month lag | Mean max temp, lagged precipitation, wind speed, precipitation | Lagged PM10 | Lagged PM10 within the winter months had the largest impact on Valley fever cases | |
Maricopa County, AZ & Kern County, CA | 2006–2020 | Superposed epoch analysis | Clinical cases | None | Dust storm data | None | None | No indication of an increase in Valley fever cases following dust storm activity | |
San Joaquin Valley, CA and southcentral, AZ | 2000–2015 | Linear and nonlinear regression | Clinical cases | Air temperature, precipitation | Soil moisture, dust concentration, NDVI, cropland area | Not applicable | Not applicable | Air temperature, precipitation, soil moisture, dust concentration, NDVI, and cropland area could be significant covariates for a predictive model | |
Maricopa, Pima Counties, AZ | 2001–2011 | Correlation Analyses | Clinical cases | Precipitation | PM10, PM2.5, dust number, dust frequency | Not applicable | Not applicable | Dust frequency was correlated but does not explain all variability in Valley fever cases | |
Maricopa, Pima Counties, AZ | 1995–2006 | Correlational analyses, regression | Clinical cases | None | NDVI | None | NDVI | Inverse relationship between NDVI and Valley fever cases | |
Kern County, CA | 1995–2003 | Generalized auto regressive moving average model | Clinical cases (weekly) | Temperature, precipitation, wind speed | None | None | None | Relationship between weather parameters and Valley fever case fluctuations are weak | |
Kern County, CA | 1980–2002 | Univariate and | Clinical cases (monthly) | Temperature, precipitation, wind speed | None | Temperature, precipitation, wind speed | None | Relationship between weather parameters and Valley fever case fluctuations are weak | |
Pima County, AZ | 1992–2003 | Linear regression | Clinical cases | Precipitation | PM10 | Precipitation (lagged) | PM10 (current) | Important role of precipitation with a 1.5–2-year lag prior to exposure | |
Maricopa County, AZ | 1998–2001 | Poisson regression analysis | Clinical cases | Precipitation, wind speed, temperature | Building permits, Palmer Z index, Palmer drought severity index, PM10 | Precipitation, wind, temperature | Building permits, Palmer Z index, Palmer drought severity index, PM10 | Poisson regression identified several weather, dust, and drought covariates as significant to Valley fever incidence | |
Pima County, AZ | 1948–1998 | Linear regression and composite analysis | Clinical cases | Precipitation (total, average. Max), dew point, average wind speed | Palmer drought severity index (PDSI) | Temperature and precipitation with different lags depending on month | None | Identified significant relationships with temperature and precipitation at different lag times corresponding to the ecology of Coccidioides |
