Estimating coccidioidomycosis endemicity while accounting for imperfect detection using spatio-temporal occupancy...

Area of Science:

• Environmental Epidemiology
• Infectious Disease Ecology
• Spatial Statistics

Background:

• Coccidioidomycosis (Valley fever) incidence is increasing, potentially due to climate change expanding the endemic range of Coccidioides fungi.
• Current Valley fever case data are imperfect proxies for Coccidioides endemicity due to underreporting and misdiagnosis.
• Accurate mapping of Coccidioides endemicity is crucial for public health surveillance and resource allocation.

Purpose of the Study:

• To develop and apply a Bayesian spatio-temporal occupancy model to estimate Coccidioides endemicity.
• To account for imperfect detection in Valley fever case data.
• To identify regions with high endemicity and understand factors influencing detection.

Main Methods:

• Utilized a Bayesian, spatio-temporal occupancy model.
• Analyzed monthly, county-level presence/absence data of Valley fever cases.
• Incorporated imperfect detection, precipitation, and agricultural factors into the model.

Main Results:

• Estimated high Coccidioides endemicity in southern California, Arizona, and New Mexico.
• Identified endemic regions in western Texas, eastern Colorado, and southeastern Washington, areas lacking mandated reporting.
• Found inverse relationships between precipitation and case detection, and a positive association with agriculture.

Conclusions:

• The model provides a robust method for estimating Coccidioides endemicity despite imperfect case data.
• Findings suggest the endemic range is expanding and highlight areas needing enhanced surveillance.
• Results can inform public health strategies and the potential expansion of mandatory case reporting.