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

Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhalingCoccidioidesfungal spores. Incidence has risen in recentyears, and it is believed the endemic region forCoccidioidesis expanding in response to climate change. While Valley fever casedata can help us understand trends in disease risk, using case data as a proxy forCoccidioidesendemicity is not ideal because casedata suffer from imperfect detection, including false positives (eg, travel-related cases reported outside of endemic area) and falsenegatives (eg, misdiagnosis or underreporting). We proposed a Bayesian, spatio-temporal occupancy model to relate monthly, county-level presence/absence data on Valley fever cases to latent endemicity ofCoccidioides, accounting for imperfect detection. We usedour model to estimate endemicity in the western United States. We estimated high probability of endemicity in southern California,Arizona, and New Mexico, but also in regions without mandated reporting, including western Texas, eastern Colorado, and southeasternWashington. We also quantified spatio-temporal variability in detectability of Valley fever, given an area is endemic toCoccidioides.Weestimated an inverse relationship between lagged 3- and 9-month precipitation and case detection, and a positive association withagriculture. This work can help inform public health surveillance needs and identify areas that would benefit from mandatory casereporting. This article is part of a Special Collection on Environmental Epidemiology.