ArticleMicrobiology spectrum2022
PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona.
Article in Microbiology spectrum, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed, 24 citations in OpenAlex.
- Urban soils along the Kern River and Los Gatos Creek are hotspots forApplied and environmental microbiology · 2026Article
- Spatial point pattern analysis of environmental effects on valley fever intensity in Phoenix, Arizona.Infectious Disease Modelling · 2026Article
- Hypothesis on the dust-borne atmospheric transport of Coccidioides, causative fungal pathogen for coccidioidomycosis (Valley fever).Medical mycology · 2026Article
- Article
- Urban soils along the Kern River and Los Gatos Creek are hotspots forbioRxiv : the preprint server for biology · 2026Article
- In Vitro Assessment ofGlobal challenges (Hoboken, NJ) · 2025Article
- Article
- The Epidemiology of Coccidioidomycosis (Valley fever) and the Disease Ecology ofPathogens (Basel, Switzerland) · 2025Article
- Investigating Asthma After Coccidioidomycosis Among Patients With Commercial Health Insurance, United States, 2017-2022.Mycoses · 2025Article
- Valley Fever: Fine Mineral Dust Modeling Points to High-Risk Regions and Seasons in California.Environmental health perspectives · 2025Article
- Proposed Relationships Between Climate, Biological Soil Crusts, Human Health, and in Arid Ecosystems.GeoHealth · 2025Review
- Estimating the Exposure-Response Relationship between Fine Mineral Dust Concentration and Coccidioidomycosis Incidence Using Speciated Particulate Matter Data: A Longitudinal Surveillance Study.Environmental health perspectives · 2025Article
- From soil to clinic: current advances in understandingMicrobiology and molecular biology reviews : MMBR · 2024Review
- Understanding the exposure risk of aerosolized Coccidioides in a Valley fever endemic metropolis.Scientific reports · 2024Article
- Coccidioides undetected in soils from agricultural land and uncorrelated with time or the greater soil fungal community on undeveloped land.PLoS pathogens · 2023Article
- Risk of Exposure toMicroorganisms · 2023Article
- Coccidioides Species: A Review of Basic Research: 2022.Journal of fungi (Basel, Switzerland) · 2022Review
- Update on the Epidemiology, Diagnosis, and Treatment of Coccidioidomycosis.Journal of fungi (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coccidioidomycosis (Valley fever) is a disease caused by the fungal pathogens Coccidioides immitis and Coccidioides posadasii that are endemic to the southwestern United States and parts of Mexico and South America. Throughout the range where the pathogens are endemic, there are seasonal patterns of infection rates that are associated with certain climatic variables. Previous studies that looked at annual and monthly relationships of coccidioidomycosis and climate suggest that infection numbers are linked with precipitation and temperature fluctuations; however, these analytic methods may miss important nonlinear, nonmonotonic seasonal relationships between the response (Valley fever cases) and explanatory variables (climate) influencing disease outbreaks. To improve our current knowledge and to retest relationships, we used case data from three counties of high endemicity in southern Arizona paired with climate data to construct a generalized additive statistical model that explores which meteorological parameters are most useful in predicting Valley fever incidence throughout the year. We then use our model to forecast the pattern of Valley fever cases by month. Our model shows that maximum monthly temperature, average PM10, and total precipitation 1 month prior to reported cases (lagged model) were all significant in predicting Valley fever cases. Our model fits Valley fever case data in the region of endemicity of southern Arizona and captures the seasonal relationships that predict when the public is at higher risk of being infected. This study builds on and retests relationships described by previous studies regarding climate variables that are important for predicting risk of infection and understanding this fungal pathogen.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.