Understanding the effect of Agricultural Practices on Valley Fever through a Novel Statistical Model using Dust Emissions

Abstract

Valley Fever (VF), or coccidioidomycosis, is a disease significantly influenced by dust exposure. Current statistical models fail to depict the nature of VF’s prevalence over time accurately. Looking to understand potential intervention plans, we determined that agricultural practices (APs) are already used in agriculture to lower dust emissions. Therefore, to investigate the impact of APs on VF epidemiology, dust emission measurements were plugged into the SIR (Susceptible, Infected, and Recovered), a standard epidemiological model to predict change in infected individuals in a population. The analysis demonstrated a clear link between dust exposure and VF incidence, with mulch emerging as the most effective AP at reducing VF cases compared to salt brine and organic material cover. Following this initial testing, soil samples were collected and tested from different locations around the Phoenix Metropolitan area, implementing another variable into this model. This additional testing revealed that the effectiveness of APs in reducing VF cases could vary depending on the soil's location. The computational model, which showed promising results in demonstrating the relationship between dust exposure and VF incidence, holds potential for broader application. It could be utilized by various stakeholders across the southwestern United States to develop interventions to mitigate the impact of VF. Modifications are proposed to ensure the accuracy of the epidemiological model. These include testing a more comprehensive range of APs, integrating machine learning (if granted access to substantial data), and acquiring advanced hardware. Such enhancements refine the model's predictive capabilities and broaden its applicability in VF mitigation efforts.