Projects
ForeSITE aims to develop and implement a library of modeling and analytic tools belonging to three toolsets: Anomaly Detection & Forecasting/Nowcasting, Parameter Estimation & Scenario Modeling, and Economic Impact Analysis. We will establish a flexible, sustainable, practical, and replicable modeling infrastructure for a broad range of settings, including local health departments in rural locations, which have limited access to analytical support. Below, we have included a sampling of our ongoing efforts.
The purpose of this project is to build an agent-based intervention model for measles in Utah school settings to emphasize the importance of vaccination coverage and the impact of increased vaccine exemption rates. UDHHS hopes to be able to apply this model to inform the Utah legislative body to maintain or improve school vaccination policies.
This app intends to easily distribute the capability of time series statistical alerts to analysts in public health. While software tools such as these usually require a software developer or statistician, the goal is to provide an easy-to-use solution so that analysts in STLT Health Departments can leverage an adaptable alerting system their current capacities.
Our cost-benefit analysis aims to generate Utah-specific estimates of the benefits to employers for maintaining a vaccinated workforce. Our economic team will model costs for employers implementing a workplace vaccination program compared to those who do not. Findings from this work may be used to inform Utah legislative decisions.
The purpose of this project is to assist DHHS in the application of space-time clustering methods to detect anomalies in reported enteric infections. The project will use synthetic data to identify optimal parameters, provide small area population estimates to improve cluster delineation, develop methods to incorporate specific potential sources of transmission (pools, water systems), and package these into easily-used tools for implementation by public health partners.
This disease ecology project intends to develop a model to identify high risk areas for early outbreaks of highly pathogenic avian influenza (HPAI) in humans in Washington state. This project aims to produce a geospatial map highlighting areas where early detection of HPAI in humans is most likely to occur. The anticipated uses of this tool will be to inform targeted surveillance and response planning among STLT partners.
The goal of this project is to create a software program that automates the process of generating local health departments’ monthly disease incident reports. This work aims to delivers a basic system for quickly updating data tables, as well as an advanced system for automating the entire reporting process.