Epi Evident: Biosurveillance to Monitor, Compare, and Forecast Disease Case Counts

• Published in: Online journal of public health informatics 2018-05, Vol.10 (1)
• Main Authors: Tomaszewski, Natalie, Agnihotri, Meeshu, Cheng, Huiwen, Bhadke, Ashutosh, Henry, Michael, Charles, Lauren E.
• Format: Article
• Language: English
• Subjects: ISDS 2018 Conference Abstracts
• ISSN: 1947-2579; 1947-2579
• DOI: 10.5210/ojphi.v10i1.8324

ObjectiveEpi Evident is a web based application built to empower public health analysts by providing a platform that improves monitoring, comparing, and forecasting case counts and period prevalence of notifiable diseases for any scale jurisdiction at regional, country, or global-level. This proof of concept application development addresses improving visualization, access, situational awareness, and prediction of disease behavior.IntroductionThe Epi Evident application was designed for clear and comprehensive visualization for monitoring, comparing, and forecasting notifiable diseases simultaneously across chosen countries. Epi Evident addresses the taxing analytical evaluation of how diseases behave differently across countries. This application provides a user-friendly platform with easily interpretable analytics which allows analysts to conduct biosurveillance with minimal user tasks. Developed at the Pacific Northwest National Laboratory (PNNL), Epi Evident utilizes time-series disease case count data from the Biosurveillance Ecosystem (BSVE) application Epi Archive (1). This diverse data source is filtered through the flexible Epi Evident workflow for forecast model building designed to integrate any entering combination of country and disease. The application aims to quickly inform analysts of anomalies in disease & location specific behavior and aid in evidence based decision making to help control or prevent disease outbreaks.MethodsA workflow was constructed to define the best disease forecast model for each location based on an adjustable method approach. The differences in disease behavior across countries was achieved through a React/Python application with a user-friendly output for monitoring and comparing different combinations.The forecast model building workflow consisted of three major steps to determine the best fit model for a given disease-country pair: data type, model type, and model comparison & selection. Testing various disease-country combinations allowed for direct evaluation of the workflow efficiency, flexibility, and criteria for determining the best fit model. Data type was characterized as either seasonal, cyclic, or sporadic. Depending on data type, a specific time series forecasting model was applied. In general, seasonal or cyclic data required either an Auto-Regression Integrated Moving Average (ARIMA) model or a Seasonal Auto-Regression Integrated Moving Average (SARIMA) model while sporadic datasets employed a Poisso