Identifying current and emerging resources and tools utilized for detection, prediction, and visualization of viral zoonotic disease clusters: a Delphi study

Abstract

Zoonotic disease surveillance presents a substantial problem in the management of public health. Globally, zoonoses have the potential to spread and negatively impact population health economic growth, and security. This research was conducted to investigate the current data sources, analytical methods, and limitations for cluster detection and prediction with particular interest in emerging bioinformatics tools and resources to inform the development of zoonotic surveillance spatial decision support systems. We recruited 10 local health personnel to participate in a Delphi study. Participants agreed cluster detection is a priority, though mathematical modeling methods and bioinformatics resources are not commonly used toward this endeavor. However, participants indicated a desire to utilize preventative measures. We identified many limitations for identifying clusters including software availability, appropriateness, training, and usage of emerging genetic data. Future decision support system development should focus on state health personnel priorities and tasks to better utilize emerging developments and available data.