A Computational Framework for a Digital Surveillance and Response Tool: Application to Avian Influenza.

Avian influenza viruses have caused infections and deaths in wild birds, commercial poultry, and humans. It poses an increasing threat of a pandemic. To understand the transmission dynamics of avian influenza viruses and assess the effectiveness of different containment strategies, we develop a flexible modeling framework based on multi-layer compartmental models for digital disease surveillance and response in combating pandemics. The model can accommodate other disease outbreaks under diverse settings. We demonstrate its usage on avian influenza and derive the basic reproduction number and spread characteristics. We contrast the effectiveness of different containment strategies and their combination effect in protecting both the human and the bird populations. Our system, a digital surveillance and response system (RealOpt-ASSURE), can record, monitor, and predict avian influenza outbreaks. It combines with intervention strategies to return policies and on-the-ground operations/actions that are needed for best population protection. RealOpt-ASSURE can accept heterogeneous types of surveillance data. It can help decision makers to evaluate the risk of a pandemic and choose proper containment strategies to rapidly mitigate the outbreak.