Problem:
Very little is known about how various pathogens infect birds and how good they are at being hosts of specific diseases. Even something as simple as how birds move and where they are at any given time is, for a large part, guesswork. In order to predict the spread of avian flu and other diseases, it was necessary for Macrosystems to:
- Develop an extension to the Bird Avoidance Model to include Hawaii and Puerto Rico
- Build an algorithmic framework for a predictive modeling tool that provides risk probability data surfaces showing several "most probable" routes of disease spread via wild and domestic animal populations.
- Present a public health data overlay that graphically displays testing results, both positive and negative, for Avian-borne pathogens.
Solution:
Macrosystems dBird was an algorithmic triumph for Macrosystems. First, the Bird Avoidance Model algorithm methodology was created for Puerto Rico and Hawaii. This involved GIS data and several disparate data sources. The second part was creating a white paper giving a general solution to pathogen predictive modeling. This was based on the S-I-R model and involved complex Bayesian statistics. All of these technical elements were encapsulated in a multi-user website model that allows for multiple user types to get the data they need in a real time environment based on a secure log-in.
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