METHOD OF IDENTIFYING CLUSTERS AND CONNECTIVITY BETWEEN CLUSTERS

• US 20090082997A1
• Filed: 12/21/2006
• Published: 03/26/2009
• Est. Priority Date: 12/21/2005
• Status: Abandoned Application

• Current Assignee: Ariel L. Rivas, Kevin L. Anderson, Michael G. Tokman, Rodolfo R. Rodriguez, Ruben N. Gonzalez, Steven J. Schwager
• Original Assignee: Ariel L. Rivas, Kevin L. Anderson, Michael G. Tokman, Rodolfo R. Rodriguez, Ruben N. Gonzalez, Steven J. Schwager
• Inventors: Schwager, Steven J., Tokman, Michael G., Gonzalez, Ruben N., Anderson, Kevin L., Rivas, Ariel L., Rodriguez, Rodolfo R.
• Application Number:
  US12/158,398
• Publication Number:
  US 20090082997A1
• Time in Patent Office: Days
• US Class Current: 702/179
• CPC Class Codes: G06F 18/2323 based on graph theory, e.g. minimum spanning trees [MST] or graph cuts
• CPC Class Codes: G16H 50/80 for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The present invention relates to a method for predicting outcome and evaluation of clusters. Particularly the invention relates to a method of determining deviation and predict future out comes of clusters with certain attributes. In one embodiment, the present invention relates to epidemic outbreaks of disease and, more particularly, to a method for predicting the spread thereof.

Claims

1. A method of identifying clusters from a set of points selected from the group consisting of individual points and spatial points comprising:

a) selecting a geographic area;

b) acquiring data on the spatial coordinates that characterize the selected geographic area;

c) selecting attributes to be measured for each point of the set;

d) processing the attributes of each point;

e) determining the linkage between the points based on the attributes;

f) identifying from the group comprising the spatial coordinates and time of any point having an attribute deviating significantly from the average point in the set as a cluster.

2. A method of determining connectivity between a set of points selected from the group consisting of individual points and spatial points comprising:

a) selecting a geographic area;

b) acquiring data on the spatial coordinates that characterize the selected geographic area;

c) selecting attributes to be measured for each point of the set;

d) processing the attributes of each point;

e) determining the linkage between the points based on the attributes;

f) identifying the magnitude of the attributes of any point having an attribute deviating significantly from the average point in the set as a cluster.

3. A method for prediction of the spread of an epidemic outbreak of a disease comprising:

a) selecting a geographic area;

b) acquiring data on the spatial coordinates that characterize the selected geographic area;

c) selecting disease attributes to be measured for each point of the set;

d) processing the attributes of each point;

e) determining the linkage between the points based on the attributes;

f) determining the rate of change of the attributes over time.

4. A method according to claim 3 wherein a geographical information system is used.

5. A method according to claim 3 wherein the epidemic outbreak is in an animal population.

6. A method according to claim 3 wherein the epidemic outbreak is in a human population.