Linked animal-human health visual analytics

• US 20090137881A1
• Filed: 10/01/2008
• Published: 05/28/2009
• Est. Priority Date: 10/01/2007
• Status: Abandoned Application

• Current Assignee: Purdue Research Foundation (Purdue University)
• Original Assignee: Purdue Research Foundation (Purdue University)
• Inventors: Jang, Yun, Ebert, David S., Tyner, Benjamin N., Amass, Sandra, Maciejewski, Ross Larry, Cleveland, William
• Application Number:
  US12/286,756
• Publication Number:
  US 20090137881A1
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: G16H 50/80 for detecting, monitoring or modelling epidemics or pandemics, e.g. flu

Abstract

Coordinated animal-human health monitoring can provide an early warning system with fewer false alarms for naturally occurring disease outbreaks, as well as biological, chemical and environmental incidents. This monitoring requires the integration and analysis of multi-field, multi-scale and multi-source data sets. In order to better understand these data sets, models and measurements at different resolutions must be analyzed. To facilitate these investigations, we have created an application to provide a visual analytics framework for analyzing both human emergency room data and veterinary hospital data. Our integrated visual analytic tool links temporally varying geospatial visualization of animal and human patient health information with advanced statistical analysis of these multi-source data. Various statistical analysis techniques have been applied in conjunction with a spatio-temporal viewing window. Such an application provides researchers with the ability to visually search the data for clusters in both a statistical model view and a spatio-temporal view. Our interface provides a factor specification/filtering component to allow exploration of causal factors and spread patterns. In this paper, we will discuss the application of our linked animal-human visual analytics (LAHVA) tool to two specific case studies. The first case study is the effect of seasonal influenza and its correlation with different companion animals (e.g., cats, dogs) syndromes. Here we use data from the Indiana Network for Patient Care (INPC) and Banfield Pet Hospitals in an attempt to determine if there are correlations between respiratory syndromes representing the onset of seasonal influenza in humans and general respiratory syndromes in cats and dogs. Our second case study examines the effect of the release of industrial wastewater in a community through companion animal surveillance.

Claims

1. A method comprising:

a) obtaining data regarding patient data for a first species and patient data for a second species, the first species patient data comprising reported symptom incidences for a first species, and the second species patient data comprising reported symptom incidences for a second species;

b) performing statistical analysis on the first species patient data and the second species patient data to obtain refined first species patient data and refined second species patient data;

c) causing a display of a visual graphic including a map background and a plurality of color-coded symbols, each symbol representative of a select one of either species or symptom, and each color representative of the other species or symptom, the location of the color-coded symbols having a screen location relative to the map background corresponding to patient location information.

2. The method of claim 1, further comprising d) receiving input identifying a time period, and wherein step c) further comprises cause the display of color-coded symbols such that the color-coded symbols correspond to first species patient data and second species patient data for symptom incidences within the identified time period.

3. The method of claim 1, wherein step d) further comprises displaying an interactive slide control, and wherein a duration of the time period is determined by a user-selected position of the interactive slide control.

4. The method of claim 1, wherein step b) further comprises performing a power transformation on the first species patient data.

5. The method of claim 1, wherein step b) further comprises performing data normalization on the first species patient data.

6. The method of claim 5, wherein step b) further comprises employing a cumulative sum model on the first species patient data.

7. The method of claim 5, wherein step b) further comprises employing quantile measures on the first species patient data.