1. One Health Early Warning Alert
Promising Research on Improving Biosurveillance Capabilities

2. ”If anything kills over ten million people….”

3. Infected
Time

5. Can Hidden Signals Be Detected

6. Noise/Signal Problem Noise/Signal Problem N N N N N N N S N N N N N N

7. NAUSEA + FEVER + VOMITING
Noise OR Signal
NAUSEA
NAUSEA + FEVER + VOMITING
NAUSEA + FEVER

8. Depends……
Frequency
Severity
Proximity
Importance

9. Some Promising Research

10. Statistical Analysis
Daily analyses of hospital emergency department visits. Four of the five strongest signals were likely local precursors to NYC citywide outbreaks due to rotavirus, norovirus, and influenza.

11. Web-Based Information
A vast amount of real-time information about infectious disease outbreaks is found in various forms of Web-based data streams.

12. Social Media
Google Flu Trends claimed to detect regional outbreaks of influenza 7–10 days before conventional Centers for Disease Control and Prevention surveillance systems
Question of Relative Value to End User
Ability to Create Alerts-Questionable.

13. Local Solutions
The New York City Department of Health and Mental Hygiene has established a syndromic surveillance system that routinely collected chief complaint information that is transmitted electronically to the health department daily and analyzed for temporal and spatial aberrations. Respiratory, fever, diarrhea, and vomiting are the key syndromes analyzed. Statistically significant aberrations or “signals” are investigated to determine their public health importance.
One Health Alert System in North Carolina Seeks to Validate Promising Research with a predictive analytics approach.

14. Taking A One Health Approach:
The One Health Alert System (OHAS)
Adding Zoonoses to the Equation
Examining Environmental Impacts on Health

15. Symptoms of Significance
Fever > 100.4
Respiratory/Shortness of Breath
Coughing/Cold Symptoms
Headache
Rash With Fever
Stomach Ache/Abdominal Pain
Stomach Ache/Abdominal Pain-Vomiting
Stomach Ache/Abdominal Pain-Diarrhea
Stomach Ache/Abdominal Pain-Nausea
Influenza like Illness (ILI)

16. OHAS Methodology and Approach
Tactical Information Approach
Adding Predictive Analytic Capability
Developing Capability to Predict Both Severity and Duration

17. Using Predictive Analytics

18. Better Alerts, Less False Positives
Stratifying Symptoms to Allow Multiple Monitoring Fields
Verify Alert Levels
Identify Abnormal Activity that Might First Appear Normal

19. OHAS Roadmap 2
Real-time Data Aggregated and Distributed in Defined Geographical Area
Data Is Analyzed and Used to Predict Future Outbreaks

20. Scaling Challenges EHR and Wearables-Opportunities and Obstacles
Scaling Is Relatively Easy and Inexpensive Using OHAS Wireframe
Need Cities and/or Counties with Robust Surveillance Systems in Place

21. OHAS Use in Practice
Importance of Accessible Dashboard

22. How Will OHAS Impact Be Measured
How fast can we detect an outbreak?
Does it integrate animal and environmental public health data?
Can we prevent an outbreak from becoming an epidemic?

23. Game Changer
Using Predictive Analytics, OHAS Optimize Machine Learning to Identify Anomalies Based on Longitudinal Factors Associated with Symptoms

24. National Expansion? Possibility of Pilots
Need for a Supportive Funding Stream
Evaluation Component

25. Proof Concept
Triggers
Signals
Targets

26. Human Vector
Animal Vector
Environmental Vector
Data Sources, Strategies and Opportunities for Expansion of Current Research

27. William F. Pilkington, M.A., M.P.A., D.P.A.