One Health Early Warning Alert Promising Research on Improving Biosurveillance Capabilities If anything kills over ten million people Infected Time Can Hidden Signals Be Detected NoiseSignal Problem ID: 762424
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One Health Early Warning Alert Promising Research on Improving Biosurveillance Capabilities
”If anything kills over ten million people….”
Infected Time
Can Hidden Signals Be Detected
Noise/Signal Problem NSN N N N N N N N N N Noise/Signal Problem N N N N N
Noise OR Signal NAUSEANAUSEA + FEVERNAUSEA + FEVER + VOMITING
Depends……FrequencySeverityProximityImportance
Some Promising Research
D aily 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 . Statistical Analysis
A vast amount of real-time information about infectious disease outbreaks is found in various forms of Web-based data streams . Web-Based Information
Social Media Ability to Create Alerts-Questionable. 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
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 . Local Solutions
Adding Zoonoses to the Equation Examining Environmental Impacts on Health Taking A One Health Approach: The One Health Alert System (OHAS)
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)
OHAS Methodology and Approach
Using Predictive Analytics
Better Alerts, Less False Positives Stratifying Symptoms to Allow Multiple Monitoring FieldsVerify Alert Levels Identify Abnormal Activity that Might First Appear Normal
OHAS Roadmap Real-time Data Aggregated and Distributed in Defined Geographical Area 2 Data Is Analyzed and Used to Predict Future Outbreaks
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
OHAS Use in Practice Importance of Accessible Dashboard
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?
Game Changer Using Predictive Analytics, OHAS Optimize Machine Learning to Identify Anomalies Based on Longitudinal Factors Associated with Symptoms
National Expansion? Possibility of Pilots Need for a Supportive Funding Stream Evaluation Component
Proof Concept
Data Sources, Strategies and Opportunities for Expansion of Current Research
William F. Pilkington, M.A., M.P.A., D.P.A.william.pilkington@cabarrushealth.org