HealthGuard AI

How Our Project Will Contribute To The Growth Of The Decentralized AI Platform

HealthGuard AI aligns with the BGI mission by harnessing AI to deliver personalized care and real-time epidemic surveillance. It enhances early disease detection, optimizes treatment strategies, and enables proactive outbreak responses, ultimately reducing health disparities and advancing global public health.

Our Team

1. Samson Ajulor – Seasoned software developer with 6+ years of expertise.

2. Adekunly Adewale – Skilled AI Engineer driving our predictive analytics.

3. Victory Chizoba – Proficient full-stack developer ensuring robust applications.

4. Dolapo Oshikoya – Experienced radiotherapist contributing clinical insights.

5. Adejumoke Onipede – Dedicated healthcare practitioner focusing on patient care.

6. Biliqis Onikoyi: Blockchain engineer.

7. Samuel Dahunsi: Frontend developer

8. Joan Nobei: UI/UX designer

The core problem we are aiming to solve

Current public health systems lack real-time surveillance tools to detect and respond to outbreaks quickly. Disease transmission in high-traffic areas often goes unnoticed until cases escalate. The challenge is to identify outbreaks early, predict their spread, and deploy resources efficiently.

Our specific solution to this problem

HealthGuard AI enhances epidemic surveillance by leveraging internet scraping, open-source sentiment analysis, and AI-driven predictive analytics to track and forecast disease outbreaks. The system aligns with key stages of disease monitoring used by health organizations:

1. Early Detection & Sentiment Analysis – Uses web scraping and NLP-based anomaly detection to analyze social media, news reports, and online search trends. By identifying spikes in symptom-related discussions and public concerns, the system detects early warning signals of potential outbreaks.

2. Risk Assessment & Heat Mapping – AI processes open-source epidemiological reports, travel patterns, and demographic data to create dynamic risk heat maps. These continuously updated visual overlays help identify high-risk zones, enabling resource allocation before outbreaks escalate.

3. Situational Awareness & Predictive Modeling – AI-driven trend analysis integrates historical outbreak patterns with real-time web data to forecast potential disease spread. This predictive modeling helps health organizations prepare interventions in advance.

4. Public Health Response Support – By flagging emerging threats based on data trends, HealthGuard AI provides actionable insights for proactive decision-making. Authorities can use these insights to issue alerts, implement targeted containment measures, and mobilize resources efficiently.

Project details

Executive Summary

HealthGuard AI is an advanced, open-source solution leveraging AI-driven epidemic surveillance and predictive analytics to protect lives. By utilizing web scraping, sentiment analysis, and real-time geospatial modeling, our platform detects potential outbreaks early—especially in high-mobility environments like transit hubs. AI-powered anomaly detection and risk heat maps empower health authorities with actionable, data-driven insights, fostering proactive intervention to prevent epidemics and safeguard communities worldwide.

Project Overview

Imagine a world where every airport, train station, or public gathering space acts as a frontline defense against disease outbreaks. HealthGuard AI transforms open-source data streams, digital health trends, and AI-driven analytics into a real-time, predictive epidemic surveillance system. Our mission extends beyond technology—it is about preserving global health security through data intelligence, ensuring timely public health responses and equitable healthcare solutions.

Concept & Technology

Stages of Disease Monitoring (Utilized in HealthGuard AI)

Health organizations typically operate in the following disease monitoring stages, which HealthGuard AI aligns with:

1. Detection & Early Warning – AI-driven sentiment analysis and web scraping detect abnormal increases in health-related discussions, symptoms, and search trends.

2. Risk Assessment & Situational Awareness – AI maps digital indicators (social media, web trends) onto geospatial risk models, identifying potential hotspots.

3. Containment & Response Planning – AI forecasts outbreak patterns based on past epidemiological data, guiding resource allocation and intervention efforts.

4. Ongoing Surveillance & Post-Epidemic Monitoring – Machine learning models continuously adapt, providing long-term disease surveillance and trend analysis.

Technology Stack

Data Acquisition & Ingestion

• Sources: Web scraping, social media APIs (Twitter, Reddit, Google Trends), WHO & CDC open data, and geospatial datasets.

• Processing: Apache Kafka for high-throughput ingestion, ensuring real-time data collection and streaming analytics.

AI-Driven Threat Analysis: Metrics & Scale for Threat Levels

HealthGuard AI uses a multi-scale threat classification system to assess and rank outbreak risks:

Geospatial Analytics & Risk Visualization

• Dynamic heat maps built with GIS platforms, React, and D3.js.

• Integrated mobility & demographic data to pinpoint high-risk zones.

• Time-series forecasting models (LSTMs, ARIMA) for predictive outbreak trends.

Data Security, Privacy and Ethical Considerations)

• Open-source governance ensures transparency and accountability.

• No direct biometric surveillance – AI relies on public data streams, digital indicators, and open reports to maintain privacy compliance.

• Data encryption & anonymization protocols ensure individual privacy protection.

Deployment, Open-Source Compliance & Ethical Considerations

Deployment & Scalability

• Microservices architecture containerized with Docker & Kubernetes, ensuring elastic scaling in cloud environments.

• Automated model updates using federated learning techniques, allowing continuous AI training without centralized data collection.

Open-Source Licensing & Compliance

• Licensed under Apache License 2.0, allowing free access, modification, and distribution.

• Proprietary data exclusions ensure legal clarity on sensitive datasets.

• GDPR & HIPAA-compliant privacy framework, ensuring no personally identifiable data is used or stored.

Q&A Recap & Additional Insights

HealthGuard AI addresses critical gaps in epidemic preparedness by offering an early-warning AI system that provides:

• Real-time alerts based on AI-driven sentiment and search trend analysis.

• Scalable outbreak forecasting for health organizations.

• Geospatial visualization to identify high-risk areas before outbreaks escalate.

Measuring Success: Key Performance Indicators (KPIs)

Conclusion

HealthGuard AI is a first-of-its-kind, AI-driven epidemic surveillance system that operates within open-source, privacy-compliant frameworks. By leveraging real-time digital health signals, sentiment analysis, and AI-driven anomaly detection, it enables health organizations worldwide to predict, prepare for, and prevent outbreaks—before they escalate into crises.

Our approach is not just about data and AI—it is about protecting lives, ensuring global health security, and enabling a future where technology-driven public health solutions become the standard.

Existing resources

1. Google Trends – Tracks public search interest related to health symptoms and disease outbreaks. https://trends.google.com/trends/

2. Twitter API (Limited Free Access) – Allows monitoring of health-related discussions and potential outbreak signals. https://developer.twitter.com/en/docs/twitter-api

3. Reddit API – Provides access to health-related discussions, sentiment analysis, and anomaly detection. https://www.reddit.com/dev/api/

4. World Health Organization (WHO) Open Data – https://www.who.int/data

5. Johns Hopkins COVID-19 Data Repository – https://github.com/CSSEGISandData/COVID-19

6. Our World in Data – https://ourworldindata.org/

7. HealthMap – https://www.healthmap.org/

8. GeoJSON & OpenStreetMap (OSM) – https://www.openstreetmap.org/

Open Source Licensing

We propose releasing HealthGuard AI under the Apache License 2.0. This license promotes open collaboration while protecting contributors with clear patent rights and liability limitations. It allows commercial and non-commercial use, modification, and distribution of the code. Any proprietary components—such as certain datasets or third-party libraries that require separate licensing—will be explicitly listed and excluded from this open-source license.

Was there any event, initiative or publication that motivated you to register/submit this proposal?

Describe the particulars.

COVID-19 Real Time Live Tweet Sentimental Analysis Using Deep Learning Methods, Its Effect on Phases of COVID-19 Waves