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🐼 Project PANDA Preventive Analysis for Nationwide Disease Awareness 🚀 Why PANDA Works 🛡️ Preventive A proactive approach to influenza surveillance — moving beyond reaction, PANDA predicts outbreaks before they escalate.
📊 Analysis Built on powerful ML algorithms like XGBoost and enhanced with fuzzy logic, PANDA delivers data-driven insights with accuracy and efficiency.
🌐 Nationwide Designed for scalability across India, PANDA supports multi-region deployment to help authorities respond faster, anywhere in the country.
🦠 Disease Focused Centering on influenza outbreak detection, PANDA keeps health at the core of its mission.
🔔 Awareness Empowering communities, health workers, and officials with timely alerts — PANDA promotes vigilance and “jagrukta” at every level.
🐼 PANDA isn't just a tool. It's a nationwide shield against outbreaks.
We are building a Generalized Influenza Pandemic Warning System that predicts the likelihood of an influenza pandemic in India, with a focus on state-level analysis. Our system will track and predict the spread of various influenza strains, including newly emerging ones, to provide early warnings.
To effectively track and predict influenza activity, we need to gather data from several sources. Here's the essential data we'll use:
- Why: ILI includes common symptoms like fever and cough, applicable to all influenza types (A, B, C) and new strains.
- Where to Get It: India's Integrated Disease Surveillance Programme (IDSP) or state health departments (e.g., Maharashtra health reports).
- What It Helps With: Spotting sudden rises in influenza cases, such as a rapid increase in ILI cases.
- Why: SARI cases represent severe illness requiring hospitalization, often associated with influenza outbreaks.
- Where to Get It: IDSP or state health departments for hospital admission data.
- What It Helps With: Detecting if influenza is causing more severe illness, which could signal a pandemic.
- Why: Influenza spreads more in cold, dry weather or during the monsoon season (e.g., January or August in India).
- Where to Get It: India Meteorological Department (IMD) for temperature, humidity, and rainfall data.
- What It Helps With: Understanding if weather conditions in 2026 could increase the spread of influenza.
- Why: New or more dangerous influenza strains (e.g., mutated H1N1 or new strains) could cause a pandemic.
- Where to Get It: National Centre for Disease Control (NCDC) or global databases like WHO’s FluNet.
- What It Helps With: Monitoring the appearance of new, fast-spreading influenza strains.
- Why: A surge in influenza cases could overwhelm hospitals, turning an outbreak into a full-scale crisis.
- Where to Get It: State health departments or IDSP for hospital bed and ICU data.
- What It Helps With: Evaluating if hospitals are prepared for a potential surge in cases.
- Why: High population density and frequent travel (e.g., during festivals like Diwali) increase the speed at which influenza spreads.
- Where to Get It: Census of India for population data, and Indian Railways for travel trends.
- What It Helps With: Identifying high-risk states, like Maharashtra, with large populations and frequent movement.
- Why: Vaccination of pigs can reduce the chances of zoonotic influenza strains affecting humans.
- Where to Get It: Local veterinary health data and government reports.
- What It Helps With: Understanding the level of protection against zoonotic strains.
We will combine all this data to build a warning system capable of predicting if a pandemic is likely in 2026. Here's how it works:
- Tracking ILI: Weekly ILI data will be monitored to track overall influenza activity.
- Rapid Increases: We’ll look for rapid increases in ILI cases (e.g., cases doubling in a few weeks).
- Severe Illness: We’ll track SARI data to identify any severe illness spikes.
- New Strains: We will monitor NCDC for reports of new influenza strains.
- Weather Impact: We'll check the weather to see if conditions in 2026 could increase influenza spread.
- Healthcare Capacity: Hospital and ICU data will help us assess the ability of healthcare systems to handle a surge.
- State-Level Risk Assessment: We'll evaluate high-risk states, considering population density and travel patterns.
If we see ILI cases in Maharashtra jump from 1,000 to 3,000 in three weeks during January 2026, the weather is cold and dry, hospitals are nearly full, and NCDC reports a new influenza strain, our system will issue an early pandemic warning.
- State Cases to Population Ratio: We will use this ratio to determine the relative risk of influenza outbreaks across states.
- Allocated Influenza Cases: The system will predict allocated influenza cases across different states to forecast potential outbreaks.
- Integrated Data Sources: The system will automatically integrate data from various sources for a holistic view of the influenza threat.
Let's build a robust system to track and predict influenza pandemics with real-time data, keeping India prepared for any unexpected outbreak!
Note: Many datasets were not available, so we had to make do with whatever we could access, ensuring the system works with the best data at hand.