About SMART HEALTH AI

SMART HEALTH AI is a system to help predict malaria-prone regions as early as possible based on the mosquito species and genus found in an area. [Image of malaria transmission cycle] It analyzes environmental features and reported clinical cases to connect community health personnel and doctors for faster response.

Tech: Flask, TensorFlow, Pandas, NumPy, PyTorch, Scikit-learn

Details GitHub Vercel

Malaria Surveillance Dashboard

Upload trap counts, report mosquito species, or simulate predictions. This front-end is a static prototype — integrate with a Flask API for real predictions.

Regions monitored
128
Active alerts (7d)
18
Community reporters
432

Map (placeholder)

Report mosquito sighting

Upload dataset (CSV)

Upload trap summaries or recent case counts to analyze trends. CSV should contain columns like date,location,species,count,cases.


      


      

        
How it works

        
    
          
  1. Collect mosquito trap data and identify species/genus using field kits or image models. 
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  2. Combine entomological data with environmental and clinical case reports.
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  3. Use machine learning models (ensemble of TensorFlow / PyTorch models) to score malaria risk per region.
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  4. Send alerts and connect community health workers and clinicians for targeted interventions.
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