Mosquito Bite Symptom and Risk Estimator

Overview

This web application estimates the probability of contracting diseases transmitted by mosquitoes based on reported symptoms and geolocation. A heat map highlights the risk of disease based on data from the past 14 days in the user's area.

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Features

• Symptom Analysis: Users can report symptoms, which the app correlates with potential mosquito-borne diseases.
• Disease Risk Mapping: A heat map shows the probability of disease transmission in the user's region based on recent data.
• API Integration: Utilizes the Peruvian Ministry of Health (MINSA) API for up-to-date information.
• Data Visualization: Embeds interactive content for a comprehensive user experience.
• Image Upload and Management: Users can upload pictures of mosquito bites for analysis and delete uploaded images as needed.

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Technologies Used

• Frontend: Vue.js
• Backend: Flask, Python
• Database: SQLAlchemy
• External APIs: MINSA API

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Database Schema

Table: ENFERMEDADES

Column	Type	Description
id	Integer	Primary key (auto-increment)
fiebre_repentina	Integer	Sudden fever severity
dolor_de_cabeza	Integer	Headache severity
hemorragia_bucal	Integer	Mouth bleeding severity
hemorragia_nasal	Integer	Nasal bleeding severity
dolor_muscular	Integer	Muscle pain severity
dolor_en_las_articulaciones	Integer	Joint pain severity
vomitos	Integer	Vomiting severity
Fatiga	Integer	Fatigue severity
Ojos_rojos	Integer	Red-eye severity

Example Model Definition

class ENFERMEDADES(db.Model):
    __tablename__ = 'enfermedades'

    id = db.Column(db.Integer, primary_key=True, autoincrement=True)
    fiebre_repentina = db.Column(db.Integer)
    dolor_de_cabeza = db.Column(db.Integer)
    hemorragia_bucal = db.Column(db.Integer)
    hemorragia_nasal = db.Column(db.Integer)
    dolor_muscular = db.Column(db.Integer)
    dolor_en_las_articulaciones = db.Column(db.Integer)
    vomitos = db.Column(db.Integer)
    Fatiga = db.Column(db.Integer)
    Ojos_rojos = db.Column(db.Integer)

    def __repr__(self):
        return f'<ENFERMEDADES {self.id}>'

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API Endpoints

/api/data

• GET: Retrieves all symptom data from the database.
• POST: Submits new symptom data for analysis.

/api/resultados

• GET: Returns general results and recommendations.

/api/upload

• POST: Accepts image files for analysis and uploads them to the server.

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Image Upload and Management

The app includes methods to upload and manage images of mosquito bites:

uploadImages(event)

• Purpose: Handles uploading of images selected by the user.
• Process:
  1. Access Selected Files: Retrieves files uploaded via the <input type="file"> element.
  2. Iterate Through Files: Loops through the selected files to process each one individually.
  3. Read File as Data URL: Uses FileReader to convert the file into a base64-encoded URL (data URL).
  4. Store Image Data: Pushes the converted URL into the imageUrls array for display or further processing.

deleteImage(index)

• Purpose: Removes an uploaded image from the display list.
• Process:
  1. Identify Image: Receives the index of the image to be removed.
  2. Update Array: Uses splice to remove the image from the imageUrls array.

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Dataset

The project utilizes the Insect Bite Identifier dataset, hosted on Roboflow. It contains annotated images of various insect bites, designed to train computer vision models for detection and classification of insect bites. This dataset supports formats for object detection models and offers a robust foundation for image-based predictions.

Dataset Link: Roboflow - Insect Bite Dataset

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Setup and Installation

1. Clone the repository:

   git clone <repository_url>

2. Navigate to the project directory:

   cd <project_directory>

3. Install dependencies:

   pip install -r requirements.txt
   npm install

4. Start the backend server:

   python app.py

5. Start the frontend server:

   npm run serve

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External Resources

• Dengue Map
• Weekly Situation Room
• Mosquitos db

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Set

1. Fork the repository.
2. Create a new feature branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add a descriptive message"

4. Push to the branch:

   git push origin feature-name

5. Open a pull request.

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