Coronavirus (COVID-19) Prediction

This project aims to predict the numbers that are published in each day regarding the amount of Coronavirus (COVID-19) cases and deaths.

Table of contents

• Requirements
• How to use
• How does it work
• Contact info
• Adding models
• Output examples
  • Terminal output
  • Cases in each day graph
  • Deaths in each day graph
• Predictions

Requirements

1. A machine with Python 3 installed.
2. The following packages are needed to be installed for this project to run:
   • numpy
   • matplotlib
   • scikit-learn
   • BeautifulSoup

How to use

(Using Poetry)

1. Install Poetry
2. Run poetry install
3. Run poetry run python main.py

(Not using Poetry)

1. Install dependencies with pip install -r requirements.txt
2. Run python main.py

How does it work

The main.py file uses the DataGrabber class (source included) to fetch the required data from https://www.worldometers.info.
The main.py file then trains 2 polynomial models using the fetched data and scikit-learn's LinearRegression - the cases in each day model and then the deaths in each day model.
Afterwards, the file displays the models' predictions for the next day, the functions that depict the trained models and displays a graph for each model.

Contact info

You may contact me via Linkedin: https://www.linkedin.com/in/eladcn/.

Adding models

It is possible to add more models (e.g. models for specific countries) to the project by taking the following steps:

1. Create a new DataGrabber class (you can name it however you want, for example: USADeathsDataGrabber), it doesn't have to inherit from class DataGrabber.
2. Implement the following method in the new class you created: def get_dataset_file_name(self, dataset_date) This method should return the dataset file name for a specific given date. For example, it may return: "USA_" + dataset_date + ".csv". You can find a good example for this in the CasesDataGrabber class.
3. (Optional) If you can fetch the data from an external source (instead of managing the dataset manually) and you would like to implement this feature, implement the following method in the new class you created: def grab_data(self) This method should fetch the data from a data source and store it into a file (which is the same format as mentioned in section 2).
4. Add the following configuration to the models array inside the config.json file:

        {
            "enabled": true, // Whether the model is enabled and the program should handle it or not.
            "model_name": "USA Deaths", // This is only used for display reasons and will not affect any logic.
            "model": {
                "type": "regression", // The model type, could be either "regression" or "neural_net".
                "polynomial_degree": 6 // A hyper parameter for regression models. See the config file for a neural network example.
            },
            "datagrabber_class": "USADeathsDataGrabber", // The class we would like to use.
            "grab_data_from_server": true, // Set this to false if you would like to manage the dataset manually (i.e not use the grab_data(self) method).
            "offline_dataset_date": "2020-04-10", // If the dataset is managed manually - specify the date of the offline dataset file.
            "days_to_predict": 10 // How many days ahead you would like to predict.
        }

Please note that the data should be formatted in a CSV file with the following structure:

0,value_for_day_0
1,value_for_day_1
2,value_for_day_2
.
.
.
n,value_for_day_n

Output examples

Terminal output

Cases in each day graph

Deaths in each day graph

Predictions