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

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eladcn/coronavirus_prediction

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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

  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

Terminal output

Cases in each day graph

Cases in each day graph

Deaths in each day graph

Deaths in each day graph

Predictions

Date Cases Deaths Predicted Cases Predicted Deaths Notes
13.05.2020 4,425,655 297,765 4,400,686 305,157
12.05.2020 4,337,602 292,451 4,315,787 298,811
11.05.2020 4,252,290 287,137 4,218,443 294,230
10.05.2020 4,178,154 283,734 4,132,764 290,529
09.05.2020 4,098,288 280,224 4,045,221 285,381
08.05.2020 4,009,291 275,976 3,961,661 279,957
07.05.2020 3,913,644 270,426 3,875,276 274,533
06.05.2020 3,817,382 264,837 3,790,035 266,889
05.05.2020 3,724,518 258,027 3,707,471 263,849
04.05.2020 3,643,271 252,241 3,616,918 256,777
02.05.2020 3,481,429 244,665 3,463,870 246,550
01.05.2020 3,398,473 239,448 3,365,306 241,043
30.04.2020 3,304,220 233,830 3,290,765 234,227
29.04.2020 3,218,184 228,030 3,205,781 224,419
28.04.2020 3,136,508 217,813 3,129,694 219,813
21.04.2020 2,556,720 177,675 2,553,102 174,137
20.04.2020 2,480,503 170,397 2,476,492 169,819
19.04.2020 2,406,575 165,031 2,396,306 163,820
18.04.2020 2,330,766 160,047 2,313,171 152,571
17.04.2020 2,248,863 154,145 2,247,654 148,412 Both models are now using a neural network architecture.
16.04.2020 2,181,334 145,471 2,153,255 138,052
15.04.2020 2,086,332 138,475 2,068,918 131,251 The cases were predicted using a neural network.
14.04.2020 2,001,681 130,379 1,990,227 125,759
13.04.2020 1,923,937 119,618 1,927,047 121,115
12.04.2020 1,852,365 114,196 1,857,691 115,824
11.04.2020 1,779,842 108,779 1,779,990 109,470 Changed the cases model's polynomial degree.
10.04.2020 1,698,881 102,687 1,662,881 102,308
09.04.2020 1,603,694 95,693 1,585,772 95,185
08.04.2020 1,518,023 88,457 1,508,529 88,618
07.04.2020 1,430,981 82,036 1,436,648 82,061
06.04.2020 1,346,036 74,654 1,369,235 77,013
05.04.2020 1,272,901 69,427 1,309,632 72,120 Changed the cases model's polynomial degree.
04.04.2020 1,201,483 64,691 1,199,951 65,524
03.04.2020 1,116,662 59,162 1,101,907 58,909 Restored the deaths model's polynomial degree.
02.04.2020 1,015,065 53,167 1,023,706 52,199 Changed the models' poloynomials degrees.
01.04.2020 935,197 47,192 965,240 47,590
31.03.2020 858,355 42,309 885,894 42,849
30.03.2020 784,794 37,788 810,279 38,570
29.03.2020 722,359 33,966 734,545 34,636
28.03.2020 663,124 30,862 657,417 30,684
27.03.2020 596,366 27,344 585,678 27,119
26.03.2020 531,865 24,073 521,527 23,997
25.03.2020 471,035 21,284 466,864 21,207
24.03.2020 422,599 18,894 417,312 18,651
23.03.2020 378,860 16,514 372,656 16,494
22.03.2020 337,469 14,647 334,355 14,544
21.03.2020 305,036 13,013 298,845 12,760
20.03.2020 275,598 11,387 265,940 11,271
19.03.2020 244,933 10,031 239,006 10,027
18.03.2020 218,822 8,951 217,680 8,934
17.03.2020 198,234 7,978 200,305 8,009
16.03.2020 182,473 7,160 184,930 7,223
15.03.2020 169,577 6,519 170,336 6,504
14.03.2020 156,622 5,833 157,620 5,966
13.03.2020 145,483 5,429 146,450 5,434
12.03.2020 134,577 4,982 137,917 4,984
27.02.2020 83,113 2,858 - 2,867
26.02.2020 81,828 2,801 - 2,839
25.02.2020 80,828 2,763 - 2,775
24.02.2020 80,087 2,699 - 2,679 The cases model needs to be changed.
23.02.2020 79,205 2,618 79,611 2,548
22.02.2020 78,651 2,460 80,423 2,458 Changed the cases model polynomial degree.
21.02.2020 77,673 2,360 75,162 2,355
20.02.2020 76,667 2,247 76,109 2,248 The data for previous days was changed in this day.
19.02.2020 75,700 2,126 77,427 2,138
18.02.2020 75,184 2,009 77,842 2,030 Changed the polynomials degrees.
17.02.2020 73,332 1,873 79,231 1,956
16.02.2020 71,329 1,775 76,943 1,824
15.02.2020 69,197 1,669 73,331 1,668
14.02.2020 67,100 1,526 67,496 1,516
13.02.2020 64,438 1,383 58,692 1,376
12.02.2020 45,134 1,261 48,123 1,233

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

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