This project contains an application source code to predict daily cases of COVID-19 using Long Short-Term Memory network models. Basically, the package has a main module (core) and submodules (lstm_covid and forecasting) with functions to tuning models and predict daily cases using time series data of COVID pandemic.
- Support data files of Comma Separated Values (CSV) with collums int this order (Region, COD, Date, Daily Cases)
Dependencies
Python 3.7.X, Numpy, Pandas, Keras, Scikit-Learn and Matplotlib
Setup Conda Environment
With Conda installed [1], run:
$ git clone https://github.com/marcosmlr/lstm_covid.git $ cd lstm_covid $ make install $ source activate lstm
| [1] | If you are using a git server inside a private network and are using a self-signed certificate or a certificate over an IP address ; you may also simply use the git global config to disable the ssl checks: git config --global http.sslverify "false" |
Source codes modules:
- core.py - the main program enables to create and validate LSTM models using required and default parameters or extra_args
- See core.py --help for command line details
- lstm_covid.py - functions necessary to create, tuning and validate models
- forecasting.py - program to forecasting daily cases of COVID-19 using tuned models using required and default parameters or extra_args
- See forecasting.py --help for command line details
This version of the application requires the input files to be in the Comma Separated Values (CSV) format.
This software is preliminary or provisional and is subject to revision. It is being provided to meet the need for timely best science. The software has not received final approval from the National Institute for Space Research (INPE). No warranty, expressed or implied, is made by the INPE or the Brazil Government as to the functionality of the software and related material nor shall the fact of release constitute any such warranty. The software is provided on the condition that neither the INPE nor the Brazil Government shall be held liable for any damages resulting from the authorized or unauthorized use of the software.
MIT License
Copyright (c) 2020 Luis Ricardo and Marcos Rodrigues
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
lstm_covid was written by Luis Ricardo and Marcos Rodrigues.