young-eun-nam / hi-covidnet

Codes and data for the paper "Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea", KDD 2020

Hi-COVIDNet : Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea

About

Source code and datasets of the paper [Hi-COVIDNet : Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea]

Since the data from Korea Telecom(KT) is not open to the public, if you would like to run the code, please contact KT.

Installation

Requirements

• Python 3.6 (Recommend Anaconda)
• Ubuntu 16.04.3 LTS
• Pytorch >= 1.2.0

Usage

• Download all codes (*.py) and put them in the same folder
• Create "model_grid_search" folder in the same folder
• Create "pickled_ds" folder for dataset and mean_std data
• Open terminal in the same folder
• Run "python data_loader.py" to preprocess and save data in ".pkl" format

python data_loader.py -h
usage: data_loader.py [-h] [--output_size O] [--save]

Hi-covidnet DATALOADER

optional arguments:
-h, --help       show this help message and exit
--output_size O  How many days you are predicting(default: 14)
--save           Saving pre-processed data

example usage :

python data_loader.py --output_size 14
data shape is  32 (14, 10)
target_continent shape is  (32, 14, 6) target_total shape is  (32, 14)
Loading KT roaming data
Loading infection ratio data
Loading passenger flights data
Normalizing continent target
Normalizing total target

• Run "python main.py" to train Hi-COVIDNet

python main.py -h
usage: main.py [-h] [--epochs N] [--model_path MODEL_PATH] [--gpu_id GPU_ID]
             [--lr LR] [--beta BETA] [--hidden_size HIDDEN]
             [--output_size OUTPUT] [--is_aux] [--is_tm]

Hi-covidnet

optional arguments:
-h, --help            show this help message and exit
--epochs N            number of epochs to train (default: 100)
--model_path MODEL_PATH
                      prefix of path of the model
--gpu_id GPU_ID       gpu_ids: e.g. 0,1,2,3,4,5
--lr LR               learning rate (default: 0.03)
--beta BETA           ratio of continent loss and total loss (default: 0.5)
--hidden_size HIDDEN  hidden size of LSTM and Transformer(default: 4) e.g.
                      2,4,8, ... depending on your dataset
--output_size OUTPUT  How many days you are predicting
--is_aux              use auxilary data
--is_tm               use transformer

Hyperparameters:

Please check the hyperparameters of Hi-COVIDNet defined in main.py