This repository contains:
- the code to analyze the demographic patterns and their relationships with COVID, for this blog on Medium.com.
- the code to estimate the COVID trend using a machine learning-enhanced SIRD model, for this blog on Medium.com.
- the API to predict future COVID trend.
.
├── covid_api
│ ├── web
│ │ ├── data
│ │ │ ├── data_pred.pkl
│ │ │ ├── dict_state_params.pkl
│ │ │ ├── scaler.pkl
│ │ │ └── model_new.h5
│ │ ├── app.py
│ │ ├── Dockerfile
│ │ └── requirements.txt
│ └── docker-compose.yml
├── COVID_predict.ipynb
├── LICENSE
└── README.md
- Demographic Data: https://www.census.gov/data/tables/time-series/demo/popest/2010s-state-detail.html from United States Census Bureau;
- Mobility Data: https://www.bts.gov/browse-statistical-products-and-data/trips-distance/daily-travel-during-covid-19-pandemic from United States Bureau of Transportation Statistics;
- COVID Public Surveillance: https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days from United States Center of Disease Control and Prevention;
- COVID Daily Record: https://coronavirus.jhu.edu/data from John Hopkins University Coronavirus Resource Center.
The API is created as a Flask app and deployed using Docker.
It takes a request as input in the format as below:
{
"state": "New York", # State to be predicted
"duration": "30" # Prediction duration
}Then, following the computation as defined in this blog, the API returns the predicted COVID trend of the given state for the requested future duration in the format as below:
{
"Status Code": 202,
"result": [
391733.0,
393394.0,
395447.0,
397035.0,
398490.0,
399787.0,
400748.0,
402126.0,
403514.0,
405320.0,
406574.0,
407670.0,
408808.0,
409702.0,
410925.0,
412051.0,
412992.0,
413827.0,
414818.0,
416141.0,
416776.0,
417665.0,
418466.0,
419163.0,
419759.0,
420490.0,
421341.0,
421767.0,
422566.0,
423275.0,
424200.0
}- Collect more data.
- Retrain the model given more data.
- Finer hyper-parameter tuning.
- In-depth analysis of mobility versus COVID