Enhanced COVID-19 data for improved prediction of survival
Supplementary files
This page contains data and code used in our paper: Wenhuan Zeng, Anupam Gautam and Daniel H. Huson (2021) On the Application of Advanced Machine Learning Methods to Analyze Enhanced, Multimodal Data from Persons Infected with COVID-19, Computation 9(4)
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Data:
Initial COVID-19 dataset 183 Cases: | Initial.xlsx |
Country-wise base and weighted average polarity score: | Polarity.csv |
Past 14 day weather for each subject: | WeatherInfoTotal.csv |
Weather, numerical values: | WeatherEmbeddingfile.xlsx |
Enhanced COVID-19 dataset: | EnhancedCOVID_19Dataset.csv |
Data downloaded from WHO website: | CSV_as_at_09_April_2020-Full_database.xlsx |
Processed WHO publication data: | WHOJournalFormatted.txt |
Country-wise mortality: | detail.xlsx |
Code:
Python script for parsing WHO database: | PublicationListProcessing.py |
Usage: | PublicationListProcessing.py -i InputFileName -o OutputFileName -t NumberOfThreads |
Input file: | CSV_as_at_09_April_2020-Full_database.xlsx (Downloaded from WHO database) |
Output file: | Text file with DOI and Institute Names |
Required Python modules: | BeautifulSoup, pandas, requests, multiprocessing, argparse |
Python script for crawling climate information on weather underground weather: | |
Usage: | CrawlWeather.py --input InputFileName --browser BrowserDriverName --output OutputFileName |
Input file: | Initial.xlsx |
Output file: | CSV file with Sample ID and Climate Information, each sample with 15 records |
Required Python modules: | BeautifulSoup, pandas, NumPy, selenium, argparse |