Algorithmen der Bioinformatik

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)

.

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:

CrawlWeather.py

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

 

Privacy settings

Our website uses cookies. Some of them are mandatory, while others allow us to improve your user experience on our website. The settings you have made can be edited at any time.

or

Essential

in2code

Videos

in2code
YouTube
Google