Making the DAEN information accessible.

Overview

AccessibleAdverseEventNotification

Making the DAEN information accessible.

The purpose of this repository is to make the information on Australian COVID-19 adverse events accessible. The Therapeutics Goods Administration (TGA) keeps a database of adverse reactions to medications including the COVID-19 vaccines. This Database of Adverse Event Notifications (DAEN) is available to the public via this awful web interface. The most recent two weeks is never available.

The DAEN website doesn't provide information in a format that might be useful for analysis. Instead you have to scrape the information by entering each individual day and collecting the results from two tables which might span multiple pages. I've already done that and the code is here (this code isn't great, but it is good enough to get the job done).

Please be aware that the numbers reported in DAEN are probably significantly less than the actual number of adverse events and deaths. As the DAEN website states:

Adverse event reports from consumers and health professionals to the TGA are voluntary, so there is under-reporting by these groups of adverse events related to therapeutic goods in Australia. This is the same around the world.

The scraped data is found in the data directory. These files are tab separated files which you can easily import in to a spreadsheet program. All of the files are only for COVID-19 vaccines.

  • DAEN_webscrape_simple.txt This file shows the date (twice for reasons that made sense at the time, but don't necessarily make sense anymore), the number of cases reported that day, the number of cases with a single suspected medicine for that day, and the number of deaths reported that day.
  • DAEN_webscrape_medsummary.txt This file gives a daily count of each adverse event category. Please note that if one patient had multiple adverse events, then each event would be counted in the appropriate category.
  • DAEN_webscrape_listofreports.txt This file provides the individual reports and includes sex and age (when recorded).

Figure 1 shows some of the basic information such as number of adverse events and deaths reported each day for the COVID-19 vaccines, myocarditis, pericarditis and the more general term cardiac disorder.

Figure 1 Figure 1.

Figure 2 shows a histogram of reported cases of myocarditis and pericarditis from the COVID-19 vaccine. Please note that the age group 10-19 is somewhat distorted as the age 10-11 should not receive the vaccine (although there are cases of 8 year olds getting the vaccine when that should not have occurred). This age group also has a significantly lower uptake than other age groups.

Figure 2 Figure 2.

Figures 3 and 4 plot the reports of myocarditis by age grouped by sex or manufacturer respectively. Figures 5 and 6 are the same for pericarditis. A '-' is used where an age was not given in the report.

Figure 3 Figure 3.

Figure 4 Figure 4.

Figure 5 Figure 5.

Figure 6 Figure 6.

Figure 7 shows how the histogram for myocarditis has progressed over time.

Figure 7
Figure 7.

Figure 8 shows the death rate of people in Australia who contracted COVID-19. Data taken from health.gov on 1/12/2021. Bottom graph is zoomed in to 1% to see what is happening with those under the age of 60.

Figure 8
Figure 8.

This creates a ohlc timeseries from downloaded CSV files from NSE India website and makes a SQLite database for your research.

NSE-timeseries-form-CSV-file-creator-and-SQL-appender- This creates a ohlc timeseries from downloaded CSV files from National Stock Exchange India (NS

PILLAI, Amal 1 Oct 02, 2022
Stock Analysis dashboard Using Streamlit and Python

StDashApp Stock Analysis Dashboard Using Streamlit and Python If you found the content useful and want to support my work, you can buy me a coffee! Th

StreamAlpha 27 Dec 09, 2022
CubingB is a timer/analyzer for speedsolving Rubik's cubes, with smart cube support

CubingB is a timer/analyzer for speedsolving Rubik's cubes (and related puzzles). It focuses on supporting "smart cubes" (i.e. bluetooth cubes) for recording the exact moves of a solve in real time.

Zach Wegner 5 Sep 18, 2022
Pandas and Dask test helper methods with beautiful error messages.

beavis Pandas and Dask test helper methods with beautiful error messages. test helpers These test helper methods are meant to be used in test suites.

Matthew Powers 18 Nov 28, 2022
Codes for the collection and predictive processing of bitcoin from the API of coinmarketcap

Codes for the collection and predictive processing of bitcoin from the API of coinmarketcap

Teo Calvo 5 Apr 26, 2022
DataPrep — The easiest way to prepare data in Python

DataPrep — The easiest way to prepare data in Python

SFU Database Group 1.5k Dec 27, 2022
Shot notebooks resuming the main functions of GeoPandas

Shot notebooks resuming the main functions of GeoPandas, 2 notebooks written as Exercises to apply these functions.

1 Jan 12, 2022
Detailed analysis on fraud claims in insurance companies, gives you information as to why huge loss take place in insurance companies

Insurance-Fraud-Claims Detailed analysis on fraud claims in insurance companies, gives you information as to why huge loss take place in insurance com

1 Jan 27, 2022
Advanced Pandas Vault — Utilities, Functions and Snippets (by @firmai).

PandasVault ⁠— Advanced Pandas Functions and Code Snippets The only Pandas utility package you would ever need. It has no exotic external dependencies

Derek Snow 374 Jan 07, 2023
A set of tools to analyse the output from TraDIS analyses

QuaTradis (Quadram TraDis) A set of tools to analyse the output from TraDIS analyses Contents Introduction Installation Required dependencies Bioconda

Quadram Institute Bioscience 2 Feb 16, 2022
Extract Thailand COVID-19 Cluster data from daily briefing pdf.

Thailand COVID-19 Cluster Data Extraction About Extract Clusters from Thailand Daily COVID-19 briefing PDF Download latest data Here. Data will be upd

Noppakorn Jiravaranun 5 Sep 27, 2021
Python for Data Analysis, 2nd Edition

Python for Data Analysis, 2nd Edition Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media Buy

Wes McKinney 18.6k Jan 08, 2023
Creating a statistical model to predict 10 year treasury yields

Predicting 10-Year Treasury Yields Intitially, I wanted to see if the volatility in the stock market, represented by the VIX index (data source), had

10 Oct 27, 2021
Open-source Laplacian Eigenmaps for dimensionality reduction of large data in python.

Fast Laplacian Eigenmaps in python Open-source Laplacian Eigenmaps for dimensionality reduction of large data in python. Comes with an wrapper for NMS

17 Jul 09, 2022
MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation [ECCV2020]

MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation [ECCV2020] by Kaisiyuan Wang, Qianyi Wu, Linsen Song, Zhuoqian Yang, Wa

112 Dec 28, 2022
Time ranges with python

timeranges Time ranges. Read the Docs Installation pip timeranges is available on pip: pip install timeranges GitHub You can also install the latest v

Micael Jarniac 2 Sep 01, 2022
yt is an open-source, permissively-licensed Python library for analyzing and visualizing volumetric data.

The yt Project yt is an open-source, permissively-licensed Python library for analyzing and visualizing volumetric data. yt supports structured, varia

The yt project 367 Dec 25, 2022
High Dimensional Portfolio Selection with Cardinality Constraints

High-Dimensional Portfolio Selecton with Cardinality Constraints This repo contains code for perform proximal gradient descent to solve sample average

Du Jinhong 2 Mar 22, 2022
A variant of LinUCB bandit algorithm with local differential privacy guarantee

Contents LDP LinUCB Description Model Architecture Dataset Environment Requirements Script Description Script and Sample Code Script Parameters Launch

Weiran Huang 4 Oct 25, 2022
PCAfold is an open-source Python library for generating, analyzing and improving low-dimensional manifolds obtained via Principal Component Analysis (PCA).

PCAfold is an open-source Python library for generating, analyzing and improving low-dimensional manifolds obtained via Principal Component Analysis (PCA).

Burn Research 4 Oct 13, 2022