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.

Modular analysis tools for neurophysiology data

Neuroanalysis Modular and interactive tools for analysis of neurophysiology data, with emphasis on patch-clamp electrophysiology. Functions for runnin

Allen Institute 5 Dec 22, 2021
PyEmits, a python package for easy manipulation in time-series data.

PyEmits, a python package for easy manipulation in time-series data. Time-series data is very common in real life. Engineering FSI industry (Financial

Thompson 5 Sep 23, 2022
Create HTML profiling reports from pandas DataFrame objects

Pandas Profiling Documentation | Slack | Stack Overflow Generates profile reports from a pandas DataFrame. The pandas df.describe() function is great

10k Jan 01, 2023
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
Show you how to integrate Zeppelin with Airflow

Introduction This repository is to show you how to integrate Zeppelin with Airflow. The philosophy behind the ingtegration is to make the transition f

Jeff Zhang 11 Dec 30, 2022
CPSPEC is an astrophysical data reduction software for timing

CPSPEC manual Introduction CPSPEC is an astrophysical data reduction software for timing. Various timing properties, such as power spectra and cross s

Tenyo Kawamura 1 Oct 20, 2021
A Python adaption of Augur to prioritize cell types in perturbation analysis.

A Python adaption of Augur to prioritize cell types in perturbation analysis.

Theis Lab 2 Mar 29, 2022
TheMachineScraper 🐱‍👤 is an Information Grabber built for Machine Analysis

TheMachineScraper 🐱‍👤 is a tool made purely for analysing machine data for any reason.

doop 5 Dec 01, 2022
Deep universal probabilistic programming with Python and PyTorch

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

7.7k Dec 30, 2022
signac-flow - manage workflows with signac

signac-flow - manage workflows with signac The signac framework helps users manage and scale file-based workflows, facilitating data reuse, sharing, a

Glotzer Group 44 Oct 14, 2022
Open-Domain Question-Answering for COVID-19 and Other Emergent Domains

Open-Domain Question-Answering for COVID-19 and Other Emergent Domains This repository contains the source code for an end-to-end open-domain question

7 Sep 27, 2022
Building house price data pipelines with Apache Beam and Spark on GCP

This project contains the process from building a web crawler to extract the raw data of house price to create ETL pipelines using Google Could Platform services.

1 Nov 22, 2021
Detecting Underwater Objects (DUO)

Underwater object detection for robot picking has attracted a lot of interest. However, it is still an unsolved problem due to several challenges. We take steps towards making it more realistic by ad

27 Dec 12, 2022
Random dataframe and database table generator

Random database/dataframe generator Authored and maintained by Dr. Tirthajyoti Sarkar, Fremont, USA Introduction Often, beginners in SQL or data scien

Tirthajyoti Sarkar 249 Jan 08, 2023
Single-Cell Analysis in Python. Scales to >1M cells.

Scanpy – Single-Cell Analysis in Python Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with anndata. It inc

Theis Lab 1.4k Jan 05, 2023
A Python module for clustering creators of social media content into networks

sm_content_clustering A Python module for clustering creators of social media content into networks. Currently supports identifying potential networks

72 Dec 30, 2022
A real data analysis and modeling project - restaurant inspections

A real data analysis and modeling project - restaurant inspections Jafar Pourbemany 9/27/2021 This project represents data analysis and modeling of re

Jafar Pourbemany 2 Aug 21, 2022
Includes all files needed to satisfy hw02 requirements

HW 02 Data Sets Mean Scale Score for Asian and Hispanic Students, Grades 3 - 8 This dataset provides insights into the New York City education system

7 Oct 28, 2021
Ejercicios Panda usando Pandas

Readme Below we add configuration details to locally test your application To co

1 Jan 22, 2022
Conduits - A Declarative Pipelining Tool For Pandas

Conduits - A Declarative Pipelining Tool For Pandas Traditional tools for declaring pipelines in Python suck. They are mostly imperative, and can some

Kale Miller 7 Nov 21, 2021