The Economist’s tracker for covid-19 excess deaths

Link: https://github.com/TheEconomist/covid-19-excess-deaths-tracker

Excerpt:

This repository contains the data behind The Economist’s tracker for covid-19 excess deaths and the code that we have used to clean, analyse and present the numbers.

….

Our tracker uses data from a number of statistical bureaus, government departments and academic projects. For many of the countries, we have imported total_deaths from the Human Mortality Database, which collates detailed weekly breakdowns from official sources around the world. For other countries, you can find a full list of sources and links in a file called list_of_sources.csv, as well as spreadsheets in the /source-data/ folder.

For most countries, we have imported national figures on official covid deaths from a time series maintained by Johns Hopkins University and Our World In Data. For some countries, we have provided a regional breakdown of mortality. In these cases, we have imported regional covid_deaths from a variety of sources, including a Latin American time series maintained by Data Science Research Peru.

Date accessed: 25 February 2021

Publication Site: github

Annual Statistical Supplement, Social Security

Link: https://www.ssa.gov/policy/docs/statcomps/supplement/2020/index.html

Preface:

The Supplement is a major resource for data on programs administered by the Social Security Administration—the Old-Age, Survivors, and Disability Insurance program, known collectively as Social Security, and the Supplemental Security Income program. The Supplement also includes program summaries and legislative histories that help users of the data understand these programs. Please note that additional disability tables and statistics can be found in the SSI Annual Statistical Report and the Annual Statistical Report on the Social Security Disability Insurance Program.

The Supplement has been published annually since 1940. Decisions affecting the future of Social Security are facilitated by the availability of relevant data over a long period. The data provide a base for research, policy analysis, and proposals for changing the programs. In addition to meeting the Social Security Administration’s information needs, the Supplement strengthens the agency’s ability to respond to requests for program data from congressional committees, government agencies at all levels, and the research community.

The Supplement is prepared by Social Security Administration staff from various components throughout the agency. I would like to express my thanks to them for their contributions.

Katherine N. Bent
Acting Associate Commissioner for Research, Evaluation, and Statistics
February 2021

Date Accessed: 24 February 2021

Publication Site: Social Security Administration

Day-by-day ridership numbers

Link: https://new.mta.info/coronavirus/ridership

Excerpt:

Updated February 18, 2021

We’re keeping this page up to date with systemwide ridership and traffic estimates for subwaysbusesLong Island Rail RoadMetro-North RailroadAccess-A-Ride, and Bridges and Tunnels. You can see changes over the past seven days, as well as get a sense of how ridership and traffic differs this year versus last year. We will generally update the page on weekdays, excluding holidays, with the prior day’s figures. At times, data issues may delay the updates.

Download all the data we have published on this page.

Date Accessed: 18 February 2021

Publication Site: MTA

Cuomo Administration Releases FOIL-Requested Nursing Home Data

Excerpt:

Tonight the Cuomo administration released additional data on coronavirus deaths in long-term care facilities that the Empire Center requested under the Freedom of Information Law.

The data have been posted on our website here.

The release came six months after the FOIL request was submitted, five months after we and the Government Justice Center filed suit, and one week after a court found that the department had violated FOIL and ordered it to release what were clearly public records.

Data link:

Author(s): press release

Publication Date: 10 February 2021

Publication Site: Empire Center for Public Policy

COVID-19 Severity Prediction

Link: https://covidseverity.com//

Graphic:

Excerpt:

The Yu Group at UC Berkeley Statistics / EECS / CCB is working to help forecast the severity of the epidemic for individual counties and hospitals in the US. We develop interpretable models (updated daily) and curate data to predict the trajectory of COVID-19-related deaths. This website provides access to those predictions, in the form of interactive visualizations. We are collaborating with Response4Life to blunt the effect of COVID-19 through the production and appropriate distribution of PPE, medical equipment, and medical personnel to healthcare facilities across the United States.

For hospital level prediction, please go to our hospitalization prediction page where one can upload data for a specific hospital and download prediction results for the given hospital. The uploaded data will only be temporarily used for prediction and will not be collected.GITHUB

Author(s): Yu Group, UC Berkeley Statistics / EECS / CCB

Accessed Date: 10 February 2021

World Mortality Data Set

Link: https://github.com/akarlinsky/world_mortality

Additional Link: https://github.com/dkobak/excess-mortality

Paper: https://www.medrxiv.org/content/10.1101/2021.01.27.21250604v1

Graph:

Abstract:

Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the recent average, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no central, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 77 countries, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in the worst-affected countries the annual mortality increased by over 50%, while in several other countries it decreased by over 5%, presumably due to lockdown measures decreasing the non-COVID mortality. Moreover, we found that while some countries have been reporting the COVID-19 deaths very accurately, many countries have been underreporting their COVID-19 deaths by an order of magnitude or more. Averaging across the entire dataset suggests that the world’s COVID-19 death toll may be at least 1.6 times higher than the reported number of confirmed deaths.

Authors: Ariel Karlinsky, Dmitry Kobak

Date Accessed: 3 February 2021

Publication Date: 29 January 2021

Publication Site: github

NJ Retirees – Politicians

Excerpt:

Public servants often spend multiples of what their salaries will be in the jobs they seek in order to get those jobs since they have other incentives. One of those is likely the pension, even for part time employment, that comes with the job.

Though job-hopping makes it impossible to finger all the mayors or council people who game the system, here are some whose last employer was the Office of the Governor, Senate, or General Assembly who, based on data on retirees in the New Jersey Retirement System taken from the the state pension website are getting over $50,000 annually – along with some other familiar names.

Author: John Bury

Publication Date: 1 February 2021

Publication Site: burypensions

My Alignment Chart of Charts

Link: https://www.makeit-makesense.com/data/my-alignment-chart-of-charts

Graph:

Excerpt:

For this specific application, when I think of lawfulness, I am going to mainly assess the likelihood to be misused. And for good versus evil, I’ll be looking at how well they can typically help the user understand the data. 


Lawful Good: Bar Chart 

This is the best alignment you can be. In traditional use, lawful good applies to people that both follow the rules and help others. Here I’m applying it to a chart that I think is often used well and is easy to read. Name a better liked and more used chart than the bar chart – you can’t. 10/10 analysts would recommend. 

Author: Autumn Battani

Publication Date: 30 January 2021

Publication Site: Make It Make Sense

Total deaths in the UK from 2000 to 2020

Link: https://www.ons.gov.uk/aboutus/transparencyandgovernance/freedomofinformationfoi/totaldeathsintheukfrom2000to2020

YearUnited KingdomEngland and WalesEnglandWalesScotlandNorthern Ireland
2018616,014541,589505,85934,40658,50315,922
2017607,172533,253498,88233,24857,88316,036
2016597,206525,048490,79133,06656,72815,430
2015602,782529,655495,30933,19857,57915,548
2014570,341501,424468,87531,43954,23914,678
2013576,458506,790473,55232,13854,70014,968
2012569,024499,331466,77931,50254,93714,756
2011552,232484,367452,86230,42653,66114,204
2010561,666493,242461,01731,19753,96714,457
2009559,617491,348459,24131,00653,85614,413
2008579,697509,090475,76332,06655,70014,907
2007574,687504,052470,72132,14855,98614,649
2006572,224502,599470,32631,08355,09314,532
2005582,964512,993479,67832,16255,74714,224
2004584,791514,250480,71732,31756,18714,354
2003612,085539,151504,12733,81058,47214,462
2002608,045535,356500,79233,31458,10314,586
2001604,393532,498497,87833,24957,38214,513
2000610,579537,877503,02633,50157,79914,903

Date Accessed: 28 January 2021

Publication Site: Office for National Statistics, UK