thymetech / covid-19

Plots and analysis relating to the pandemic

COVID-19

Plots and analysis relating to the pandemic

1. Heatmaps:
   English LA Heatmaps.R generates heatmaps and animated maps showing English Upper Tier Local Authority trajectories in both confirmed COVID-19 cases and estimated COVID-19 deaths (in hospitals only) inspired by similar plots for US states from @Marco_Piani. The approach to modelling deaths, which are only published at NHS trust level, was developed by @Benj_Barr. The code also generates a map of Local Authority-level changes in case numbers in the past week and animated maps of both case and death counts.
   
   Scottish HB Heatmaps.R, Welsh LA Heatmaps.R, Irish County Heatmaps.R and German State Heatmaps.R produce equivalent case trajectory plots for Scottish Health Boards, Welsh Local Authorities, Irish Counties and German Bundesländer respectively.
   
   UK Hex Animation.R uses this data to generate an animated hex map of COVID-19 cases across the UK & Ireland, built on various excellent hex map resources from @ODILeeds and @olihawkins.
   
   I've also made a couple of apps to allow you to explore local and national COVID-19 data. One for mortality, cases and admissions data and another for case data stratified by age.

2. All Cause Mortality:
   AllCauseDeaths.R harmonises weekly all-cause mortality data from England & Wales (from ONS), Scotland (from NRS) and Northern Ireland (from NISRA) and draws plots comparing deaths in 2020 so far to the previous decade, split by age, sex and region inspired by a plot from @EdConwaySky.
   
   ScottishAllCauseDeathsDetail.R uses richer data published by NRS to look at patterns in excess mortality in Scotland by place of death, Health Board area and age. NRS Excess Deaths by Cause.R produces graphs of excess deaths in Scotland by cause and location of death.
   
   All Cause Deaths France.R uses detailed French all-cause mortality records published by Insee, the French statistical authority, to examine age-specific excess deaths in France and All Cause Deaths Italy.R does the same for Italy using data from ISTAT. AllCauseDeathsxAge.R brings these together and extends this analysis using data from the UK and international data from the Human Mortality Database.
   
   MSOA Deaths.R takes mortality data from England & Wales at Middle Super Output Area level and from Scotland at Intermediate Zone level and maps it, ready for 3D visualisation using Aerialod.

3. Exposure Mapping:
   COVIDExposures.R brings together data on health deprivation and estimates of the potential COVID-19 mortality risk based on the age-sex structure of the population (following the approach developed by @ikashnitsky and @jm_aburto) at Lower Super Output Area level and plots bivariate maps highlighting areas with the greatest potential COVID-19 risk. I also made a Shiny app which creates slightly lower resolution versions of the same plots online, which you can find here, and wrote about these maps for the UK Data Service's Impact and Innovation blog.

4. Initial Inequality Estimates:
   Estimated Cases By IMD.R takes published figures on confirmed COVID-19 cases by Local Authority in England and maps that onto quintiles of the Index of Multiple Deprivation, then plots a variety of case trajectories by deprivation quintile as well as a map of confirmed case rates.

5. Observed Inequality:
   ONS Deaths Ineq.R takes data that ONS have published for England on deaths from COVID-19 and other causes between 1st March-17th April and illustrates socioeconomic inequalities in the impact of the pandemic.
   
   ONS DeathsIneq 2.R brings in historical data on socioeconomic inequalities in all-cause deaths to compare the inequality impacts of the pandemic on mortality to historical levels of inequality.

Suggested citation for any of this analysis:
Angus, Colin (2020): CoVid Plots and Analysis. The University of Sheffield. Dataset. https://doi.org/10.15131/shef.data.12328226