The One Graph You Need to Understand Covid-19 Comorbidities

I wrote a series of much longer articles on Covid-19 fatality rates, including one on the effect of comorbidities. Here’s a graph that summarizes the conclusions of that article:

The full article provides details and explains some of the ifs, ands, or buts about the numbers on this graph. But the graph is basically it, including the fat lines that are intended to show that the fatality rates are approximate.

The figure below highlights the risk for a 74 year old (Trump) vs. a 77 year old (Biden). I don’t know the comorbidity status of either candidate, so I’ve highlighted the range for each candidate.

Five-Part Series on Covid-19 Fatality Rates

These are the more detailed articles on Covid-19 fatality rates.

  • Part 1: Establishing an overall base IFR for the US
  • Part 2: Age-based IFRs
  • Part 3: Variation in IFRs across states and countries based on demographics
  • Part 4: IFRs by individual year of age
  • Part 5: IFRs with and without co-morbidities

More Details on the Covid-19 Information Website

I lead the team that contributes the CovidComplete forecasts into the CDC’s Ensemble model. For updates to these graphs, more graphs, forecasts at the US and state-level, and forecast evaluations, check out my Covid-19 Information website.

My Background

For the past 20 years, I have focused on understanding the data analytics of software development, including quality, productivity, and estimation. The techniques I’ve learned from working with noisy data, bad data, uncertainty, and forecasting all apply to COVID-19.

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Author of Code Complete and More Effective Agile, Contributor to CDC Covid19 Forecasting, CEO at Construx, Dog Walker, Motorcyclist, Cinephile, DIYer, Rotarian.

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Steve McConnell

Steve McConnell

Author of Code Complete and More Effective Agile, Contributor to CDC Covid19 Forecasting, CEO at Construx, Dog Walker, Motorcyclist, Cinephile, DIYer, Rotarian.

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