Covid-19 Comorbidities are the Elephant in the Room

Note from the editors:Towards Data Science_ is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional advice. To learn more about the coronavirus pandemic, you can click here._

A _comorbidity _is a condition that a person already has before they contract Covid-19. This is also known as a preexisting condition. Common comorbidities include diabetes, obesity, heart disease, hypertension, dementia, and cancer.

You’ve probably read statements like, “90% of Covid-19 deaths involve comorbidities,” and I have too. Those statements leave me wondering, “How does that affect me?” Because I don’t have any comorbidities, they give me a general sense that I’m at lower risk. But how much lower risk, exactly?

The analysis in this article answers those questions.


If you’re a healthy 21-year-old, your odds of dying from Covid-19 are about 1 in 100,000 — if you even get Covid-19 in the first place. But if you’re 21 with a preexisting condition, might as well be a healthy 60 year old as far as Covid-19 risk is concerned.

If you’re the parent of a high school student with no preexisting conditions, your child’s chances of dying from Covid-19 (if they even get it) is about 1 in 100,000. If your child is under 11 years old, the odds are literally 1 in a million.

On the other hand, if you’re 60 years old with a heart condition and thinking, “Covid-19 isn’t such a big deal,” think again. Your odds of dying from Covid-19 are approximately 1 in 100.

In this article, I’ll explain the math behind these statements. At the end of the article, I’ll provide year-of-age-specific tables that you can use to better understand your personal risk level.

Image for post

Data Needed to Calculate Risk With and Without Comorbidities

We need three pieces of data to calculate age-specific fatality rates with and without comorbidities:

  • Overall infection fatality rate (IFR), by year of age
  • Percentage of Covid-19 deaths involving comorbidities, by year of age
  • Percentage of the population with comorbidities, by year of age

Part 4 of this series explained how to calculate IFRs by year of age and presented the results, so we have that first bit of data covered. Let’s look at the second bit of data we need on the percentage of Covid-19 deaths involving comorbidities, by year of age.

Prevalence of Comorbidities in Covid-19 Fatalities

The CDC reports that, overall, only 6% of deaths involving Covid-19 indicate Covid-19 as the only cause mentioned. Ninety-four percent of Covid-19 deaths involved one or more comorbid conditions [source].

Figure 1 shows a summary of recent CDC data on the presence of specific co-morbidities.

Image for post

Figure 1 — Prevalence of comorbidities in Covid-19 fatalities.

The specific comorbidities vary significantly across ages. Vascular dementia plays a role in 22% of the fatalities in the 85+ age band, but 0% in the 0–24 age band. Obesity is the opposite — factoring in 22% of the youngest group’s deaths, but 0% of the older group’s.

Even though the details vary, the overall number of comorbidities involved in a Covid-19 fatality does not change much across age bands — it runs from a low of 2.02 in the 0–24 age band to a high of 2.37 in the 65–74 age band.

Does that mean we can treat co-morbidity as an age-independent factor?

At the end of May, the CDC published a “Coronavirus Disease 2019 Case Surveillance” report [source]. It listed the percentage of deaths that included comorbidities for each age group, which is shown in Figure 2.

#data-analysis #data-science #covid-19-data #covid19 #data science

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Covid-19 Comorbidities are the Elephant in the Room
Osiki  Douglas

Osiki Douglas

1620127560

Data Scientist Creates Python Script To Track Available Slots For Covid Vaccinations

Bhavesh Bhatt, Data Scientist from Fractal Analytics posted that he has created a Python script that checks the available slots for Covid-19 vaccination centres from CoWIN API in India. He has also shared the GitHub link to the script.

The YouTube content creator posted, “Tracking available slots for Covid-19 Vaccination Centers in India on the CoWIN website can be a bit strenuous.” “I have created a Python script which checks the available slots for Covid-19 vaccination centres from CoWIN API in India. I also plan to add features in this script of booking a slot using the API directly,” he added.

We asked Bhatt how did the idea come to fruition, he said, “Registration for Covid vaccines for those above 18 started on 28th of April. When I was going through the CoWIN website – https://www.cowin.gov.in/home, I found it hard to navigate and find empty slots across different pin codes near my residence. On the site itself, I discovered public APIs shared by the government [https://apisetu.gov.in/public/marketplace/api/cowin] so I decided to play around with it and that’s how I came up with the script.”

Talking about the Python script, Bhatt mentioned that he used just 2 simple python libraries to create the Python script, which is datetime and requests. The first part of the code helps the end-user to discover a unique district_id. “Once he has the district_id, he has to input the data range for which he wants to check availability which is where the 2nd part of the script comes in handy,” Bhatt added.

#news #covid centre #covid news #covid news india #covid python #covid tracing #covid tracker #covid vaccine #covid-19 news #data scientist #python #python script

Aketch  Rachel

Aketch Rachel

1618099140

How Is TCS Helping With COVID-19 Testing In India

COVID-19 cases have only been on the rise. With the non-availability of effective drugs and vaccines, one of the effective ways to control it is to detect it early in patients. However, the task is easier said than done. While a large number of test kits are being produced, they are not enough to conduct testing in large numbers.

Government-run body, C-CAMP or Centre for Cellular and Molecular Platform, has been a key enabler in driving COVID-19 testing as it has been aggressively building, managing and scaling the ecosystem of MSMEs to produce test kits indigenously. However, they might not be enough.

#opinions #c-camp #c-camp tcs #covid-19 #covid-19 testing #tcs #tcs covid-19

Abigail  Cassin

Abigail Cassin

1596574500

How The New AI Model For Rapid COVID-19 Screening Works?

With the current pandemic spreading like wildfire, the requirement for a faster diagnosis can not be more critical than now. As a matter of fact, the traditional real-time polymerase chain reaction testing (RT-PCR) using the nose and throat swab has not only been termed to have limited sensitivity but also time-consuming for operational reasons. Thus, to expedite the process of COVID-19 diagnosis, researchers from the University of Oxford developed two early-detection AI models leveraging the routine data collected from clinical reports.

In a recent paper, the Oxford researchers revealed the two AI models and highlighted its effectiveness in screening the virus in patients coming for checkups to the hospital — for an emergency checkup or for admitting in the hospital. To validate these real-time prediction models, researchers used primary clinical data, including lab tests of the patients, their vital signs and their blood reports.

Led by a team of doctors — including Dr Andrew Soltan, an NIHR Academic Clinical Fellow at the John Radcliffe Hospital, Professor David Clifton from Oxford’s Institute of Biomedical Engineering, and Professor David Eyre from the Oxford Big Data Institute — the research initiated with developing ML algorithms trained on COVID-19 data and pre-COVID-19 controls to identify the differences. The study has been aimed to determine the level of risk a patient can have to have COVID-19.

#opinions #covid screening #covid-19 news #covid-19 screening test #detecting covid

Covid-19 Comorbidities are the Elephant in the Room

Note from the editors:Towards Data Science_ is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional advice. To learn more about the coronavirus pandemic, you can click here._

A _comorbidity _is a condition that a person already has before they contract Covid-19. This is also known as a preexisting condition. Common comorbidities include diabetes, obesity, heart disease, hypertension, dementia, and cancer.

You’ve probably read statements like, “90% of Covid-19 deaths involve comorbidities,” and I have too. Those statements leave me wondering, “How does that affect me?” Because I don’t have any comorbidities, they give me a general sense that I’m at lower risk. But how much lower risk, exactly?

The analysis in this article answers those questions.


If you’re a healthy 21-year-old, your odds of dying from Covid-19 are about 1 in 100,000 — if you even get Covid-19 in the first place. But if you’re 21 with a preexisting condition, might as well be a healthy 60 year old as far as Covid-19 risk is concerned.

If you’re the parent of a high school student with no preexisting conditions, your child’s chances of dying from Covid-19 (if they even get it) is about 1 in 100,000. If your child is under 11 years old, the odds are literally 1 in a million.

On the other hand, if you’re 60 years old with a heart condition and thinking, “Covid-19 isn’t such a big deal,” think again. Your odds of dying from Covid-19 are approximately 1 in 100.

In this article, I’ll explain the math behind these statements. At the end of the article, I’ll provide year-of-age-specific tables that you can use to better understand your personal risk level.

Image for post

Data Needed to Calculate Risk With and Without Comorbidities

We need three pieces of data to calculate age-specific fatality rates with and without comorbidities:

  • Overall infection fatality rate (IFR), by year of age
  • Percentage of Covid-19 deaths involving comorbidities, by year of age
  • Percentage of the population with comorbidities, by year of age

Part 4 of this series explained how to calculate IFRs by year of age and presented the results, so we have that first bit of data covered. Let’s look at the second bit of data we need on the percentage of Covid-19 deaths involving comorbidities, by year of age.

Prevalence of Comorbidities in Covid-19 Fatalities

The CDC reports that, overall, only 6% of deaths involving Covid-19 indicate Covid-19 as the only cause mentioned. Ninety-four percent of Covid-19 deaths involved one or more comorbid conditions [source].

Figure 1 shows a summary of recent CDC data on the presence of specific co-morbidities.

Image for post

Figure 1 — Prevalence of comorbidities in Covid-19 fatalities.

The specific comorbidities vary significantly across ages. Vascular dementia plays a role in 22% of the fatalities in the 85+ age band, but 0% in the 0–24 age band. Obesity is the opposite — factoring in 22% of the youngest group’s deaths, but 0% of the older group’s.

Even though the details vary, the overall number of comorbidities involved in a Covid-19 fatality does not change much across age bands — it runs from a low of 2.02 in the 0–24 age band to a high of 2.37 in the 65–74 age band.

Does that mean we can treat co-morbidity as an age-independent factor?

At the end of May, the CDC published a “Coronavirus Disease 2019 Case Surveillance” report [source]. It listed the percentage of deaths that included comorbidities for each age group, which is shown in Figure 2.

#data-analysis #data-science #covid-19-data #covid19 #data science

Anna Yusef

Anna Yusef

1612362000

Charting COVID-19 Data With Python

Charting provides a powerful way to visualize and explore your data by helping to uncover patterns, trends, relationships, and structures that might not be apparent when looking at a table or map. The COVID-19 pandemic has created voluminous streams of data for scientists, researchers, and decision-makers to visualize, analyze, and understand through a variety of data analysis packages and tools.

This blog walks through visualizing characteristics and trends of the COVID-19 pandemic in the United States during 2020 using the integration between Python and ArcGIS Platform.

Preparing the Data

To get started, I’ll load and prepare the data using pandas, but you can use whatever Python tools you prefer. I’m acquiring the data from the New York Times COVID-19 data repository (publicly accessible here), and I’m filtering the data to include only dates from the complete year of 2020.

import pandas as pd
from arcgis.features import GeoAccessor
import arcpy
arcpy.env.workspace = 'memory'

DATA_URL = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv'
# load data with pandas, create new fields, and filter

daily_df = (
    pd.read_csv(DATA_URL, parse_dates=['date'])
        .sort_values(['state', 'date'])
        .rename(columns={
            'cases': 'cases_total',
            'deaths': 'deaths_total'
        })
        .assign(
            cases_new = lambda df: df.groupby('state')['cases_total'].diff().clip(lower=0),
            deaths_new = lambda df: df.groupby('state')['deaths_total'].diff().clip(lower=0)
        )
        .query("'2020-01-01' <= date <= '2020-12-31'")
        .reset_index(drop=True)
)

Here’s a quick look at the prepared dataset. Notice that there is an individual row for each date and state combination. These rows will be summarized and aggregated when I visualize this data with charts.

#python #covid-19 #covid 19 #charting