Angela  Dickens

Angela Dickens

1596441660

Impact of COVID-19 on the UK BAME population

_“…the virus knows no race or nationality; it can’t peek at your driver’s license or census form to check whether you are black. Society checks for it, and provides the discrimination on the virus’s behalf.” — _Game Wood — The Atlantic

Image for post

There is growing concern that coronavirus has had a greater impact on people from ethnic minorities. Black, Asian and Minority Ethnic (BAME) communities account for 14% of the population but make up a third of critically ill coronavirus patients in hospitals is the headline statistic. The Public Health England report finds that there is a disproportionate impact of Covid-19 on people from all non-white ethnic minorities.

This article compares excess death statistics with openly available socio-economic data to present a descriptive picture of the situation in England and Wales. Descriptive analysis uses 2011 census data to determine the ethnic mix of local authorities; an approach similar to one used by the ONS. As a result, the unit of observation will be local authorities. A full breakdown of raw data, transformation and analysis can be found and reproduced on GitHub.

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._

What’s going on?

Local Authority Ethnicity share against Excess Deaths

The above plot shows deaths occurring within a Local Authority against the proportional representation of a particular ethnicity in that Local Authority. Lines sloping upwards from left to right indicate that local authorities with higher proportion of BAME population see more excess deaths. Whereas the downward sloping purple line indicates that less ethnically diverse Local Authorities see lower number of excess deaths. It is important to note that data does cluster close to 0, and regression lines fitted to this kind of clumped data is not evidence of a relationship by itself. This is not measuring the impact to the BAME community directly, but instead comparing geographic areas more negatively affected by the virus, and it’s corresponding ethnic make up.

Image for post

Age

Before we look at the location specific factors affecting virus’ impact, we must consider the age of the afflicted, which we understand to be a contributing factor to the mortality of COVID-19. Data published by the ONS contains counts of coronavirus-related deaths by ethnic group in England and Wales and show that 88% of Coronavirus-related deaths are people over 64.

COVID Deaths and Total Population

The figure above shows ethnic breakdown of all recorded covid deaths. Interestingly, this breakdown is fairly similar to the 2011 census statistics which showed a 14% of the population were BAME and the covid data shows that 16.2% of COVID deaths are BAME. There doesn’t seem to be a disproportionate effect on the BAME population.

To square this circle we can use a Dana Mackenzie blog post looking at race and covid in the USA through the Simpsons paradox. Presenting the below causal model, he assumes that ethnicity will influence chances of living to age 65 or older.

Image for post

This forms a chain, through which the causal effect can pass through. If we are to ask, what is the effect of ethnicity **only **on Covid mortality, then we must hold all other variables constant. It follows that we should control for age and split by age, creating two groups ‘0–64’ and ‘65+’:

#data-analysis #covid19 #bame #open-data #data analysisa

What is GEEK

Buddha Community

Impact of COVID-19 on the UK BAME population

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

Angela  Dickens

Angela Dickens

1596441660

Impact of COVID-19 on the UK BAME population

_“…the virus knows no race or nationality; it can’t peek at your driver’s license or census form to check whether you are black. Society checks for it, and provides the discrimination on the virus’s behalf.” — _Game Wood — The Atlantic

Image for post

There is growing concern that coronavirus has had a greater impact on people from ethnic minorities. Black, Asian and Minority Ethnic (BAME) communities account for 14% of the population but make up a third of critically ill coronavirus patients in hospitals is the headline statistic. The Public Health England report finds that there is a disproportionate impact of Covid-19 on people from all non-white ethnic minorities.

This article compares excess death statistics with openly available socio-economic data to present a descriptive picture of the situation in England and Wales. Descriptive analysis uses 2011 census data to determine the ethnic mix of local authorities; an approach similar to one used by the ONS. As a result, the unit of observation will be local authorities. A full breakdown of raw data, transformation and analysis can be found and reproduced on GitHub.

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._

What’s going on?

Local Authority Ethnicity share against Excess Deaths

The above plot shows deaths occurring within a Local Authority against the proportional representation of a particular ethnicity in that Local Authority. Lines sloping upwards from left to right indicate that local authorities with higher proportion of BAME population see more excess deaths. Whereas the downward sloping purple line indicates that less ethnically diverse Local Authorities see lower number of excess deaths. It is important to note that data does cluster close to 0, and regression lines fitted to this kind of clumped data is not evidence of a relationship by itself. This is not measuring the impact to the BAME community directly, but instead comparing geographic areas more negatively affected by the virus, and it’s corresponding ethnic make up.

Image for post

Age

Before we look at the location specific factors affecting virus’ impact, we must consider the age of the afflicted, which we understand to be a contributing factor to the mortality of COVID-19. Data published by the ONS contains counts of coronavirus-related deaths by ethnic group in England and Wales and show that 88% of Coronavirus-related deaths are people over 64.

COVID Deaths and Total Population

The figure above shows ethnic breakdown of all recorded covid deaths. Interestingly, this breakdown is fairly similar to the 2011 census statistics which showed a 14% of the population were BAME and the covid data shows that 16.2% of COVID deaths are BAME. There doesn’t seem to be a disproportionate effect on the BAME population.

To square this circle we can use a Dana Mackenzie blog post looking at race and covid in the USA through the Simpsons paradox. Presenting the below causal model, he assumes that ethnicity will influence chances of living to age 65 or older.

Image for post

This forms a chain, through which the causal effect can pass through. If we are to ask, what is the effect of ethnicity **only **on Covid mortality, then we must hold all other variables constant. It follows that we should control for age and split by age, creating two groups ‘0–64’ and ‘65+’:

#data-analysis #covid19 #bame #open-data #data analysisa

How Technology ensures an Omnichannel Experience

Consumer behaviour has forever transformed with the onset of the COVID-19 pandemic. Retail, like all other industries, has been disrupted in its own ways. To begin with, the closure of most physical stores due to lockdown measures and social distancing has left retail businesses looking for new channels to reach their customers.

Retail has seen one of the most significant shifts till date after the onset of the COVID-19 pandemic – Retail consumers are adopting digital commerce and omnichannel modes of purchase and this is likely to stay post-COVID-19.

_As per Accenture’s research, there will be a 160% rise in eCommerce purchases from new and low-frequency consumers. _

So what does it mean for retail organizations? Those who viewed digital and omnichannel networks as secondary need to reorganize their strategies to point towards them.

There’s an opportunity here to increase revenue channels and onboard new customers but only the use of technology can help organizations to scale these services appropriately. Let’s find out how.

How retail post COVID 19 will change?

Now that a lot of countries are starting to lift lockdown measures, retail stores will begin to re-open. Amidst the uncertainty, one thing is certain – retail stores will never be the same again. COVID-19’s permanent effect on consumer behaviour has to be taken into account going forward to design their strategies.

Retailers who had a digital-first mindset and an omnichannel approach have responded better than the ones who gave preference to physical stores and face-to-face engagement. They have been able to quickly enrich the omnichannel experience for their customers and will be able to recover faster from the damages.

Let’s go through a few phenomena that are indicating this shift towards omnichannel models and which will stay even post-COVID-19.

#business insights #covid 19 business impact #retail analytics #retail post covid 19 #technology in covid 19 #data analytic