A quick look at the timing of measures for COVID-19

Authors: Gita Das & Catherine Lopes

From the 1st COVID-19 case appears in a country, this virus has penetrated into different communities at a different rate among different countries. At a certain point of time, governments normally implement a certain type of measures and policies in order to save lives and livelihood. These measures include testing, tracing, increasing the capacity of the health system, lockdown and travel restrictions, and many more. In this article, we study a group of countries to see how they are performing in controlling the spread of the virus with the timing of various measures they used.

Data & countries in this study

Continuing from our previous work, we included the countries that have more confirmed cases than Australia in this study. As of 12 Jun 2020, there are 64 countries that have more confirmed cases and their measures data are available. As a result, these 64 countries are to be explored in this study and their confirmed cases as of 12th Jun are shown in Fig 1 below.

Fig 1. Countries have more cases than Australia as of 12 Jun, 2020

We used 2 sets of data from the Humanitarian Data Exchange (HDE).

  1. Dataset that tracks the number of people affected by COVID-19 worldwide including the cases of confirmed, recovered and deaths.
  2. Dataset for imposed measures “acaps_covid19_government_measures_dataset.xlsx” is obtained HDE.

Correlations among the imposed measures

A group of 33 measures are presented among the countries in this study, and they range from “strengthening the public health system” to “partial lockdown”, and many others. Each measure is scaled to [0,1] based on the timing of each specific measure used by a country. From the day of the 1st case reported in a country, if a measure was not used, then the measure is recorded as 0. The earlier a measure was introduced by a country, the higher value (closer to 1) that specific measure is recorded. A heatmap based on Pearson correlation coefficient is shown in Fig 2 below.

#covid19 #clustering-analysis #australia #correlation #preventive-measures #data analysis

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A quick look at the timing of measures for COVID-19
Osiki  Douglas

Osiki Douglas

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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.

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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.

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Abigail  Cassin

Abigail Cassin

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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

Jenny Jabde

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A quick look at the timing of measures for COVID-19

Authors: Gita Das & Catherine Lopes

From the 1st COVID-19 case appears in a country, this virus has penetrated into different communities at a different rate among different countries. At a certain point of time, governments normally implement a certain type of measures and policies in order to save lives and livelihood. These measures include testing, tracing, increasing the capacity of the health system, lockdown and travel restrictions, and many more. In this article, we study a group of countries to see how they are performing in controlling the spread of the virus with the timing of various measures they used.

Data & countries in this study

Continuing from our previous work, we included the countries that have more confirmed cases than Australia in this study. As of 12 Jun 2020, there are 64 countries that have more confirmed cases and their measures data are available. As a result, these 64 countries are to be explored in this study and their confirmed cases as of 12th Jun are shown in Fig 1 below.

Fig 1. Countries have more cases than Australia as of 12 Jun, 2020

We used 2 sets of data from the Humanitarian Data Exchange (HDE).

  1. Dataset that tracks the number of people affected by COVID-19 worldwide including the cases of confirmed, recovered and deaths.
  2. Dataset for imposed measures “acaps_covid19_government_measures_dataset.xlsx” is obtained HDE.

Correlations among the imposed measures

A group of 33 measures are presented among the countries in this study, and they range from “strengthening the public health system” to “partial lockdown”, and many others. Each measure is scaled to [0,1] based on the timing of each specific measure used by a country. From the day of the 1st case reported in a country, if a measure was not used, then the measure is recorded as 0. The earlier a measure was introduced by a country, the higher value (closer to 1) that specific measure is recorded. A heatmap based on Pearson correlation coefficient is shown in Fig 2 below.

#covid19 #clustering-analysis #australia #correlation #preventive-measures #data analysis