Exploring the Link between COVID-19

The drastic changes in our lifestyles coupled with restrictions, quarantines, and social distancing measures introduced to combat the corona virus outbreak have lead to an alarming rise in mental health issues all over the world. Social media is a powerful indicator of the mental state of people at a given location and time. In order to study the link between the corona virus pandemic and the accelerating pace of depression and anxiety in the general population, I decided to explore tweets related to corona virus.

How is this blog organized?

In this blog post, I will first use keras to train a neural network to recognize depressive tweets. For this, I will use a data set of 10,314 tweets divided into depressive tweets (labelled 1) and non depressive tweets (labelled 0). This data set is made by Viridiana Romero Martinez. Here is the link to her github profile: https://github.com/viritaromero

Once I have the network trained, I will use it for testing tweets scraped from twitter. To establish the link between COVID-19 and depression, I will obtain two different data sets. The first data set will be comprised of tweets with corona virus related keywords such as ‘COVID-19’, ‘quarantine’, ‘pandemic’, and ‘virus’. The second data set will be comprised of random tweets searched using neutral keywords such as ‘and’, ‘I’, ‘the’ etc. The second data set will serve as a control to check the percentage of depressive tweets in a random sample of tweets. This will allow us to measure the difference in percentage of depressive tweets in a random sample and a sample with COVID-19 specific tweets.

Preprocessing the data

Image for post

Image source: https://xaltius.tech/why-is-data-cleaning-important/

Before we can get started with training the neural networks, we need to collect and clean the data.

#twitter-sentiment #covid19 #neural-networks #data-science #machine-learning #deep learning

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Exploring the Link between COVID-19

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


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


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

Anna Yusef

Anna Yusef


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'])
            'cases': 'cases_total',
            'deaths': 'deaths_total'
            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'")

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

Travel after COVID-19: Learning from previous industry crises

Long before the 2020 coronavirus pandemic started, travel business repeatedly faced wars, energy shocks, economic downturns, natural disasters, and epidemics. But every time it adapted to the new reality, learning lessons from crises.

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Check our article to learn more about the Travel After Covid-19: https://www.altexsoft.com/blog/before-coronavirus-five-biggest-crises-in-travel-industry/

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#coronavirus #covid #travel #airlines #crises #covid19 #sarscov #sars-cov #airlineindustry #travelworks #Eyjafjallajökull #iceland #volcano #yumkippurwar #oilshock #concorde #postcoronavirus #11september #travelcrises #airlinecrises

#covid-19 #travel after covid-19