Covid-19 Research: Are we moving too fast?

There has been an enormous volume of important COVID-19 research coming out into the public domain This includes studies aimed at calculating case fatalities, effectiveness of new treatments, risk profiles, and effectiveness of mitigation strategies. One can understand why — there is an insatiable appetite and need for information about the novel coronavirus, and a promise of not only much publicity for any research findings on the topic but also the hope that such research can make an immediate difference in people’s lives by helping to determine the best response to this pandemic. That being said, a degree of caution is needed when it comes to the dissemination of new findings.

Existing problems with publishing

It is not uncommon for scientists to spend months, if not years, carefully developing an idea into a paper but we are seeing an increasing number of instances where the whole process takes a matter of days. Bias towards publishing research with ‘sexy’ findings often facilitated by problems in the research design, such as small samples and the winners curse, multiple comparisons, and selective reporting of results have been the source of much discussion. There are a small number of exceptions but it is generally the result of misinformation coupled with cognitive biases such as confirmation bias which we are all susceptible too (e.g. we tend to only see the evidence we want to see) rather than any malfeasance. There are also signs that such problems are beginning to be taken more seriously by scientists across all disciplines.

The pandemic has intensified the above issues, however, as not only are researchers rushing to write papers, but journals are also rushing to publish them with an expedited peer review process. Of course it is important to get good science on an important topic out into the public domain as quickly as possible but this does make an already unpredictable peer review process even noisier than usual. While good science has been key to shaping our response to the pandemic, research undertaken and published with great haste has the potential to cause harm.

#peter-howley #research #data-science #academia #coronavirus #data analysis

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Covid-19 Research: Are we moving too fast?

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

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

Covid-19 Research: Are we moving too fast?

There has been an enormous volume of important COVID-19 research coming out into the public domain This includes studies aimed at calculating case fatalities, effectiveness of new treatments, risk profiles, and effectiveness of mitigation strategies. One can understand why — there is an insatiable appetite and need for information about the novel coronavirus, and a promise of not only much publicity for any research findings on the topic but also the hope that such research can make an immediate difference in people’s lives by helping to determine the best response to this pandemic. That being said, a degree of caution is needed when it comes to the dissemination of new findings.

Existing problems with publishing

It is not uncommon for scientists to spend months, if not years, carefully developing an idea into a paper but we are seeing an increasing number of instances where the whole process takes a matter of days. Bias towards publishing research with ‘sexy’ findings often facilitated by problems in the research design, such as small samples and the winners curse, multiple comparisons, and selective reporting of results have been the source of much discussion. There are a small number of exceptions but it is generally the result of misinformation coupled with cognitive biases such as confirmation bias which we are all susceptible too (e.g. we tend to only see the evidence we want to see) rather than any malfeasance. There are also signs that such problems are beginning to be taken more seriously by scientists across all disciplines.

The pandemic has intensified the above issues, however, as not only are researchers rushing to write papers, but journals are also rushing to publish them with an expedited peer review process. Of course it is important to get good science on an important topic out into the public domain as quickly as possible but this does make an already unpredictable peer review process even noisier than usual. While good science has been key to shaping our response to the pandemic, research undertaken and published with great haste has the potential to cause harm.

#peter-howley #research #data-science #academia #coronavirus #data analysis