Variants, Lineages, and Strains of Coronavirus

All viruses mutate. How fast depends on several factors. Viruses with an RNA genome tend to mutate faster than viruses with a DNA genome. This is because RNA viruses have less ability to fix errors when their genetic material is copied to make virus particles inside infected cells. So every time a virus replicates, there is the chance of a mutation occurring.

Most mutations do not change the encoded viral protein. These are considered “silent” mutations. Others do change the amino acid sequence of the encoded viral protein. However, most mutations have no functional consequence for the virus. The amino acid change does not make the virus pathogenic or not pathogenic (able to cause disease), more or less transmissible (contagious), or more or less disease causing (virulent). So, these mutations are functionally inert. Some mutations may be functionally inert to the virus but alter the ability of the infected host to recognize and eliminate the virus. These do not alter the function of the viral protein, but they do change the immunogenicity of the viral protein, which can impact virulence and transmissibility.

Mutations can result in a new “lineage” of the virus. This is not the same as a new strain. Tracking these lineages can be very useful for determining how a virus spread through communities or populations (Figure 1). For a cool interactive that shows how the various lineages of SARS-CoV-2, which causes COVID-19, spread throughout the globe visit nextstrain.org/ncov.

Figure 1. Tracking the spread of the coronavirus SARS-CoV-2 throughout the world using viral genomic data. From https://nextstrain.org/ncov . Read more

Mutations that alter any of the following can lead to a new strain:

• pathogenicity
• virulence
• immunogenicity.

Not all changes that affect immunogenicity of the viral protein affect the function of the viral protein, and not all changes that alter viral protein function affect immunogenicity (Figure 2).

#evolution #coronavirus #science #health #covid19 #data science

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Variants, Lineages, and Strains of Coronavirus

All viruses mutate. How fast depends on several factors. Viruses with an RNA genome tend to mutate faster than viruses with a DNA genome. This is because RNA viruses have less ability to fix errors when their genetic material is copied to make virus particles inside infected cells. So every time a virus replicates, there is the chance of a mutation occurring.

Most mutations do not change the encoded viral protein. These are considered “silent” mutations. Others do change the amino acid sequence of the encoded viral protein. However, most mutations have no functional consequence for the virus. The amino acid change does not make the virus pathogenic or not pathogenic (able to cause disease), more or less transmissible (contagious), or more or less disease causing (virulent). So, these mutations are functionally inert. Some mutations may be functionally inert to the virus but alter the ability of the infected host to recognize and eliminate the virus. These do not alter the function of the viral protein, but they do change the immunogenicity of the viral protein, which can impact virulence and transmissibility.

Mutations can result in a new “lineage” of the virus. This is not the same as a new strain. Tracking these lineages can be very useful for determining how a virus spread through communities or populations (Figure 1). For a cool interactive that shows how the various lineages of SARS-CoV-2, which causes COVID-19, spread throughout the globe visit nextstrain.org/ncov.

Figure 1. Tracking the spread of the coronavirus SARS-CoV-2 throughout the world using viral genomic data. From https://nextstrain.org/ncov . Read more

Mutations that alter any of the following can lead to a new strain:

• pathogenicity
• virulence
• immunogenicity.

Not all changes that affect immunogenicity of the viral protein affect the function of the viral protein, and not all changes that alter viral protein function affect immunogenicity (Figure 2).

#evolution #coronavirus #science #health #covid19 #data science

5 Things the Coronavirus Forced the World to Learn About Remote Work

Like pretty much the rest of the world, one fine day I was told to not set foot in the office any longer. Fortunately for me, I was not being let go. Offices were closing all over the world, my country included, and we were told to work from our homes. I am fortunate enough to work for a company that hasn’t wilted from the lockdowns and to have a programming job that can still continue unabated, albeit with more Zoom calls, creature comforts, and the temptation to nap in my cozy bed the next room over.

After some reading, I’ve discovered that what I’ve experienced in my time remote working is not only common across telecommuters, but there are aspects of it superior to my commuting life. **Make no mistake, the benefits of remote work are nothing to be sneezed at. **And while the pandemic has been catastrophic for everyone literally everywhere, this involuntary change of work arrangement so far has been the perfect catalyst to impress upon old school office managers that people can perform without cramming them in cubicles. By forcing everyone to try to figure out how to physically distance everyone, companies have had to come up with ways to get work done even from our homes. Our homes!

How am I gonna micromanage- I mean, motivate my team now?!

But as we’re gonna see here, remote work is not just viable now, it’s only getting better with time, and will probably be here to stay. So let’s jump right into what the coronavirus has pushed us to learn about remote work.

#coronavirus #remote-working #remote work #coronavirus #machine learning

Python Analysis on Corona-virus data with ArcGIS Dashboards

Tntroduction: The year 2020 have many unexpected scenarios. The main concern is about corona-virus (COVID-19) which is spreading on a daily basis in India. So, let’s try to analyse visualise how fast it is spreading.We use Python tool to analyse the data from git.

First, let’s look at the dashboard created by Johns Hopkins University. You can look at the following live dashboard to see the real-time trend. COVID-19 Live Dashboard prepared by ArcGIS .Now, let’s create a similar analysis for India using Python to visualise the most affected states in India due to corona-virus.

ArcGIS Dashboards

COVID 19 Cases www.arcgis.com

Figure 1.1: ARC GIS: Cumulative Cases 2020

The global statistic showing the cumulative cases for USA, Brazil, India.The figure 1 chart prepared using ARCGIS software helps us to to present data for Active cases, indicate, rate, case-fatality ratio, testing rate, hospitalisation rate. With the help of data points we understand the global presentation of data points.

Figure 1.2. Dashboard explaining Global deaths and incidence rate

With help of Figure 1.2 ,the dashboard explains the total incidence rate. The total global deaths showing for each countries. There are 5 tabs with distribution of data. Left side of graph is count of cases by region. Right side of graph is number of recoveries and number of death region wise. The vertical bar chart is presented at the right side corner of the dashboard.

#coronavirus-update #corona #python #coronavirus-covid19 #coronavirus

Scraping COVID19 Data Using Python

With various attempt to clamp down the effect of COVID19 on the world, various research works and innovative measures depends on insights gained from the right data. Most of the data required to aid innovations may not be available via Application Programming Interface (API) or file formats like ‘.csv’ waiting to be downloaded, but can only be accessed as part of a web page. All code snippet can be found here.

Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Whether you are a data scientist, engineer, or anybody who analyzes large amounts of datasets, the ability to scrape data from the web is a useful skill to have.

Worldometers has a credible sources of COVID19 data around the world. In this article, we will learn how to scrape COVID19 data depicted below from a web page to a Dask dataframe from the site using python.

Why Dask dataframe?

Pandas have been one of the most popular and favorite data science tools used in Python programming language for data wrangling and analysis. Pandas have their own limitations when it comes to big data due to its algorithm and local memory constraints.

However, Dask is an open-source and freely available Python library. Dask provides ways to scale Pandas, Scikit-Learn, and Numpy in terms of performance and scalability. In the context of this article, the dataset is bound to be constantly increasing, making Dask the ideal tool to use.

Elements of a web page

Before we delve into web scraping proper, lets clear up the difference between a webpage and website. A web page can be considered as a single entity whereas a website is a combination of web pages. Web pages are accessed through a browser while in website HTTP, and DNS protocols are used to access it. The content in a website changes according to the web page while a web page contains more specific information.

There are Four(4) basic elements of a webpage, which are:

1. Structure
2. Function
3. Content
4. Aesthetics

The above-listed elements, fall into but not limited to these programmable component such as HTML— contain the main content of the page, CSS — add styling to make the page look nicer and lastly JS— JavaScript files add interactivity to web pages.

When we perform web scraping, we’re interested in extraction of information from the main content of the web page, which makes a good understanding of HTML important.

#web-scraping #covid19 #coronavirus-covid19 #coronavirus #data

Why is Quick Dev Collaboration A Massive Headache?

COVID-19 has undoubtedly exacerbated the feeling, but who else thinks collaborating or merely communicating with your teammates on code is far from convenient, especially when it comes to quick problem resolution?

As software engineers, we are all too familiar with being stuck on a problem for hours, whereas it’s solution turns out to be a single line of code or even less. Let’s have a deeper dive into one such basic, however frequent situation.

Say my teammate works on an issue that I happen to be more familiar with than them. So my teammate is stuck and can’t be bothered to resolve the issue communicating. Why? Because it is a pain in the a!**

#collaboration #team-collaboration #remote-work #software-development #latest-tech-stories #coronavirus-impact-on-business #coronavirus #developer-collaboration