Is Earthquake A Natural Disaster
Is Earthquake A Natural Disaster . . . . . . 1. Natural disaster - Wikipedia, the free encyclopedia en.wikipedia.org/wiki/Natural_disaster o o A natural disaster is a major adverse event...
By: Sarfraz Nawaz Shaik
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Is Earthquake A Natural Disaster - Video
Epidemiologist want to forecast disease like meteorologists forecast rain. And the way people browse Wikipedia could be the key, they say.
This time last year, the Centers for Disease Control and Prevention in Atlanta launched a competition to find the best way to forecast the characteristics of the 2013-2014 influenza season using data gathered from the internet. Today, Kyle Hickmann from Los Alamos National Laboratories in New Mexico and a few pals reveal the results of their model which used real-time data from Wikipedia to forecast the ground truth data gathered by the CDC that surfaces about two weeks later.
They say their model has the potential to transform flu forecasting from a black art to a modern science as well-founded as weather forecasting.
Flu takes between 3,000 and 49,000 lives each year in the U.S. so an accurate forecast can have a significant impact on the way society prepares for the epidemic. The current method of monitoring flu outbreaks is somewhat antiquated. It relies on a voluntary system in which public health officials report the percentage of patients they see each week with influenza-like illnesses. This is defined as the percentage of people with a temperature higher than 100 degrees, a cough and no other explanation other than flu.
These numbers give a sense of the incidence of flu at any instant but the accuracy is clearly limited. They do not, for example, account for people with flu who do not seek treatment or people with flu-like symptoms who seek treatment but do not have flu.
There is another significant problem. The network that reports this data is relatively slow. It takes about two weeks for the numbers to filter through the system so the data is always weeks old.
Thats why the CDC is interested in finding new ways to monitor the spread of flu in real time. Google, in particular, has used the number of searches for flu and flu-like symptoms to forecast flu in various parts of the world. That approach has had considerable success but also some puzzling failures. One problem, however, is that Google does not make its data freely available and this lack of transparency is a potential source of trouble for this kind of research.
So Hickmann and co have turned to Wikipedia. Their idea is that the variation in numbers of people accessing articles about flu is an indicator of the spread of the disease. And since Wikipedia makes this data freely available to any interested party, it is an entirely transparent source that is likely to be available for the foreseeable future.
Hickman and co use the flu-article data from earlier years to train a machine learning algorithm to spot the link with the influenza-like illness figures collected by the CDC. They then used the algorithm to predict flu levels in real time during last years flu season.
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How Wikipedia Data Is Revolutionising Flu Forecasting
Epidemiologist want to forecast disease like meteorologists forecast rain. And the way people browse Wikipedia could be the key, they say.
This time last year, the Centers for Disease Control and Prevention in Atlanta launched a competition to find the best way to forecast the characteristics of the 2013-2014 influenza season using data gathered from the internet. Today, Kyle Hickmann from Los Alamos National Laboratories in New Mexico and a few pals reveal the results of their model which used real-time data from Wikipedia to forecast the ground truth data gathered by the CDC that surfaces about two weeks later.
They say their model has the potential to transform flu forecasting from a black art to a modern science as well-founded as weather forecasting.
Flu takes between 3,000 and 49,000 lives each year in the U.S. so an accurate forecast can have a significant impact on the way society prepares for the epidemic. The current method of monitoring flu outbreaks is somewhat antiquated. It relies on a voluntary system in which public health officials report the percentage of patients they see each week with influenza-like illnesses. This is defined as the percentage of people with a temperature higher than 100 degrees, a cough and no other explanation other than flu.
These numbers give a sense of the incidence of flu at any instant but the accuracy is clearly limited. They do not, for example, account for people with flu who do not seek treatment or people with flu-like symptoms who seek treatment but do not have flu.
There is another significant problem. The network that reports this data is relatively slow. It takes about two weeks for the numbers to filter through the system so the data is always weeks old.
Thats why the CDC is interested in finding new ways to monitor the spread of flu in real time. Google, in particular, has used the number of searches for flu and flu-like symptoms to forecast flu in various parts of the world. That approach has had considerable success but also some puzzling failures. One problem, however, is that Google does not make its data freely available and this lack of transparency is a potential source of trouble for this kind of research.
So Hickmann and co have turned to Wikipedia. Their idea is that the variation in numbers of people accessing articles about flu is an indicator of the spread of the disease. And since Wikipedia makes this data freely available to any interested party, it is an entirely transparent source that is likely to be available for the foreseeable future.
Hickman and co use the flu-article data from earlier years to train a machine learning algorithm to spot the link with the influenza-like illness figures collected by the CDC. They then used the algorithm to predict flu levels in real time during last years flu season.
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How Wikipedia Data Is Revolutionizing Flu Forecasting
TIME Politics 2014 Election Can Wikipedia Predict the Elections? Kentucky Democratic Senate candidate Alison Lundergan Grimes holds a mailer she asserted to be an illegal voter intimidation tactic as she rallies her supporters during a stop at the United Auto Workers hall in Bowling Green, Ky., Monday, Nov. 3, 2014. J. Scott ApplewhiteAP Your move, Nate Silver
You can almost picture it: The voter picks up the voter guide or maybe even an absentee ballot sits down at his or her computer and gets ready to decide the crucial Senate race. First stop: Wikipedia.
Yes, the community-edited online encyclopedia is hardly the most thorough (or fair) source of information on political candidates, but its not a bad start. (Hey, weve done it, and were professionals.)
So what does it tell us about the crucial Senate fights? A look at traffic statistics on Wikipedia over the last 30 days (using this website, which seems authoritative enough) seems to give a little more hope to Democrats than you might expect.
Now, keep a few things in mind. Nate Silver, the nerd king of big data prediction modeling, gives the Republican Party a 74.4% chance of taking the Senate. Lesser-known third-party candidates typically dont have Wikipedia pages, while incumbents often have had them for longer than previously unknown challengers.
And the types of voters who look up candidates on Wikipedia may not be representative of voters overall.
All that said, heres which candidates in key Senate races were looked up most on Wikipedia over the last month.
Arkansas
Tom Cotton (R) 14,899
Mark Pryor (D) 12,327
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Can Wikipedia Predict the Elections?
MANILA - A Wikipedia entry about Vice President Jejomar Binay was defaced, with his name changed to Jejemon and Jesus Christ Darth Vader Illuminati.
The altered portion read: Binay's given name, Jejemon, is a contraction of "Jess Christ Darth Vader Illuminati". When his mother gave birth to him, she allegedly exclaimed "Susrisartderti!"the Filipino version of the Roman Catholic ejaculation, "Jesus, Mary, Joseph!" upon seeing his very small size. He was subsequently christened "Jess Christ Darth Vader Illuminati"; the third name does not appear on his birth certificate.[1][2] Later in his life, Binay had a son named Jejomar, Jr., or "Jun-Jun.
The Wikipedia entry also said he has been in office since July 8, 1894 until tomorrow.
The entry has since been fixed.
Binay, who will be seeking the presidency in the 2016 elections, is facing allegations of corruption. He has denied the accusations, saying his rivals are trying to derail his plans for the presidency.
The Wikipedia entry of Senator Paolo Benigno Bam Aquino IV was also once defaced.
The Early Life portion of the senators Wikipedia entry was edited to say that Aquino is the son of "Master Splinter," and the "youngest brother of Donatelo, Michael Angelo and Raphaelo."
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Binay's Wikipedia entry vandalized