Difference between revisions of "Covid-19 Research"

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*'''<u>Don't judge an article by its title (or its abstract!)</u>''': Although you should search for scientific articles by category, title, and abstract, you should not simply read the title or an abstract of an article expect to know everything about it. You should read it in its entirety so that you get the complete picture and intended message.
 
*'''<u>Don't judge an article by its title (or its abstract!)</u>''': Although you should search for scientific articles by category, title, and abstract, you should not simply read the title or an abstract of an article expect to know everything about it. You should read it in its entirety so that you get the complete picture and intended message.
 
*'''<u>Approach articles with skepticism</u>''': Be critical of the evidence provided in the article and try not to draw your own conclusions or accept the conclusions presented until you have read many articles on the subject.
 
*'''<u>Approach articles with skepticism</u>''': Be critical of the evidence provided in the article and try not to draw your own conclusions or accept the conclusions presented until you have read many articles on the subject.
*'''<u>Be careful when drawing conclusions from statistics</u>''': Ronald Coase has been credited with saying: "If you torture the data enough, nature will always confess." What this means is that you should avoid reading too much into Covid-19 statistics (or ''any'' statistics for that matter) and avoid making far-fetched implications. Statistics is a great tool for extracting meaningful information and finding patterns in a large body of data. However, statistics are only as meaningful as the accuracy and complexity of the model. And remember, ''correlation does not imply causation''.
+
*'''<u>Be careful when drawing conclusions from statistics</u>''': Ronald Coase has been credited with saying: "If you torture the data enough, nature will always confess." What this means is that you should avoid reading too much into Covid-19 statistics (or ''any'' statistics for that matter) and avoid making decisive conclusions. Statistics is a great tool for extracting meaningful information and finding patterns in a large body of data. However, statistics are only as meaningful as the accuracy and complexity of the model. And remember, ''correlation does not imply causation''.
 +
*'''<u>Keep track of the publication date</u>''': Because Covid-19 research is rapidly expanding (for obvious reasons), research articles may become out-of-date and unreliable quickly. Although you should read as many articles as possible on Covid-19 regardless of its publication date, you should stay up-to-date by reading the latest articles on the subject.
  
 
== Scientific Articles ==
 
== Scientific Articles ==

Revision as of 22:29, 26 April 2020

Note: This page is under construction and will be updated with the latest info by the end of this week. It will then be updated on a ~daily basis.

Introduction and Guidelines

The purpose of this page is to provide the latest facts on Coronavirus disease 2019 (Covid-19 or colloquially the "coronavirus") using research directly from scientific articles. With many secondary sources being inaccurate (or misleading at best), it is more important than ever to relay scientific research as unbiased as possible and to be as thorough as possible. Of course, in practice, it is impossible to be completely unbiased in conveying information, so my best advice in using this page is to use the guidelines below:

  • Don't judge an article by its title (or its abstract!): Although you should search for scientific articles by category, title, and abstract, you should not simply read the title or an abstract of an article expect to know everything about it. You should read it in its entirety so that you get the complete picture and intended message.
  • Approach articles with skepticism: Be critical of the evidence provided in the article and try not to draw your own conclusions or accept the conclusions presented until you have read many articles on the subject.
  • Be careful when drawing conclusions from statistics: Ronald Coase has been credited with saying: "If you torture the data enough, nature will always confess." What this means is that you should avoid reading too much into Covid-19 statistics (or any statistics for that matter) and avoid making decisive conclusions. Statistics is a great tool for extracting meaningful information and finding patterns in a large body of data. However, statistics are only as meaningful as the accuracy and complexity of the model. And remember, correlation does not imply causation.
  • Keep track of the publication date: Because Covid-19 research is rapidly expanding (for obvious reasons), research articles may become out-of-date and unreliable quickly. Although you should read as many articles as possible on Covid-19 regardless of its publication date, you should stay up-to-date by reading the latest articles on the subject.

Scientific Articles

General Info and Literature Review Articles

Virus Description and Mechanism of Action

Case Reporting and Testing

Treatment and Vaccine

Societal and Economic Impact

Sources of Articles

Note that since Covid-19 is a such a rapidly expanding and important area of scientific research, many articles on the subject are free to the public. If an article is not free, two asterisks (**) will be to the left of the title. However, you may still be able to access it through your institution.

Also, please note that this page only provides scientific articles and does NOT provide statistics or sources to statistics on Covid-19 due to the risk of spreading misleading information.

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