Covid-19 ER Diagram
Tags: datascience
Published: 2020-09-07 00:28:54

#Hey, everyone!

###     Covid-19 has had a devastating impact on the state of the world in the past year. It's reasonable to assume the fact that every single person, at least in the developed world, has been personally affected by the pandemic in some way. The extent to which we've been affected can vary greatly from the loss of a loved one, to the loss of a job, to even just the loss of being able to go to your favorite restaurant. Just as varied are the responses that people have taken to the threats posed by Covid-19. There are those who doubt any claims of its dangers or its existence and then there are those who believe that this pandemic is the end of the world as we know it.

###     With the virus, there's a lot of information flooding the internet that can make it hard for people to properly understand the scale of what's happening in the world. As data scientists and those knowledgeable enough to parse through so much information, we have a social responsibility to help others understand the information being thrown at them. Below is a simple entity-relationship diagram, ER diagram for short, that can be used to understand how different factors on the issue come together as a system.

![IST210 Lab2 Diagram](https://dev-to-uploads.s3.amazonaws.com/i/pwezllg858un3y39rj9e.PNG)

###   The keystone of this diagram is of course the individual, labelled patient. With a virus, people are the aspect of the system that we are most concerned about. From a systematic point of view, we don't need to know everything about an individual to understand the virus's affect on us but there are important factors such as sex, race, age, and how healthy the person that can help us understand their situation as well as a form of identification for the individual. Directly related to the individual, is their personal habits that affect both their general health and their likely of getting the virus. The most influential factors I thought of were if they attended large parties, social distanced, dined out, exercised regularly, and wore a mask when in public. 

###    The next factor I thought would be good to model is the occupation of the individual. More specifically I wanted to track what type of interactions the individual would have at their job, such as the frequency of their interactions with others and how intimate those interactions were. Just as important would be the scale of safety procedures in place at their job. After jobs, I added a field for locations to account for the general surroundings of the individual. This includes broader information such as country, city, and basic information about the virus in these locations. The location entry directly ties into the individual in terms of where they live and work, but also the next category of hospitals. Hospitals are obviously a key factor when trying to analyze a virus including the availability of the hospital by location, capacity, and how ready they are to treat patients. 

###     Lastly I added the broad risk entity that ties many entities together and helps to contextualize how the other fields interact. Being such a broad category I included a diverse topic of attributes such as the chance of someone being infected which directly ties into the number of people they've had interactions with and their closest contacts. I also decided to add the severity of their particular case as well as the treatment options available to them. Below I've included a small sample of possible examples for each aspect of the model.
 
![IST210 Lab2 Samples](https://dev-to-uploads.s3.amazonaws.com/i/xhfiqty8vw8k6mnw8sf4.PNG)

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