Covid-19 Testing Game
You are the director of a Covid-19 Testing Center. Because of the high demand for testing kits across the country, you received a limited number of them. You are trying to find out a strategy to use the available kits effectively.
The objective of this game is to utilize the limited number of kits you received to the fullest extent. In other words, you should test only those patients who are likely to have the virus and shouldn't test those who are not likely to have it.
When you decide to test a positive patient or not to test a negative patient, it is a success for you. However, when you decide to test a negative patient or not to test a positive patient, it is your failure.
The game consists of fifty random patients. When a patient arrives, you have information about their age, gender, income group, whether they have a history of asthma, and how much is the fever currently.
At the end of the game, your score is the percentage of the right decisions you have made.
Try to look at the various attributes of the patients and see if there is any pattern exhibited in terms of what value of which attributes influence the likelihood of being positive the most.
The objective of this game is to utilize the limited number of kits you received to the fullest extent. In other words, you should test only those patients who are likely to have the virus and shouldn't test those who are not likely to have it.
When you decide to test a positive patient or not to test a negative patient, it is a success for you. However, when you decide to test a negative patient or not to test a positive patient, it is your failure.
The game consists of fifty random patients. When a patient arrives, you have information about their age, gender, income group, whether they have a history of asthma, and how much is the fever currently.
At the end of the game, your score is the percentage of the right decisions you have made.
Try to look at the various attributes of the patients and see if there is any pattern exhibited in terms of what value of which attributes influence the likelihood of being positive the most.
Patient#:
Age:
Gender:
Income Group:
History of Asthma?
Fever:
°F
Decision:
Actual Status of Patient:
Total Number of Patients:
Testing Accuracy:
%
Data:
Age:
Gender:
Income Group:
History of Asthma?
Fever:
°F
Decision:
Actual Status of Patient:
Total Number of Patients:
Testing Accuracy:
%
Data:
Copyright © 2019 Abhimanyu Gupta. All rights reserved.
