Open AccessPredicting the Severity of Coronavirus Cases Given Demographics and Pre-existing Conditions
Author(s) -
Mahi Ravi,
Jackie Li,
Vineet Burugu,
Sarvagya Goyal,
Sireesh Pedapenki,
Aditya Goel,
Nandambiar,
Aadhya Subhash,
Larry McMahan
Publication year - 2021
Publication title -
journal of student research
Language(s) - English
Resource type - Journals
ISSN - 2167-1907
DOI - 10.47611/jsrhs.v10i3.1635
Subject(s) - demographics , covid-19 , race (biology) , demography , geography , computer science , medicine , disease , infectious disease (medical specialty) , pathology , outbreak , botany , sociology , biology
Beginning in early 2020, coronavirus disease (COVID-19) has rapidly spread all over the world. As of now there have been over 102.52 million confirmed cases along with 2.21 million deaths worldwide. Our objective is to create an algorithm that will predict the severity of a COVID-19 case for an individual based on demographic data such as race, age, gender, and location. Using international, national and local datasets, we collected the demographic data and organized them into their respective categories, namely age, race, gender, and location of origin. We then inputted this data into an algorithm that works around the principle of probability. Our algorithm uses such trends to develop a risk assessment and create a model. While compiling that data we noted common trends within the three demographics. Specifically, around the age thirty, cases were higher compared to other age ranges. The data collected and trends noted can be used to prioritize and prepare for patients that may be in critical danger, providing a chance for hospitals and vaccine distribution centers to preemptively address higher risk cases early.