Predicting the Severity of Coronavirus Cases Given Demographics and Pre-existing Conditions


  • Mahi Ravi Student
  • Jackie Li
  • Vineet Burugu
  • Sarvagya Goyal
  • Sireesh Pedapenki
  • Aditya Goel
  • Nandana Nambiar
  • Aadhya Subhash
  • Larry McMahan Mentor, ASDRP



coronavirus, COVID-19, severity, demographics


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. 


Download data is not yet available.

References or Bibliography

Center, Johns Hopkins University & Medicine, 2020,

Yao, Haochen, et al. “Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the Blood and Urine Tests.” Frontiers, Frontiers, 6 July 2020,

Li, Lin, et al. “Using Artificial Intelligence to Detect COVID-19 and Community-Acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.” Radiology, Radiological Society of North America, 19 Mar. 2020,

Jiang, X., Coffee, M., Bari, A., Wang, J., Jiang, X. et al. (2020). Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity. CMC-Computers, Materials & Continua, 63(1), 537–551.

‌, 2021, Accessed 17 Jan. 2021.

‌“Coronavirus (COVID-19) Data Dashboard - Novel Coronavirus (COVID-19) - County of Santa Clara.”,

‌“Data | COVID-19 | Alameda County Public Health.”, Accessed 17 Jan. 2021.

CDC. “COVID-19 Cases, Deaths, and Trends in the US | CDC COVID Data Tracker.” Centers for Disease Control and Prevention, 28 Mar. 2020,

“Clinical Records, China National Center for Bioinformation.” Clinical Records, CNCB , 2019 Novel Coronavirus Resource (2019nCoVR), 20 Jan. 2021,



How to Cite

Ravi, M., Li, J., Burugu, V., Goyal, S., Pedapenki, S., Goel, A. ., Nambiar, N., Subhash, A., & McMahan, L. (2021). Predicting the Severity of Coronavirus Cases Given Demographics and Pre-existing Conditions . Journal of Student Research, 10(3).



HS Research Articles