Mathematical Analysis Shows Impact of Subgroup-Based Testing On COVID-19 Positivity Rate

Sameeksha Panda

Overview

One of the most widely known tools used to track the spread of COVID-19 in an area is the overall positivity rate, or the number of positive coronavirus tests over all tests performed. However, different parts of the population, known as subgroups, have different probability rates of infection, the chance of contracting the virus. The purpose of this experiment was to determine whether the overall positivity rate is influenced by different testing strategies of subgroups with different probability rates of infection, which make them either high or low risk, in order to see the overall positivity rate’s accuracy in tracking the spread of COVID-19. For this experiment, the tested subgroups included the children subgroup (0-18; low risk), Residents/Workers of Long Term Facilities (high risk), Essential Medical Personnel/Workers (high risk), Non-Essential Worker And Low Risk Adults (19-64; low risk), Non-Essential Worker And High Risk Adults (19-64; high risk), and Essential Workers (Non-medical; high risk). For the procedures, the experimenter assigned the data, save for the overall positivity rate, the dependent variable, as the purpose was to determine whether different sub-group testing strategies actually had an impact on the overall positivity rate, not to approximate the numerical value of the potential impact. For each of the six experimental testing strategies, the data was put in a mathematical model where the experimenter changed the number of tests allocated to each subgroup to see its effect on the overall positivity rate. The resulting overall positivity rates were 8.55%, 9.75%, 12.50%, 12.38%, 5.00%, and 7.79%, which show that different subgroup testing strategies do indeed have an effect on the overall positivity rate, as these positivity rates all vary. With the results of this experiment, the population will have a stronger understanding of the overall positivity rate’s limitations, and will instead turn to more reliable tracking tools like COVID-19 related deaths and hospitalizations recorded when making their daily decisions, thus helping to combat COVID-19 transmission more accurately.

From the student

Hello! My name is Sameeksha Panda, and I am a current junior at Methacton High School, Eagleville PA (19403)! I love to spend my time as an officer in several STEM clubs, including the MHS Medicine & Science Club, Science Fair Team, and Biology Olympiad. I also am involved as a local student representative for the township Board of Supervisors. I spend much of my time volunteering in locations such as the Cecil & Grace Bean's Soup Kitchen, the American Red Cross, and Skippack Pharmacy as a part of their Fight COVID-19 Task Force, which directly influenced this year's project! I have been participating in science fairs since I was in 2nd grade, and some of my latest projects have revolved around water conservation and solar regeneration. I am honored to continue exploring my scientific passions at various science competitions like the Pennsylvania Junior Academy of Science (PJAS), Montgomery County Science Research Competition, and Delaware Valley Science Fairs, where I have received several awards. I am excited to continue this passion and participation as a first-time AJAS delegate, and learn from my fellow peers, colleagues, and scientists!