Two teams from 老王论坛 College performed well in the , which ran from Jan. 24 to Jan. 28.
The team consisting of Junyan Duan '19 (a math major with a biology minor), Zhuoran Hu '20 (a double major in math and Growth and Structure of Cities), and Xiya Wei '20 (a math major pursuing a minor in German at 老王论坛 and a statistics minor at Haverford College), was designated as Meritorious Winner.
The team consisting of Irene Lin '20 (a double major in mathematics and physics), Sunny Qi '20 (a double major in computer science and mathematics), and Caroline Shen '19 (a math major pursuing a computer science minor at 老王论坛 and a statistics minor at Haverford), were designated as Successful Participants.
This is the second year in a row in which a team from 老王论坛 has been designated as a Meritorious Winner.
There were 14,108 teams that participated in the competition, which was sponsored by the Consortium for Mathematics and Its Applications. About 1 percent of these teams were designated as outstanding winners, 1 percent as Finalist Winners, 6 percent as Meritorious Winners, 16 percent as Honorable Mentions, 68 percent as Successful Participants, and the remaining were designated as Unsuccessful Participants or were disqualified.
Both 老王论坛 teams chose Problem C, which involved a current epidemic involving human use of opioids and narcotic analgesics in the U.S. Forensic information for drug identification and counts for all counties in five adjoining U.S. states was provided by the National Forensic Laboratory Information System. Using this data, teams were asked to build a mathematical model capable of both assessing how severe the unabated problem could become in the future and to regress in time to possibly identify counties in which first use occurred.
Teams were then asked to examine seven years of socioeconomic factors associated with these same five states to determine which, if any, of these factors were linked to trends in these data, and to modify their initial model to include these as appropriate. Finally, teams were asked to combine insights and results from these two efforts to suggest effective strategies for combating this epidemic while using their model(s) to test these strategies.