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--- seminars:stat:210513 [2021/04/26 17:06] – created qyu
+++ seminars:stat:210513 [2021/04/26 17:08] (current) – qyu
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+<WRAP 70% center>
+^  **DATE:**|Thursday, May 13, 2021 |
+^  **TIME:**|1:15pm -- 2:15pm |
+^  **LOCATION:**|Zoom meeting |
+^  **SPEAKER:**|Hongshik Ahn, Stony Brook  University |
+^  **TITLE:**|Modeling and computation of multi-step batch testing for infectious diseases  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+We propose a mathematical model based on probability theory to optimize
+COVID-19 testing by a multi-step batch testing approach with variable batch
+sizes. This model and simulation tool dramatically increase the efficiency
+and efficacy of the tests in a large population at a low cost, particularly
+when the infection rate is low. The proposed method combines statistical
+modeling with numerical methods to solve nonlinear equations and obtain
+optimal batch sizes at each step of tests, with the flexibility to
+incorporate geographic and demographic information. In theory, this method
+substantially improves the false positive rate and positive predictive
+value as well. We also conducted a Monte Carlo simulation to verify this
+theory. Our simulation results show that our method significantly reduces
+the false negative rate. More accurate assessment can be made if the
+dilution effect or other practical factors are taken into consideration. The
+proposed method will be particularly useful for the early detection of
+infectious diseases and prevention of future pandemics.  The proposed work
+will have broader impacts on medical testing for contagious diseases in
+general.
+</WRAP>