论文标题
高效率和功效多步批次测试的传染病的建模和计算
Modeling and Computation of High Efficiency and Efficacy Multi-Step Batch Testing for Infectious Diseases
论文作者
论文摘要
我们提出了一个基于概率理论的数学模型，以通过可变批量大小的多步批次测试方法优化COVID-19测试。该模型和仿真工具大大提高了大量人群中测试的效率和功效，尤其是在感染率较低时。所提出的方法将统计建模与数值方法相结合，以求解非线性方程并在测试的每个步骤中获得最佳批量大小，并具有合并地理和人口统计信息的灵活性。从理论上讲，该方法显着提高了假阳性速率和阳性预测价值。我们还进行了蒙特卡洛模拟以验证这一理论。我们的仿真结果表明，我们的方法大大降低了假负率。如果考虑稀释效应或其他实际因素，则可以进行更准确的评估。提出的方法将对早期发现传染病和预防未来大流行的方法特别有用。拟议的工作将对传染性疾病的医学测试产生更大的影响。
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.
