Augmenting Epidemiological Models with Point-Of-Care Diagnostics Data
Augmenting Epidemiological Models with Point-Of-Care Diagnostics Data
复制标题
利用即时诊断数据增强流行病学模型
作者：
Özgür Özmen;L. Pullum;A. Ramanathan;J. Nutaro
尽管新型护理点（POC）诊断的采用正在增加，但使用POC诊断数据来改进流行病学模型仍面临重大挑战。在这项工作中，我们提出了一种方法来处理邮政编码水平的POC数据集，并应用这些处理后的数据来校准流行病学模型。我们专门开发了一个校准算法，使用模拟退火和校准一个简约的基于方程的模型修改后的易感-感染-感染（SIR）的动态。结果表明，简约的模型是非常有效的预测感染患者的数量和我们的校准算法是足够的，能够预测峰值负荷POC诊断数据中观察到的，同时保持在合理的和经验的参数范围内，在文献中报道的动态。此外，我们还通过测试人口普查数据中峰值负荷与人口密度之间的相关性，探索了校准值的未来使用。我们的研究结果表明，各种因素之间的关系的线性假设可能会产生误导，因此需要进一步的数据来源和分析，以确定额外的参数和现有的校准之间的关系。像我们这样的校准方法可以确定沿着现有参数的新增加的参数的值，并使决策者能够做出更好的多尺度决策。
Although adoption of newer Point-of-Care (POC) diagnostics is increasing, there is a significant challenge using POC diagnostics data to improve epidemiological models. In this work, we propose a method to process zip-code level POC datasets and apply these processed data to calibrate an epidemiological model. We specifically develop a calibration algorithm using simulated annealing and calibrate a parsimonious equation-based model of modified Susceptible-Infected-Recovered (SIR) dynamics. The results show that parsimonious models are remarkably effective in predicting the dynamics observed in the number of infected patients and our calibration algorithm is sufficiently capable of predicting peak loads observed in POC diagnostics data while staying within reasonable and empirical parameter ranges reported in the literature. Additionally, we explore the future use of the calibrated values by testing the correlation between peak load and population density from Census data. Our results show that linearity assumptions for the relationships among various factors can be misleading, therefore further data sources and analysis are needed to identify relationships between additional parameters and existing calibrated ones. Calibration approaches such as ours can determine the values of newly added parameters along with existing ones and enable policy-makers to make better multi-scale decisions.