解读流行病监测数据:浦那市的模型研究

Prathith Bhargav, Soumil Kelkar, Joy Merwin Monteiro, Philip Cherian
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引用次数: 0

摘要

常规流行病学监测数据是流行病发生过程中最连续、最全面的数据来源之一。这些数据被用作流行病学预测模型的输入数据以及公共卫生决策的输入数据,如实施和解除封锁和检疫措施。然而,这些数据是在检测和接触者追踪过程中产生的,而不是通过随机抽样产生的,因此不清楚这些数据对疫情本身的代表性如何。利用 BharatSim 仿真框架,我们建立了一个基于代理的流行病学模型,其中包含测试和接触者追踪的详细算法,代表了浦那市实际采用的策略,从而生成合成监测数据。我们模拟了不同的公共卫生策略、检测和接触追踪效率对生成的监测数据以及疫情进程的影响。我们以浦那市为背景,探讨了生成的监测数据在反映疫情实时状态和决策方面的保真度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpreting epidemiological surveillance data: A modelling study from Pune City
Routine epidemiological surveillance data represents one of the most continuous and comprehensive sources of data during the course of an epidemic. This data is used as inputs to epidemiological forecasting models as well as for public health decision making such as imposition and lifting of lockdowns and quarantine measures. However, such data is generated during testing and contact tracing and not through randomized sampling which makes it unclear how representative such data is of the epidemic itself. Using the BharatSim simulation framework, we build an agent-based epidemiological model with a detailed algorithm of testing and contact tracing representative of actual strategies employed in Pune city to generate synthetic surveillance data. We simulate the impact of different public health strategies, availability of tests and contact tracing efficiencies on the resulting surveillance data as well as on the course of the epidemic. The fidelity of the resulting surveillance data in representing the real-time state of the epidemic and in decision-making is explored in the context of Pune city.
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