标题： 利用信息论准确估计未报告的感染 显示英文标题

标题： Accurately Estimating Unreported Infections using Information Theory

摘要： 对抗传染病传播过程中面临的最重大挑战之一是难以估算感染的真实规模。未报告的感染可能加剧疾病的传播，使得准确估计病原体的传染性变得非常困难，从而妨碍我们有效应对的能力。 尽管使用了基于监测的方法（如血清学研究），确定真实感染规模仍然具有挑战性。 本文提出了一种信息论方法，用于准确估算总感染人数。 我们的方法建立在基于常微分方程（ODE）的模型之上，这些模型在流行病学中通常用于估算此类感染。 我们展示了如何帮助这些模型更好地计算总感染人数，并确定通过最少比特描述报告感染动态的参数化。 我们在 COVID-19 传播方面的实验表明，我们的方法不仅能够显著提高总感染人数估算的准确性，还能比基于标准模型校准的方法提供更好的感染预测。 我们还展示了我们学习到的参数化如何有助于更准确地建模非药物干预下的假设情景。 我们的方法提供了一种改进流行病建模的一般方法，具有广泛适用性。 显示英文摘要

摘要： One of the most significant challenges in combating against the spread of infectious diseases was the difficulty in estimating the true magnitude of infections. Unreported infections could drive up disease spread, making it very hard to accurately estimate the infectivity of the pathogen, therewith hampering our ability to react effectively. Despite the use of surveillance-based methods such as serological studies, identifying the true magnitude is still challenging. This paper proposes an information theoretic approach for accurately estimating the number of total infections. Our approach is built on top of Ordinary Differential Equations (ODE) based models, which are commonly used in epidemiology and for estimating such infections. We show how we can help such models to better compute the number of total infections and identify the parametrization by which we need the fewest bits to describe the observed dynamics of reported infections. Our experiments on COVID-19 spread show that our approach leads to not only substantially better estimates of the number of total infections but also better forecasts of infections than standard model calibration based methods. We additionally show how our learned parametrization helps in modeling more accurate what-if scenarios with non-pharmaceutical interventions. Our approach provides a general method for improving epidemic modeling which is applicable broadly.