论文标题
估计大流行病的抽样方案
A sampling scheme for estimating the prevalence of a pandemic
论文作者
论文摘要
COVID-19的传播使研究其流行率至关重要。据我们所知，在此研究研究中，广泛使用的抽样方法没有充分使用有关先前诊断的病例数量的信息，该信息提供了有关感染真实数量的先验信息。这促使我们在本文中开发了一种新的两阶段抽样方法，该方法利用有关人口和诊断病例的分布的信息，以更有效地调查患病率。我们的采样策略中使用了全局可能性采样，是一种从任何概率密度函数中绘制样品的强大而有效的采样器，因此，我们的新方法可以自动适应种群和病例的复杂分布。此外，相应的估计方法很简单，这有助于实际实施。给出了一些有关实施的建议。最后，一些模拟和一个实际的例子验证了其效率。
The spread of COVID-19 makes it essential to investigate its prevalence. In such investigation research, as far as we know, the widely-used sampling methods didn't use the information sufficiently about the numbers of the previously diagnosed cases, which provides a priori information about the true numbers of infections. This motivates us to develop a new, two-stage sampling method in this paper, which utilises the information about the distributions of both population and diagnosed cases, to investigate the prevalence more efficiently. The global likelihood sampling, a robust and efficient sampler to draw samples from any probability density function, is used in our sampling strategy, and thus, our new method can automatically adapt to the complicated distributions of population and cases. Moreover, the corresponding estimating method is simple, which facilitates the practical implementation. Some recommendations for practical implementation are given. Finally, several simulations and a practical example verified its efficiency.
