Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada.
Bull Math Biol. 2020 Mar 7;82(3):37. doi: 10.1007/s11538-020-00713-2.
Many disease models focus on characterizing the underlying transmission mechanism but make simple, possibly naive assumptions about how infections are reported. In this note, we use a simple deterministic Susceptible-Infected-Removed (SIR) model to compare two common assumptions about disease incidence reports: Individuals can report their infection as soon as they become infected or as soon as they recover. We show that incorrect assumptions about the underlying observation processes can bias estimates of the basic reproduction number and lead to overly narrow confidence intervals.
许多疾病模型侧重于描述潜在的传播机制,但对感染报告的方式做出了简单、可能过于幼稚的假设。在本说明中,我们使用一个简单的确定性易感-感染-移除(SIR)模型来比较两种关于疾病发病率报告的常见假设:个体可以在感染后立即或在康复后立即报告其感染。我们表明,对潜在观察过程的不正确假设会使基本繁殖数的估计产生偏差,并导致置信区间过于狭窄。