In the last year many public health decisions were based on real-time monitoring the spread of the ongoing COVID-19 pandemic. For this one often considers the reproduction number which measures the amount of secondary cases produced by a single infectious individual. While estimates of this quantity are readily available on the national level, subnational estimates, e.g. on the county level, pose more difficulties since only few incidences occur there. However, as countermeasures to the pandemic are usually enforced on the subnational level, such estimates are of great interest to assess the efficacy of the measures taken, and to guide future policy. We present a novel extension of the well established estimator of the country level reproduction number to the county level by applying techniques from small-area estimation. This new estimator yields sensible estimates of reproduction numbers both on the country and county level. It can handle low and highly variable case counts on the county level, and may be used to distinguish local outbreaks from more widespread ones. We demonstrate the capabilities of our novel estimator by a simulation study and by applying the estimator to German case data.
翻译:去年,许多公共卫生决定是根据实时监测正在发生的COVID-19流行病的蔓延情况作出的。关于这一点,我们经常考虑用来衡量单个感染者所生二级病例数量的复制数。虽然这一数量的估计数可以随时在国家一级得到,但国家以下一级的估计数,例如县一级的估计数,却造成更多的困难,因为那里发生的病例很少。然而,由于该流行病的防治措施通常是在国家以下一级执行的,因此这种估计数对于评估所采取措施的效力和指导未来政策非常有意义。我们通过应用小地区估计的技术,将国家一级复制数的既定估计数重新扩大到县一级。这个新的估计数得出了国家和县一级复制数的合理估计数。它可以处理县一级低和高度可变的案件数,并可用于区分地方爆发和较广泛的疾病。我们通过模拟研究和将估计数应用于德国案例数据,展示了我们的新估计数的能力。