Rees Erin E, Rodin Rachel, Ogden Nicholas H
Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, St. Hyacinthe, QC and Guelph, ON.
Infectious Disease Prevention and Control Branch, Public Health Agency of Canada, Ottawa, ON.
Can Commun Dis Rep. 2021 Jun 9;47(56):243-250. doi: 10.14745/ccdr.v47i56a01.
To maintain control of the coronavirus disease 2019 (COVID-19) epidemic as lockdowns are lifted, it will be crucial to enhance alternative public health measures. For surveillance, it will be necessary to detect a high proportion of any new cases quickly so that they can be isolated, and people who have been exposed to them traced and quarantined. Here we introduce a mathematical approach that can be used to determine how many samples need to be collected per unit area and unit time to detect new clusters of COVID-19 cases at a stage early enough to control an outbreak.
We present a sample size determination method that uses a relative weighted approach. Given the contribution of COVID-19 test results from sub-populations to detect the disease at a threshold prevalence level to control the outbreak to 1) determine if the expected number of weekly samples provided from current healthcare-based surveillance for respiratory virus infections may provide a sample size that is already adequate to detect new clusters of COVID-19 and, if not, 2) to determine how many additional weekly samples were needed from volunteer sampling.
In a demonstration of our method at the weekly and Canadian provincial and territorial (P/T) levels, we found that only the more populous P/T have sufficient testing numbers from healthcare visits for respiratory illness to detect COVID-19 at our target prevalence level-assumed to be high enough to identify and control new clusters. Furthermore, detection of COVID-19 is most efficient (fewer samples required) when surveillance focuses on healthcare symptomatic testing demand. In the volunteer populations: the higher the contact rates; the higher the expected prevalence level; and the fewer the samples were needed to detect COVID-19 at a predetermined threshold level.
This study introduces a targeted surveillance strategy, combining both passive and active surveillance samples, to determine how many samples to collect per unit area and unit time to detect new clusters of COVID-19 cases. The goal of this strategy is to allow for early enough detection to control an outbreak.
随着封锁措施的解除，为持续控制2019冠状病毒病（COVID-19）疫情，加强其他公共卫生措施至关重要。对于监测而言，迅速检测出高比例的新增病例以便对其进行隔离，并追踪和隔离与之接触的人员非常必要。在此，我们介绍一种数学方法，可用于确定在足够早的阶段检测到COVID-19病例新集群以控制疫情爆发时，每单位面积和单位时间需要采集多少样本。
我们提出一种使用相对加权方法的样本量确定方法。考虑到亚人群的COVID-19检测结果在阈值流行水平下对检测疾病以控制疫情爆发的贡献，以1）确定当前基于医疗保健的呼吸道病毒感染监测每周提供的预期样本数量是否足以提供检测COVID-19病例新集群的样本量，如果不足，则2）确定通过志愿者采样每周还需要多少额外样本。
在我们的方法在每周以及加拿大省级和地区层面的演示中，我们发现只有人口较多的省份和地区有足够的呼吸道疾病就诊检测数量，以我们假定足以识别和控制新集群的目标流行水平来检测COVID-19。此外，当监测集中于医疗保健症状检测需求时，检测COVID-19的效率最高（所需样本较少）。在志愿者人群中：接触率越高；预期流行水平越高；在预定阈值水平下检测COVID-19所需的样本就越少。
本研究引入了一种有针对性的监测策略，结合被动和主动监测样本，以确定每单位面积和单位时间采集多少样本以检测COVID-19病例新集群。该策略的目标是实现足够早的检测以控制疫情爆发。
