PDF(Pubmed)
Abstract：
UNASSIGNED: Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada.
UNASSIGNED: We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period.
UNASSIGNED: For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test.
UNASSIGNED: Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.
UNASSIGNED: We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period.
UNASSIGNED: For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test.
UNASSIGNED: Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.
摘要：
■公共卫生监测数据并不总是捕获所有病例，部分原因是测试可用性和寻求医疗保健的行为。我们的研究旨在估计多伦多COVID-19报告链中每个步骤的不确定乘数，加拿大。
■我们应用随机模型来估计从2020年3月(大流行开始)到2020年5月23日期间的这些比例，以及在此期间具有不同实验室测试标准的三个不同窗口。
■对于整个期间向多伦多公共卫生报告的每个实验室确认的有症状病例，社区中COVID-19感染的估计数量为18(第5百分位数和第95百分位数:12,29).与漏报最相关的因素是寻求护理的人接受测试的比例。
公共卫生官员应该使用改进的估计，以更好地了解COVID-19和其他类似感染的负担。
■我们应用随机模型来估计从2020年3月(大流行开始)到2020年5月23日期间的这些比例，以及在此期间具有不同实验室测试标准的三个不同窗口。
■对于整个期间向多伦多公共卫生报告的每个实验室确认的有症状病例，社区中COVID-19感染的估计数量为18(第5百分位数和第95百分位数:12,29).与漏报最相关的因素是寻求护理的人接受测试的比例。
公共卫生官员应该使用改进的估计，以更好地了解COVID-19和其他类似感染的负担。
