Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, SAR, China.
Centre for Applied One Health Research and Policy Advice, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, SAR, China.
BMC Infect Dis. 2022 Mar 20;22(1):271. doi: 10.1186/s12879-022-07190-z.
During the COVID-19 outbreak in Taiwan between May 11 and June 20, 2021, the observed fatality rate (FR) was 5.3%, higher than the global average at 2.1%. The high number of reported deaths suggests that many patients were not treated promptly or effectively. However, many unexplained deaths were subsequently identified as cases, indicating a few undetected cases, resulting in a higher estimate of FR. Whether the true FR is exceedingly high and what factors determine the detection of cases remain unknown. Estimating the true number of total infected cases (i.e. including undetected cases) can allow an accurate estimation of FR and effective reproduction number ([Formula: see text]).
We aimed at quantifying the time-varying FR and [Formula: see text] using the estimated true numbers of cases; and, exploring the relationship between the true case number and test and trace data. After adjusting for reporting delays, we developed a model to estimate the number of undetected cases using reported deaths that were and were not previously detected. The daily FR and [Formula: see text] were calculated using the true number of cases. Afterwards, a logistic regression model was used to assess the impact of daily testing and tracing data on the detection ratio of deaths.
The estimated true daily case number at the peak of the outbreak on May 22 was 897, which was 24.3% higher than the reported number, but the difference became less than 4% on June 9 and afterwards. After taking account of undetected cases, our estimated mean FR (4.7%) was still high but the daily rate showed a large decrease from 6.5% on May 19 to 2.8% on June 6. [Formula: see text] reached a maximum value of 6.4 on May 11, compared to 6.0 estimated using the reported case number. The decreasing proportion of undetected cases was found to be associated with the increases in the ratio of the number of tests conducted to reported cases, and the proportion of cases that are contact traced before symptom onset.
Increasing testing capacity and contact tracing coverage without delays not only improve parameter estimation by reducing hidden cases but may also reduce fatality rates.
2021 年 5 月 11 日至 6 月 20 日台湾地区 COVID-19 疫情期间，观察到的病死率（FR）为 5.3%，高于全球平均水平 2.1%。大量报告的死亡人数表明，许多患者没有得到及时或有效的治疗。然而，随后发现许多不明原因的死亡被确认为病例，表明有一些未被发现的病例，导致 FR 估计值较高。真实 FR 是否过高，以及哪些因素决定病例的检出，尚不清楚。估计总感染病例（即包括未检出病例）的真实数量，可以准确估计 FR 和有效繁殖数([Formula: see text])。
我们旨在使用估计的真实病例数量来量化时变 FR 和[Formula: see text]；并探索真实病例数量与检测和追踪数据之间的关系。在调整报告延迟后，我们使用报告的死亡病例来建立一个模型，该模型用于估计未检出病例的数量，这些死亡病例之前已被检测到或未被检测到。使用真实病例数量计算每日 FR 和[Formula: see text]。之后，使用逻辑回归模型评估每日检测和追踪数据对死亡检出率的影响。
疫情高峰期 5 月 22 日估计的真实每日病例数为 897，比报告的数字高 24.3%，但到 6 月 9 日及以后，这一差异小于 4%。考虑到未检出病例后，我们估计的平均 FR（4.7%）仍然较高，但日率从 5 月 19 日的 6.5%大幅下降到 6 月 6 日的 2.8%。[Formula: see text]在 5 月 11 日达到最大值 6.4，而使用报告病例数估计为 6.0。未检出病例的减少比例与检测数量与报告病例数量之比的增加以及发病前接受接触追踪的病例比例的增加有关。
在没有延迟的情况下增加检测能力和接触追踪覆盖范围，不仅可以通过减少隐藏病例来改善参数估计，还可以降低病死率。
