Center for Statistics, Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
Department of Statistics, University of Pretoria, Pretoria, South Africa.
Front Public Health. 2023 Feb 22;11:979230. doi: 10.3389/fpubh.2023.979230. eCollection 2023.
Identification and isolation of COVID-19 infected persons plays a significant role in the control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in understanding and monitoring the disease transmission and spread for the planning of intervention policy. Using publicly available data collected between March 5th, 2020 and May 31st, 2021, we proposed to estimate both the positive testing rate and its daily rate of change in South Africa with a flexible semi-parametric smoothing model for discrete data. There was a gradual increase in the positive testing rate up to a first peak rate in July, 2020, then a decrease before another peak around mid-December 2020 to mid-January 2021. The proposed semi-parametric smoothing model provides a data driven estimates for both the positive testing rate and its change. We provide an online R dashboard that can be used to estimate the positive rate in any country of interest based on publicly available data. We believe this is a useful tool for both researchers and policymakers for planning intervention and understanding the COVID-19 spread.
识别和隔离 COVID-19 感染者在控制 COVID-19 大流行中发挥着重要作用。一个国家的 COVID-19 阳性检出率有助于了解和监测疾病的传播和扩散，从而为干预政策的制定提供依据。本研究利用 2020 年 3 月 5 日至 2021 年 5 月 31 日期间公开收集的数据，提出了一种灵活的半参数平滑模型来估计南非的阳性检出率及其日变化率。阳性检出率呈逐渐上升趋势，在 2020 年 7 月达到第一个峰值，然后在 2020 年 12 月中旬至 2021 年 1 月中旬再次下降之前，出现了一次下降。所提出的半参数平滑模型为阳性检出率及其变化提供了数据驱动的估计。我们提供了一个在线 R 仪表板，可以根据公开数据来估计任何感兴趣国家的阳性检出率。我们相信，这是研究人员和决策者规划干预措施和了解 COVID-19 传播的有用工具。
