Université de Lyon, Lyon, France.
Université Claude Bernard Lyon 1, Villeurbanne, France.
BMC Med Res Methodol. 2023 Nov 17;23(1):272. doi: 10.1186/s12874-023-02077-2.
In most African countries, confirmed COVID-19 case counts underestimate the number of new SARS-CoV-2 infection cases. We propose a multiplying factor to approximate the number of biologically probable new infections from the number of confirmed cases.
Each of the first thousand suspect (or alert) cases recorded in South Kivu (DRC) between 29 March and 29 November 2020 underwent a RT-PCR test and an IgM and IgG serology. A latent class model and a Bayesian inference method were used to estimate (i) the incidence proportion of SARS-CoV-2 infection using RT-PCR and IgM test results, (ii) the prevalence using RT-PCR, IgM and IgG test results; and, (iii) the multiplying factor (ratio of the incidence proportion on the proportion of confirmed -RT-PCR+- cases).
Among 933 alert cases with complete data, 218 (23%) were RT-PCR+; 434 (47%) IgM+; 464 (~ 50%) RT-PCR+, IgM+, or both; and 647 (69%) either IgG + or IgM+. The incidence proportion of SARS-CoV-2 infection was estimated at 58% (95% credibility interval: 51.8-64), its prevalence at 72.83% (65.68-77.89), and the multiplying factor at 2.42 (1.95-3.01).
In monitoring the pandemic dynamics, the number of biologically probable cases is also useful. The multiplying factor helps approximating it.
在大多数非洲国家，确诊的 COVID-19 病例数低估了新出现的 SARS-CoV-2 感染病例数。我们提出了一个乘数，用于根据确诊病例数估算可能出现的新感染病例数。
在 2020 年 3 月 29 日至 11 月 29 日期间，对南基伍（刚果民主共和国）记录的前 1000 例疑似（或警报）病例中的每一例进行 RT-PCR 检测和 IgM 和 IgG 血清学检测。采用潜伏类模型和贝叶斯推断方法，估计（i）使用 RT-PCR 和 IgM 检测结果的 SARS-CoV-2 感染发生率比例，（ii）使用 RT-PCR、IgM 和 IgG 检测结果的患病率；以及（iii）乘数（发病率比例与确诊的 RT-PCR+-病例比例的比值）。
在 933 例具有完整数据的警报病例中，218 例（23%）为 RT-PCR+；434 例（47%）为 IgM+；464 例（~50%）为 RT-PCR+、IgM+或两者兼有；647 例（69%）为 IgG+或 IgM+。SARS-CoV-2 感染的发病率比例估计为 58%（95%置信区间：51.8-64），患病率为 72.83%（65.68-77.89%），乘数为 2.42（1.95-3.01）。
在监测大流行动态时，生物上可能出现的病例数也很有用。乘数有助于对其进行估算。
