目标:在大多数非洲国家,确诊的COVID-19病例数低估了新的SARS-CoV-2感染病例的数量。我们提出了一个倍增因子,以从确诊病例数中估算出生物学上可能的新感染数量。
方法:2020年3月29日至11月29日在南基伍(DRC)记录的前千例可疑(或警报)病例中的每一例都接受了RT-PCR测试以及IgM和IgG血清学检查。采用潜伏类模型和贝叶斯推断方法,利用RT-PCR和IgM检测结果估计(i)SARS-CoV-2感染的发病率比例,(ii)使用RT-PCR的患病率,IgM和IgG测试结果;和,(iii)倍增因子(发病率比例与确诊-RT-PCR-病例比例的比率)。
结果:在933个具有完整数据的警报案例中,218(23%)RT-PCR+;434(47%)IgM+;464(~50%)RT-PCR+,IgM+,或两者兼有;和647(69%)IgG+或IgM+。SARS-CoV-2感染的发生率估计为58%(95%的可信度区间:51.8-64),患病率为72.83%(65.68-77.89),乘数为2.42(1.95-3.01)。
结论:在监测大流行动态时,生物学上可能的病例数也是有用的。乘数因子有助于近似它。
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.