Analyzing inherent biases in SARS-CoV-2 PCR and serological epidemiologic metrics

Posted on 14.05.2022 - 05:10
Abstract Background Prospective observational data show that infected persons with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain polymerase chain reaction (PCR) positive for a prolonged duration, and that detectable antibodies develop slowly with time. We aimed to analyze how these effects can bias key epidemiological metrics used to track and monitor SARS-CoV-2 epidemics. Methods An age-structured mathematical model was constructed to simulate progression of SARS-CoV-2 epidemics in populations. PCR testing to diagnose infection and cross-sectional surveys to measure seroprevalence were also simulated. Analyses were conducted on simulated outcomes assuming a natural epidemic time course and an epidemic in presence of interventions. Results The prolonged PCR positivity biased the epidemiological measures. There was a lag of 10 days between the true epidemic peak and the actually-observed peak. Prior to epidemic peak, PCR positivity rate was twofold higher than that based only on current active infection, and half of those tested positive by PCR were in the prolonged PCR positivity stage after infection clearance. Post epidemic peak, PCR positivity rate poorly predicted true trend in active infection. Meanwhile, the prolonged PCR positivity did not appreciably bias estimation of the basic reproduction number R0. The time delay in development of detectable antibodies biased measured seroprevalence. The actually-observed seroprevalence substantially underestimated true prevalence of ever infection, with the underestimation being most pronounced around epidemic peak. Conclusions Caution is warranted in interpreting PCR and serological testing data, and any drawn inferences need to factor the effects of the investigated biases for an accurate assessment of epidemic dynamics.


Makhoul, Monia; Abou-Hijleh, Farah; Seedat, Shaheen; Mumtaz, Ghina R.; Chemaitelly, Hiam; Ayoub, Houssein; et al. (2022): Analyzing inherent biases in SARS-CoV-2 PCR and serological epidemiologic metrics. figshare. Collection.
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