Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset

Abstract

Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA data that inform exposure and shedding, but extensive variation in assays, study group demographics and laboratory protocols across published studies confounds inference of true biological patterns. Our meta-analysis leverages 3,214 datapoints from 516 individuals in 21 studies to reveal that seroconversion of both IgG and IgM occurs around 12 days post symptom onset (range 1-40), with extensive individual variation that is not significantly associated with disease severity. IgG and IgM detection probabilities increase from roughly 10% at symptom onset to 98-100% by day 22, after which IgM wanes while IgG remains reliably detectable. RNA detection probability decreases from roughly 90% to zero by day 30, and is highest in faeces and lower respiratory tract samples. Our findings provide a coherent evidence base for interpreting clinical diagnostics, and for the mathematical models and serological surveys that underpin public health policies.

COVIDStrategyCalculator: A software to assess testing- and quarantine strategies for incoming travelers, contact person management and de-isolation

“…The software allows full flexibility with regards to parameter choices, that, for example, determine the time-course of infection, the proportion of asymptomatic cases and the test sensitivity, etc., and can thus be tailored to user-specific queries. We have however carefully calibrated the models' default parameters to reproduce published and inhouse clinical data on the incubation time 53 , the off-set of infectiousness after peak virus load/symptom onset [54][55][56]59 , as well as the time-dependent test sensitivities 57,58 . Figure 2A shows the cumulative time-to-symptom-onset (grey shaded area) compiled in a meta-analysis of 56 studies 53 , together with the model-predictions (solid-and dashed lines) using the default parameters.…”

“…We adjusted the models' default parameters to each study individually (Supplementary Note 2) and derived parameter ranges that capture the entire range of infectivity profiles, emphasizing on the tail of the distribution, which is most important to accurately capture the waning off of infectiousness. Figure 2C shows the decrease of detection probability 57 , whereas Figure 2D shows the reported time-dependent false omission rate FOR(t) of the PCR diagnostics (shaded areas) 58 , as well as respective model-predicted dynamics with default parameters (lines). As shown, the model captures the time-dependent assay sensitivity reasonably well with default parameters.…”

“…The software was designed to provide maximum flexibility to the user combined with intuitive operability. The underlying mathematical models and -methods are entirely novel and were calibrated to reproduce the spectrum of clinically observed infection dynamics from in-house and published studies [53][54][55][56][57][58][59] .The software thus synthesizes the current state of knowledge on within-host infection dynamics and utilizes it to enable the rational, evidence-based design of non-pharmaceutical control strategies. Given that the underlying models reproduce the statistical attributes of population dynamics, we see the COVIDStrategyCalculator 's prime field of application in providing rational, evidence-based guidance to policy makers determining test, quarantine and isolation strategies at national and subnational levels.…”

“…The software allows full flexibility with regards to parameter choices, that, for example, determine the time-course of infection, the proportion of asymptomatic cases and the test sensitivity and much more. However, a set of default parameters is provided, that has been carefully derived by fitting the model's parameters to available clinical and in-house data on the incubation time 53 , the off-set of infectiousness after symptom onset or peak virus load [54][55][56] , as well as the time-dependent test sensitivities 57,58 . Details on the parameter fitting procedure and analysis of infectivity profiles are provided in Supplementary Note 2-3.…”

“…Relative infectiousness after symptom onset/peak viral load extracted from Singanayagam et al and van Kampen et al 55,56 , deduced from in-house data (Supplementary Note 3) and derived from viral load kinetics reported by Ejima et al 54 59 are shown as shaded areas, whereas model-predicted infectiousness profiles are depicted by lines (solid line: typical dynamics, dashed lines: lower and upper extremes). C. Time-dependent PCR sensitivity after symptom onset reported by Borremans et al 57 (error bars) together with model simulated PCR sensitivity using default parameters (solid line: typical dynamics, dashed lines: lower and upper extremes). D. Time-dependent false omission rate as reported by Kucirca et al 58 (shaded area).…”