Knowing the assays identity (or its characteristics, for assays not represented in our sample), and the time span between the epidemic wave and the serosurvey date at the tested location (which can be estimated from case or death curves), a seroreversion-adjusted sensitivity estimate can be selected from our results. on 50 different seroassays were included in the analysis. Sensitivity decay depended strongly on the antigen and the analytic technique used by the assay, with average sensitivities ranging between 26% and 98% at 6?months after infection, depending on assay characteristics. We found that a third of the included assays departed considerably from manufacturer specifications after 6 months. Conclusions Seroassay sensitivity decay depends on assay characteristics, and for some types of assays, it can make manufacturer specifications highly unreliable. We provide a tool to correct for this phenomenon and to assess the risk of decay for a given assay. Our analysis can guide the design and interpretation of serosurveys for SARS-CoV-2 and other pathogens and quantify systematic biases in the existing serology literature. Keywords: SARS-CoV-2, COVID-19; Serology; Meta-analysis; Serosurveillance; Antibody; Serological assays Key public health message What did you want to address in this study? Knowing how many people get infected with SARS-CoV-2 is important for LDS 751 determining the severity of LDS 751 the virus, herd immunity thresholds and groups at higher LDS 751 risk. To estimate this, results from antibody tests are used. However, antibody Rabbit Polyclonal to NCOA7 levels fall with time after infection, which can make these tests unreliable. We aimed to quantify the reliability of different tests, to understand possible biases in our understanding of COVID-19. What have we learnt from this study? The change of antibody test reliability through time LDS 751 is very variable, and it depends on assay characteristics. Some assays will give strongly biased estimates of infections a couple of months after an epidemic wave, while others will remain reliable for many months. We provide a tool for researchers to assess the risk that an assay will give biased results, and to quantitatively correct for this effect. What are the implications of your findings for public health? Because test reliability changes across time, some antibody tests have the potential to strongly bias our understanding of crucial aspects of COVID-19. As this effect varies depending on the test, the reliability of test results needs to be considered in a test-specific way. For future outbreaks and new infectious diseases, it is important that public health agencies provide guidelines and tools to account for this possible bias. Introduction Throughout the COVID-19 pandemic, policymakers have been guided by the number of past infections inferred from serological assays. Seroassays have been heavily used to estimate the proportion of individuals that have been infected, the rate of fatal or severe infections [1-5] and population-wide immunity [6-8], and to anticipate the effect of future infection waves [9,10], among other purposes. However, antibody levels wane with time after infection [11], reducing the sensitivity of serological assays for detecting previous infections [12-14]. We refer to the decay of assay sensitivity (in the context of serosurveillance) with time after seroconversion as seroreversion (by time, we refer to the time spanned between COVID-19 diagnosis and serological testing). Seroreversion is a major potential source of bias when estimating numbers of infections [1,15,16], and because these estimates guide public health policies such as vaccination programmes, it is important to account for this phenomenon. More broadly, understanding seroreversion in general is important for the management of other emerging infectious diseases. For this, the study of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections presents a unique opportunity. Firstly, an emergent pathogen with distinct symptoms, leading to a high rate of people seeking diagnosis and doctors requesting tests, and short incubation times allows for precise timing of epidemic waves and infections. Secondly, in some cohorts, it can be assumed that reinfections are rare (i.e. serosurveys performed after first epidemic waves). Thirdly, large numbers of serological surveys were performed for SARS-CoV-2 infection, using a wide range of assays and cohorts. These features of the COVID-19 pandemic allow for a rich analysis of seroreversion. Strikingly, there is a lack.