Dynamics of SARS-CoV-2 seroassay sensitivity: a systematic review and modelling study.

BackgroundSerological surveys have been the gold standard to estimate numbers of SARS-CoV-2 infections, the dynamics of the epidemic, and disease severity. Serological assays have decaying sensitivity with time that can bias their results, but there is a lack of guidelines to account for this phenomenon for SARS-CoV-2.AimOur goal was to assess the sensitivity decay of seroassays for detecting SARS-CoV-2 infections, the dependence of this decay on assay characteristics, and to provide a simple method to correct for this phenomenon.MethodsWe performed a systematic review and meta-analysis of SARS-CoV-2 serology studies. We included studies testing previously diagnosed, unvaccinated individuals, and excluded studies of cohorts highly unrepresentative of the general population (e.g. hospitalised patients).ResultsOf the 488 screened studies, 76 studies reporting 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.ConclusionsSeroassay 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.

Assessing the burden of COVID-19 in developing countries: systematic review, meta-analysis and public policy implications.

INTRODUCTION: The infection fatality rate (IFR) of COVID-19 has been carefully measured and analysed in high-income countries, whereas there has been no systematic analysis of age-specific seroprevalence or IFR for developing countries. METHODS: We systematically reviewed the literature to identify all COVID-19 serology studies in developing countries that were conducted using representative samples collected by February 2021. For each of the antibody assays used in these serology studies, we identified data on assay characteristics, including the extent of seroreversion over time. We analysed the serology data using a Bayesian model that incorporates conventional sampling uncertainty as well as uncertainties about assay sensitivity and specificity. We then calculated IFRs using individual case reports or aggregated public health updates, including age-specific estimates whenever feasible. RESULTS: In most locations in developing countries, seroprevalence among older adults was similar to that of younger age cohorts, underscoring the limited capacity that these nations have to protect older age groups.Age-specific IFRs were roughly 2 times higher than in high-income countries. The median value of the population IFR was about 0.5%, similar to that of high-income countries, because disparities in healthcare access were roughly offset by differences in population age structure. CONCLUSION: The burden of COVID-19 is far higher in developing countries than in high-income countries, reflecting a combination of elevated transmission to middle-aged and older adults as well as limited access to adequate healthcare. These results underscore the critical need to ensure medical equity to populations in developing countries through provision of vaccine doses and effective medications.

Age-specific rate of severe and critical SARS-CoV-2 infections estimated with multi-country seroprevalence studies.

BACKGROUND: Knowing the age-specific rates at which individuals infected with SARS-CoV-2 develop severe and critical disease is essential for designing public policy, for infectious disease modeling, and for individual risk evaluation. METHODS: In this study, we present the first estimates of these rates using multi-country serology studies, and public data on hospital admissions and mortality from early to mid-2020. We combine these under a Bayesian framework that accounts for the high heterogeneity between data sources and their respective uncertainties. We also validate our results using an indirect method based on infection fatality rates and hospital mortality data. RESULTS: Our results show that the risk of severe and critical disease increases exponentially with age, but much less steeply than the risk of fatal illness. We also show that our results are consistent across several robustness checks. CONCLUSION: A complete evaluation of the risks of SARS-CoV-2 for health must take non-fatal disease outcomes into account, particularly in young populations where they can be 2 orders of magnitude more frequent than deaths.

Contact tracing-induced Allee effect in disease dynamics.

Contact tracing, case isolation, quarantine, social distancing, and other non-pharmaceutical interventions (NPIs) have been a cornerstone in managing the COVID-19 pandemic. However, their effects on disease dynamics are not fully understood. Saturation of contact tracing caused by the increase of infected individuals has been recognized as a crucial variable by healthcare systems worldwide. Here, we model this saturation process with a mechanistic and a phenomenological model and show that it induces an Allee effect which could determine an infection threshold between two alternative states-containment and outbreak. This transition was considered elsewhere as a response to the strength of NPIs, but here we show that they may be also determined by the number of infected individuals. As a consequence, timing of NPIs implementation and relaxation after containment is critical to their effectiveness. Containment strategies such as vaccination or mobility restriction may interact with contact tracing-induced Allee effect. Each strategy in isolation tends to show diminishing returns, with a less than proportional effect of the intervention on disease containment. However, when combined, their suppressing potential is enhanced. Relaxation of NPIs after disease containment--e.g. because vaccination--have to be performed in attention to avoid crossing the infection threshold required to a novel outbreak. The recognition of a contact tracing-induced Allee effect, its interaction with other NPIs and vaccination, and the existence of tipping points contributes to the understanding of several features of disease dynamics and its response to containment interventions. This knowledge may be of relevance for explaining the dynamics of diseases in different regions and, more importantly, as input for guiding the use of NPIs, vaccination campaigns, and its combination for the management of epidemic outbreaks.