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Stomata play a central role in surface-atmosphere exchange by controlling the flux of water and CO

Many terrestrial biosphere models depend on an understanding of the relationship between stomatal conductance and photosynthesis. However, unlike the measurement of photosynthetic parameters, such as the maximum carboxylation capacity, where standard methods (e.g., CO2 response or ACi curves) are widely accepted, a consensus method for empirically measuring parameters representing stomatal response has not yet emerged. Most models of stomatal response to environment represent stomatal conductance as being bounded by a lower intercept parameter (g0), and linearly scaled based on a multivariate term described by the stomatal slope parameter (g1). Here we employ the widely used Unified Stomatal Optimization model, to test whether g1 and g0 parameters are impacted by the choice of measurement method, either on an intact branch or a cut branch segment stored in water. We measured paired stomatal response curves on intact and excised branches of a hybrid poplar clone (Populus deltoides Bartr. × Populus nigra L. OP367), measured twice over a diurnal period. We found that predawn branch excision did not significantly affect measured g0 and g1 when measured within 4 h of excision. Measurement in the afternoon resulted in significantly higher values of g1 and lower values of g0, with values changing by 55% and 56%, respectively. Excision combined with afternoon measurement resulted in a marked effect on parameter estimates, with g1 increasing 89% from morning to afternoon and a 25% lower g1 for cut branches than those measured in situ. We also show that in hybrid poplar the differences in parameter estimates obtained from plants measured under different conditions can directly impact models of canopy function, reducing modeled transpiration by 18% over a simulated 12.5-h period. Although these results are only for a single isohydric woody species, our findings suggest that stomatal optimality parameters may not remain constant throughout the day.

Binding antibody levels against SARS-CoV-2 have shown to be correlates of protection against infection with pre-Omicron lineages. This has been challenged by the emergence of immune-evasive variants, notably the Omicron sublineages, in an evolving immune landscape with high levels of cumulative incidence and vaccination coverage. This in turn limits the use of commercially available high-throughput methods to quantify binding antibodies as a tool to monitor protection at the population-level. In this work, we leverage repeated serological measurements between April 2020 and December 2021 on 1’083 participants of a population-based cohort in Geneva, Switzerland, to evaluate anti-Spike RBD antibody levels as a correlate of protection against Omicron BA.1/BA.2 infections during the December 2021-March 2022 epidemic wave. We do so by first modeling antibody dynamics in time with kinetic models. We then use these models to predict antibody trajectories into the time period where Omicron BA.1/BA.2 were the predominant circulating sub-lineages and use survival analyses to compare the hazard of having a positive SARS-CoV-2 test by antibody level, vaccination status and infection history. We find that antibody kinetics in our sample are mainly determined by infection and vaccination history, and to a lesser extent by demographics. After controlling for age and previous infections (based on anti-nucleocapsid serology), survival analyses reveal a significant reduction in the hazard of having a documented positive SARS-CoV-2 infection during the Omicron BA.1/BA.2 wave with increasing antibody levels, reaching up to a three-fold reduction for anti-S antibody levels above 800 IU/mL (HR 0.30, 95% CI 0.22-0.41). However, we did not detect a reduction in hazard among uninfected participants. Taken together these results indicate that anti-Spike RBD antibody levels, as quantified by the immunoassay used in this study, are an indirect correlate of protection against Omicron BA.1/BA.2 for individuals with a history of previous SARS-CoV-2 infection. Despite the uncertainty in what SARS-COV-2 variant will come next, these results provide reassuring insights into the continued interpretation of SARS-CoV-2 binding antibody measurements as an independent marker of protection at both the individual and population levels.

Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. We provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.

Understanding seasonal variation in photosynthesis is important for understanding and modeling plant productivity. Here, we used shotgun sampling to examine physiological, structural and spectral leaf traits of upper canopy, sun-exposed leaves in Quercus coccinea Münchh (scarlet oak) across the growing season in order to understand seasonal trends, explore the mechanisms underpinning physiological change and investigate the impact of extrapolating measurements from a single date to the whole season. We tested the hypothesis that photosynthetic rates and capacities would peak at the summer solstice, i.e., at the time of peak photoperiod. Contrary to expectations, our results reveal a late-season peak in both photosynthetic capacity and rate before the expected sharp decrease at the start of senescence. This late-season maximum occurred after the higher summer temperatures and vapor pressure deficit and was correlated with the recovery of leaf water content and increased stomatal conductance. We modeled photosynthesis at the top of the canopy and found that the simulated results closely tracked the maximum carboxylation capacity of Rubisco. For both photosynthetic capacity and modeled top-of-canopy photosynthesis, the maximum value was therefore not observed at the summer solstice. Rather, in each case, the measurements at and around the solstice were close to the overall seasonal mean, with values later in the season leading to deviations from the mean by up to 41 and 52%, respectively. Overall, we found that the expected Gaussian pattern of photosynthesis was not observed. We conclude that an understanding of species- and environment-specific changes in photosynthesis across the season is essential for correct estimation of seasonal photosynthetic capacity.

The representation of stomatal regulation of transpiration and CO

Stomata play a central role in regulating the exchange of carbon dioxide and water vapor between ecosystems and the atmosphere. Their function is represented in land surface models (LSMs) by conductance models. The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is a dynamic vegetation demography model that can simulate both detailed plant demographic and physiological dynamics. To evaluate the effect of stomatal conductance model formulation on forest water and carbon fluxes in FATES, we implemented an optimality-based stomatal conductance model – the Medlyn (MED) model – that simulates the relationship between photosynthesis (A) and stomatal conductance to water vapor (gsw) as an alternative to the FATES default Ball–Woodrow–Berry (BWB) model. To evaluate how the behavior of FATES is affected by stomatal model choice, we conducted a model sensitivity analysis to explore the response of gsw to climate forcing, including atmospheric CO2 concentration, air temperature, radiation, and vapor pressure deficit in the air (VPDa). We found that modeled gsw values varied greatly between the BWB and MED formulations due to the different default stomatal slope parameters (g1). After harmonizing g1 and holding the stomatal intercept parameter (g0) constant for both model formulations, we found that the divergence in modeled gsw was limited to conditions when the VPDa exceeded 1.5 kPa. We then evaluated model simulation results against measurements from a wet evergreen forest in Panama. Results showed that both the MED and BWB model formulations were able to capture the magnitude and diurnal changes of measured gsw and A but underestimated both by about 30 % when the soil was predicted to be very dry. Comparison of modeled soil water content from FATES to a reanalysis product showed that FATES captured soil drying well, but translation of drying soil to modeled physiology reduced the models' ability to match observations. Our study suggests that the parameterization of stomatal conductance models and current model response to drought are the critical areas for improving model simulation of CO2 and water fluxes in tropical forests.

BackgroundIt is now established that a significant proportion of adults experience persistent symptoms after SARS-CoV-2 infection. However, evidence for children and adolescents is still inconclusive. In this population-based study, we examine the proportion of children and adolescents reporting persistent symptoms after SARS-CoV-2 infection, as assessed by serological status, and compare this to a seronegative control group.MethodsWe conducted a serosurvey in June-July 2021, recruiting 660 children and adolescents from 391 households selected randomly from the Geneva population. We tested participants for anti-SARS-CoV-2 antibodies targeting the nucleocapsid (N) protein to determine previous infection. A parent filled a questionnaire including questions on COVID-19-related symptoms lasting at least 2 weeks.FindingsAmong children seropositive for anti-SARS-CoV-2 antibodies, the sex- and age-adjusted prevalence of symptoms lasting longer than two weeks was 18.3%, compared to 11.1% among seronegative children (prevalence difference (ΔaPrev)=7.2%, 95%CI:1.5-13.0). Main symptoms declared among seropositive children were fatigue (11.5%) and headache (11.1%). For 8.6% (aPrev, 95%CI: 4.7-12.5) of seropositives, these symptoms were declared to be highly limiting of daily activities. Adolescents aged 12-17 years had a higher adjusted prevalence of persistent symptoms (aPrev=29.1%, 95%CI:19.4-38.7) than younger children. Comparing seropositive and seronegative adolescents, the estimated prevalence of symptoms lasting over four weeks is 4.4% (ΔaPrev, 95%CI:-3.8-13.6).InterpretationA significant proportion of children aged 12 to 17 years had symptoms lasting over two weeks after SARS-CoV-2 infection, with an estimated prevalence of symptoms lasting over 4 weeks of 4.4% in this age group. This represents a large number of adolescents in absolute terms, and should raise concern in the context of unknown long-term evolution of symptoms. Younger children appear to experience long-lasting symptoms less frequently, as no difference was observed between the seropositive and seronegative sample. Further studies with larger samples sizes are needed.FundingSwiss Federal Office of Public Health, Geneva General Directorate of Health, HUG Private Foundation, SSPH+, Fondation des Grangettes.

More than two years into the COVID-19 pandemic, most of the population has developed anti-SARS-CoV-2 antibodies from infection and/or vaccination. However, public health decision-making is hindered by the lack of up-to-date and precise characterization of the immune landscape in the population. Here, we estimated anti-SARS-CoV-2 antibodies seroprevalence and cross-variant neutralization capacity after Omicron became dominant in Geneva, Switzerland.We conducted a population-based serosurvey between April 29 and June 9, 2022, recruiting children and adults of all ages from age-stratified random samples of the general population of Geneva, Switzerland. We tested for anti-SARS-CoV-2 antibodies using commercial immunoassays targeting either the spike (S) or nucleocapsid (N) protein, and for antibody neutralization capacity against different SARS-CoV-2 variants using a cell-free Spike trimer-ACE2 binding-based surrogate neutralization assay. We estimated seroprevalence and neutralization capacity using a Bayesian modeling framework accounting for the demographics, vaccination, and infection statuses of the Geneva population.Among the 2521 individuals included in the analysis, the estimated total antibodies seroprevalence was 93.8% (95% CrI 93.1-94.5), including 72.4% (70.0-74.7) for infection-induced antibodies. Estimates of neutralizing antibodies in a representative subsample (N = 1160) ranged from 79.5% (77.1-81.8) against the Alpha variant to 46.7% (43.0-50.4) against the Omicron BA.4/BA.5 subvariants. Despite having high seroprevalence of infection-induced antibodies (76.7% [69.7-83.0] for ages 0-5 years, 90.5% [86.5-94.1] for ages 6-11 years), children aged12 years had substantially lower neutralizing activity than older participants, particularly against Omicron subvariants. Overall, vaccination was associated with higher neutralizing activity against pre-Omicron variants. Vaccine booster alongside recent infection was associated with higher neutralizing activity against Omicron subvariants.While most of the Geneva population has developed anti-SARS-CoV-2 antibodies through vaccination and/or infection, less than half has neutralizing activity against the currently circulating Omicron BA.5 subvariant. Hybrid immunity obtained through booster vaccination and infection confers the greatest neutralization capacity, including against Omicron.General Directorate of Health in Geneva canton, Private Foundation of the Geneva University Hospitals, European Commission ("CoVICIS" grant), and a private foundation advised by CARIGEST SA.

BackgroundUp-to-date seroprevalence estimates are critical to describe the SARS-CoV-2 immune landscape in the population and guide public health measures. We aimed to estimate the seroprevalence of anti-SARS-CoV-2 antibodies 15 months into the COVID-19 pandemic and six months into the vaccination campaign.MethodsWe conducted a population-based cross-sectional serosurvey between June 1 and July 7, 2021, recruiting participants from age- and sex-stratified random samples of the general population. We tested participants for anti-SARS-CoV-2 antibodies targeting the spike (S) or nucleocapsid (N) proteins (Roche Elecsys immunoassays). We estimated the anti-SARS-CoV-2 antibodies seroprevalence following vaccination and/or infection (anti-S antibodies), or infection only (anti-N antibodies).ResultsWe included 3355 individuals, of which 1814 (54.1%) were women, 697 (20.8%) were aged ConclusionsMost adults have developed anti-SARS-CoV-2 antibodies, while most teenagers and children remain vulnerable to infection. As the SARS-CoV-2 Delta variant spreads and vaccination rates stagnate, efforts are needed to address vaccine hesitancy, particularly among younger individuals and socioeconomically disadvantaged groups, and to minimize spread among children.

Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO2 absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to estimate the CO2 absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO2 exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO2 fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration (Rdark25), the maximum rate of carboxylation by the enzyme Rubisco (Vcmax25), the maximum rate of electron transport (Jmax25), and the triose phosphate utilization rate (Tp25), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for Vcmax25, Jmax25, Tp25 and Rdark25 (RMSE of 13, 20, 1.5 and 0.3 μmol m-2 s-1, and R2 of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.

A primary source of uncertainty in terrestrial biosphere model (TBM) projection of carbon uptake and water cycling from ecosystems is the relationship between CO2 assimilation (A) and wa...

Background Up-to-date seroprevalence estimates are critical to describe the SARS-CoV-2 immune landscape and to guide public health decisions. Aim We estimate seroprevalence of anti-SARS-CoV-2 antibodies 15 months into the COVID-19 pandemic and 6 months into the vaccination campaign. Methods We conducted a population-based cross-sectional serosurvey between 1 June and 7 July 2021, recruiting participants from age- and sex-stratified random samples of the general population. We tested participants for anti-SARS-CoV-2 antibodies targeting the spike (S) or nucleocapsid (N) proteins using the Roche Elecsys immunoassays. We estimated the anti-SARS-CoV-2 antibodies seroprevalence following vaccination and/or infection (anti-S antibodies), or infection only (anti-N antibodies). Results Among 3,355 individuals (54.1% women; 20.8% aged Conclusion Most of the population has developed anti-SARS-CoV-2 antibodies, despite most teenagers and children remaining vulnerable to infection. As the SARS-CoV-2 Delta variant spreads and vaccination rates stagnate, efforts are needed to address vaccine hesitancy, particularly among younger individuals and to minimise spread among children.

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system. publishedVersion

Protocol for predicting leaf traits from leaf reflectance spectra, using partial least-squares regression (PLSR).

International audience; Precision agriculture for banana crops has been little investigated so far. The main difficulty to implement precision agriculture methods lies in the asynchronicity of this crop: after a few cycles, each plant has its own development stage in the field. It results in a diversity of the phenological stages within a field and also a continuous production of bananas over time. Therefore, maps of agronomic interest derived from plant responses are difficult to produce using existing methods. This study proposes a mapping approach that handles the diversity of phenological stages and the temporal continuity of production. It explores the feasibility of applying this general approach to a plant response parameter which is the time between flowering and maturity (time to harvest) of banana denoted tfm. The tfm gives an insight into the spatial distribution of vigour. The study was conducted using a large database (more than 395 000 observations) generated by two commercial farms in 2015 and 2016 in Cameroon. The temporal variability of tfm, which is induced by meteorological and operational constraints, and the spatial variability, which is assumed to be due to environmental factors, was assessed by decomposing the tfm variance. This method allowed mapping of the effect of the temporal variability as well as the effect of agri-environmental variables on tfm using a block kriging method. Spatial structures highlighted by this decomposition either at the farm level or at the field level, suggest that the map of the effect of environmental factors on tfm could be used to support agronomic decisions. This idea is reinforced by the identification of factors explaining the environmental variability of tfm and by the temporal stability of the spatial structures.

Super-resolution methods in literature has in recent years been dominated by convolutional neural networks (CNNs), aiming to learn a direct mapping from a low to high resolution image. Although successful, these methods rely on large-scale and high-quality datasets, to learn more powerful models. The scale of the existing super-resolution datasets are limiting the performance of current deep and highly complex architectures. Moreover, current datasets are severely limited in terms of resolution, prohibiting the move towards more extreme conditions with high upscaling factors. In this paper, we introduce the DIVerse 8K resolution image dataset (DIV8K). The dataset contains a over 1500 images with a resolution up to 8K. It highly covers diverse scene contents. It is therefore the ideal dataset for training and benchmarking super-resolution approaches, applicable to extreme upscaling factors of 32x and beyond. The dataset was employed for the AIM 2019 Image Extreme Super Resolution Challenge.

International audience; The objective of this study was to evaluate the effect of banana phenological stages on the chlorophyll content of its leaves in order to assess possible restrictions on the potential use of remote sensing for nutritional diagnoses when phenological stages cannot be discriminated on images. Chlorophyll measurements were made with a Dualex sensor at different positions on banana leaves with different ranks on the plant, on plants at different phenological stages and located in different fields of a commercial farm. The effect of these factors was studied using mixed models with nested factors and the results showed that they have a significant effect on the chlorophyll content. It is therefore suggested to take into account the diversity of phenological stages in banana fields before considering nutritional diagnoses services based on remote sensing.

International audience; A specific processing chain has been designed to build leaf chlorophyll content (LCC) prediction maps in banana fields using multispectral UAV imagery. A supervised regression model was first calibrated out-of-field using multispectral images of collected leaves laid on the ground, and then applied to build a LCC ortho-mosaic for a complete field using the same multispectral sensor. Though the quality of the resulting LCC map was limited mainly due to light scattering inside the canopy, a significant correlation with within-field vigor heterogeneity has been found, making this approach promising for further banana production management.

The world we live in is an increasingly spatial and temporal data-rich environment, and agriculture is no exception. However, data needs to be processed in order to first get information and then make informed management decisions. The concepts of &lsquo, Precision Agriculture&rsquo, and &lsquo, Smart Agriculture&rsquo, are and will be fully effective when methods and tools are available to practitioners to support this transformation. An open-source software called GeoFIS has been designed with this objective. It was designed to cover the whole process from spatial data to spatial information and decision support. The purpose of this paper is to evaluate the abilities of GeoFIS along with its embedded algorithms to address the main features required by farmers, advisors, or spatial analysts when dealing with precision agriculture data. Three case studies are investigated in the paper: (i) mapping of the spatial variability in the data, (ii) evaluation and cross-comparison of the opportunity for site-specific management in multiple fields, and (iii) delineation of within-field zones for variable-rate applications when these latter are considered opportune. These case studies were applied to three contrasting crop types, banana, wheat and vineyards. These were chosen to highlight the diversity of applications and data characteristics that might be handled with GeoFIS. For each case-study, up-to-date algorithms arising from research studies and implemented in GeoFIS were used to process these precision agriculture data. Areas for future development and possible relations with existing geographic information systems (GIS) software is also discussed.

Precision agriculture for banana crops has been little investigated so far. The main difficulty to implement precision agriculture methods lies in the asynchronicity of this crop: after a few cycles, each plant has its own development stage in the field. Indeed, maps of agronomical interest are difficult to produce from plant responses without implementing new methods. The present study explores the feasibility to derive a spatially relevant indicator from the date offlowering and the date of maturity (time to harvest). The time between these dates (TFM) may give insight in spatial distribution of vigor. The study was carried out using production data from 2015 acquired in a farm from Cameroon. Data from individual plants that flowered at different weeks were gathered so as to increase the density of TFM sampling. The temporal variability of TFM, which is induced by weather and operational constraints, was compensated by centering TFM data on their medians (TFMc). The mapping of TFMc was obtained using a classical kriging method. Spatial structures highlighted by TFMc either at the farm level or at the plot level, suggest that such maps could be used to support agronomic decisions.

Introduction The COVID-19 pandemic has affected billions of people around the world both directly through the infection itself and indirectly through its economic, social and sanitary impact. Collecting data over time is essential for the understanding of the disease spread, the incidence of COVID-19-like symptoms, the level and dynamics of immunity, as well as the long-term impact of the pandemic. The objective of the study was to set up a longitudinal follow-up of adult participants of serosurveys carried out in the canton of Geneva, Switzerland, during the COVID-19 pandemic. This follow-up aims at monitoring COVID-19 related symptoms and SARS-CoV-2 seroconversion, as well as the overall impact of the pandemic on several dimensions of health and on socioeconomic factors over a period of at least 2 years., Methods and analysis Serosurvey participants were invited to create an account on the dedicated digital platform Specchio-COVID19 (https://www.specchio-covid19.ch/) . On registration, an initial questionnaire assessed sociodemographic and lifestyle characteristics (including housing conditions, physical activity, diet, alcohol and tobacco consumption), anthropometry, general health and experience related to COVID-19 (symptoms, COVID-19 test results, quarantines, hospitalisations). Weekly, participants were invited to fill in a short questionnaire with updates on self-reported COVID-19-compatible symptoms, SARS-CoV-2 infection testing and vaccination. A more detailed questionnaire about mental health, wellbeing, risk perception and changes in working conditions was proposed monthly. Supplementary questionnaires were proposed at regular intervals to assess more in depth the impact of the pandemic on physical and mental health, vaccination adherence, healthcare consumption and changes in health behaviours. At baseline, serology testing allowed to assess the spread of SARS-CoV-2 infection among the general population and subgroups of workers. Additionally, seropositive participants and a sample of randomly selected participants were invited for serologic testing at regular intervals in order to monitor both the seropersistance of anti-SARS-CoV-2 antibodies and the seroprevalence of anti-SARS-CoV-2 antibodies in the population of the canton of Geneva., Ethics and dissemination The study was approved by the Cantonal Research Ethics Commission of Geneva, Switzerland (CCER Project ID 2020-00881). Results will be disseminated in a variety of ways, via the SpecchioCOVID-19 platform, social media posts, press releases and through regular scientific dissemination methods (openaccess articles, conferences).