Age-Specific Estimates of Respiratory Syncytial Virus-Associated Hospitalizations in 6 European Countries: A Time Series Analysis.

BACKGROUND: Knowledge on age-specific hospitalizations associated with RSV infection is limited due to limited testing, especially in older children and adults in whom RSV infections are not expected to be severe. Burden estimates based on RSV coding of hospital admissions are known to underestimate the burden of RSV. We aimed to provide robust and reliable age-specific burden estimates of RSV-associated hospital admissions based on data on respiratory infections from national health registers and laboratory-confirmed cases of RSV. METHODS: We conducted multiseason regression analysis of weekly hospitalizations with respiratory infection and weekly laboratory-confirmed cases of RSV and influenza as covariates, based on national health registers and laboratory databases across 6 European countries. The burden of RSV-associated hospitalizations was estimated by age group, clinical diagnosis, and presence of underlying medical conditions. RESULTS: Across the 6 European countries, hospitalizations of children with respiratory infections were clearly associated with RSV, with associated proportions ranging from 28% to 60% in children younger than 3 months and we found substantial proportions of admissions to hospital with respiratory infections associated with RSV in children younger than 3 years. Associated proportions were highest among hospitalizations with ICD-10 codes of "bronchitis and bronchiolitis." In all 6 countries, annual incidence of RSV-associated hospitalizations was >40 per 1000 persons in the age group 0-2 months. In age group 1-2 years the incidence rate ranged from 1.3 to 10.5 hospitalizations per 1000. Adults older than 85 years had hospitalizations with respiratory infection associated to RSV in all 6 countries although incidence rates were low. CONCLUSIONS: Our findings highlight the substantial proportion of RSV infections among hospital admissions across different ages and may help public health professionals and policy makers when planning prevention and control strategies. In addition, our findings provide valuable insights for health care professionals attending to both children and adults presenting with symptoms of viral respiratory infections.

SARS-CoV-2 Spike S1-specific IgG kinetic profiles following mRNA or vector-based vaccination in the general Dutch population show distinct kinetics.

mRNA- and vector-based vaccines are used at a large scale to prevent COVID-19. We compared Spike S1-specific (S1) IgG antibodies after vaccination with mRNA-based (Comirnaty, Spikevax) or vector-based (Janssen, Vaxzevria) vaccines, using samples from a Dutch nationwide cohort. In adults 18-64 years old (n = 2412), the median vaccination interval between the two doses was 77 days for Vaxzevria (interquartile range, IQR: 69-77), 35 days (28-35) for Comirnaty and 33 days (28-35) for Spikevax. mRNA vaccines induced faster inclines and higher S1 antibodies compared to vector-based vaccines. For all vaccines, one dose resulted in boosting of S1 antibodies in adults with a history of SARS-CoV-2 infection. For Comirnaty, two to four months following the second dose (n = 196), S1 antibodies in adults aged 18-64 years old (436 BAU/mL, IQR: 328-891) were less variable and median concentrations higher compared to those in persons ≥ 80 years old (366, 177-743), but differences were not statistically significant (p > 0.100). Nearly all participants seroconverted following COVID-19 vaccination, including the aging population. These data confirm results from controlled vaccine trials in a general population, including vulnerable groups.

Seropositivity to Nucleoprotein to detect mild and asymptomatic SARS-CoV-2 infections: A complementary tool to detect breakthrough infections after COVID-19 vaccination?

BACKGROUND: With COVID-19 vaccine roll-out ongoing in many countries globally, monitoring of breakthrough infections is of great importance. Antibodies persist in the blood after a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Since COVID-19 vaccines induce immune response to the Spike protein of the virus, which is the main serosurveillance target to date, alternative targets should be explored to distinguish infection from vaccination. METHODS: Multiplex immunoassay data from 1,513 SARS-CoV-2 RT-qPCR-tested individuals (352 positive and 1,161 negative) without COVID-19 vaccination history were used to determine the accuracy of Nucleoprotein-specific immunoglobulin G (IgG) in detecting past SARS-CoV-2 infection. We also described Spike S1 and Nucleoprotein-specific IgG responses in 230 COVID-19 vaccinated individuals (Pfizer/BioNTech). RESULTS: The sensitivity of Nucleoprotein seropositivity was 85% (95% confidence interval: 80-90%) for mild COVID-19 in the first two months following symptom onset. Sensitivity was lower in asymptomatic individuals (67%, 50-81%). Participants who had experienced a SARS-CoV-2 infection up to 11 months preceding vaccination, as assessed by Spike S1 seropositivity or RT-qPCR, produced 2.7-fold higher median levels of IgG to Spike S1 ≥ 14 days after the first dose as compared to those unexposed to SARS-CoV-2 at ≥ 7 days after the second dose (p = 0.011). Nucleoprotein-specific IgG concentrations were not affected by vaccination in infection-naïve participants. CONCLUSIONS: Serological responses to Nucleoprotein may prove helpful in identifying SARS-CoV-2 infections after vaccination. Furthermore, it can help interpret IgG to Spike S1 after COVID-19 vaccination as particularly high responses shortly after vaccination could be explained by prior exposure history.

High Infection Secondary Attack Rates of Severe Acute Respiratory Syndrome Coronavirus 2 in Dutch Households Revealed by Dense Sampling.

BACKGROUND: Indoor environments are considered one of the main settings for transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Households in particular represent a close-contact environment with high probability of transmission between persons of different ages and roles in society. METHODS: Households with a laboratory-confirmed SARS-CoV-2 positive case in the Netherlands (March-May 2020) were included. At least 3 home visits were performed during 4-6 weeks of follow-up, collecting naso- and oropharyngeal swabs, oral fluid, feces and blood samples from all household members for molecular and serological analyses. Symptoms were recorded from 2 weeks before the first visit through to the final visit. Infection secondary attack rates (SAR) were estimated with logistic regression. A transmission model was used to assess household transmission routes. RESULTS: A total of 55 households with 187 household contacts were included. In 17 households no transmission took place; in 11 households all persons were infected. Estimated infection SARs were high, ranging from 35% (95% confidence interval [CI], 24%-46%) in children to 51% (95% CI, 39%-63%) in adults. Estimated transmission rates in the household were high, with reduced susceptibility of children compared with adolescents and adults (0.67; 95% CI, .40-1.1). CONCLUSION: Estimated infection SARs were higher than reported in earlier household studies, presumably owing to our dense sampling protocol. Children were shown to be less susceptible than adults, but the estimated infection SAR in children was still high. Our results reinforce the role of households as one of the main multipliers of SARS-CoV-2 infection in the population.

Associations Between Measures of Social Distancing and Severe Acute Respiratory Syndrome Coronavirus 2 Seropositivity: A Nationwide Population-based Study in the Netherlands.

This large, nationwide, population-based, seroepidemiological study provides evidence of the effectiveness of physical distancing (>1.5 m) and indoor group size reductions in reducing severe acute respiratory syndrome coronavirus 2 infection. Additionally, young adults may play an important role in viral spread, contrary to children up until age 12 years with whom close contact is permitted. CLINICAL TRIALS REGISTRATION: NTR8473.

Persistence of Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 in Relation to Symptoms in a Nationwide Prospective Study.

BACKGROUND: Assessing the duration of immunity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a first priority to gauge the degree of protection following infection. Such knowledge is lacking, especially in the general population. Here, we studied changes in immunoglobulin isotype seropositivity and immunoglobulin G (IgG) binding strength of SARS-CoV-2-specific serum antibodies up to 7 months following onset of symptoms in a nationwide sample. METHODS: Participants from a prospective representative serological study in the Netherlands were included based on IgG seroconversion to the spike S1 protein of SARS-CoV-2 (N = 353), with up to 3 consecutive serum samples per seroconverted participant (N = 738). Immunoglobulin M (IgM), immunoglobulin A (IgA), and IgG antibody concentrations to S1, and increase in IgG avidity in relation to time since onset of disease symptoms, were determined. RESULTS: While SARS-CoV-2-specific IgM and IgA antibodies declined rapidly after the first month after disease onset, specific IgG was still present in 92% (95% confidence interval [CI], 89%-95%) of the participants after 7 months. The estimated 2-fold decrease of IgG antibodies was 158 days (95% CI, 136-189 days). Concentrations were sustained better in persons reporting significant symptoms compared to asymptomatic persons or those with mild upper respiratory complaints only. Similarly, avidity of IgG antibodies for symptomatic persons showed a steeper increase over time compared with persons with mild or no symptoms (P = .022). CONCLUSIONS: SARS-CoV-2-specific IgG antibodies persist and show increasing avidity over time, indicative of underlying immune maturation. These data support development of immune memory against SARS-CoV-2, providing insight into protection of the general unvaccinated part of the population. CLINICAL TRIALS REGISTRATION: NL8473 (the Dutch trial registry).

Controlling the pandemic during the SARS-CoV-2 vaccination rollout.

There is a consensus that mass vaccination against SARS-CoV-2 will ultimately end the COVID-19 pandemic. However, it is not clear when and which control measures can be relaxed during the rollout of vaccination programmes. We investigate relaxation scenarios using an age-structured transmission model that has been fitted to age-specific seroprevalence data, hospital admissions, and projected vaccination coverage for Portugal. Our analyses suggest that the pressing need to restart socioeconomic activities could lead to new pandemic waves, and that substantial control efforts prove necessary throughout 2021. Using knowledge on control measures introduced in 2020, we anticipate that relaxing measures completely or to the extent as in autumn 2020 could launch a wave starting in April 2021. Additional waves could be prevented altogether if measures are relaxed as in summer 2020 or in a step-wise manner throughout 2021. We discuss at which point the control of COVID-19 would be achieved for each scenario.

Estimating the asymptomatic proportion of SARS-CoV-2 infection in the general population: Analysis of nationwide serosurvey data in the Netherlands.

BACKGROUND: The proportion of SARS-CoV-2 positive persons who are asymptomatic-and whether this proportion is age-dependent-are still open research questions. Because an unknown proportion of reported symptoms among SARS-CoV-2 positives will be attributable to another infection or affliction, the observed, or 'crude' proportion without symptoms may underestimate the proportion of persons without symptoms that are caused by SARS-CoV-2 infection. METHODS: Based on two rounds of a large population-based serological study comprising test results on seropositivity and self-reported symptom history conducted in April/May and June/July 2020 in the Netherlands (n = 7517), we estimated the proportion of reported symptoms among those persons infected with SARS-CoV-2 that is attributable to this infection, where the set of relevant symptoms fulfills the ECDC case definition of COVID-19, using inferential methods for the attributable risk (AR). Generalised additive regression modelling was used to estimate the age-dependent relative risk (RR) of reported symptoms, and the AR and asymptomatic proportion (AP) were calculated from the fitted RR. RESULTS: Using age-aggregated data, the 'crude' AP was 37% but the model-estimated AP was 65% (95% CI 63-68%). The estimated AP varied with age, from 74% (95% CI 65-90%) for < 20 years, to 61% (95% CI 57-65%) for the 50-59 years age-group. CONCLUSION: Whereas the 'crude' AP represents a lower bound for the proportion of persons infected with SARS-CoV-2 without COVID-19 symptoms, the AP as estimated via an attributable risk approach represents an upper bound. Age-specific AP estimates can inform the implementation of public health actions such as targetted virological testing and therefore enhance containment strategies.

Nationwide seroprevalence of SARS-CoV-2 and identification of risk factors in the general population of the Netherlands during the first epidemic wave.

BACKGROUND: We aimed to detect SARS-CoV-2 serum antibodies in the general population of the Netherlands and identify risk factors for seropositivity amidst the first COVID-19 epidemic wave. METHODS: Participants (n=3207, aged 2-90 years), enrolled from a previously established nationwide serosurveillance study, provided a self-collected fingerstick blood sample and completed a questionnaire (median inclusion date 3 April 2020). IgG antibodies targeted against the spike S1-protein of SARS-CoV-2 were quantified using a validated multiplex-immunoassay. Seroprevalence was estimated controlling for survey design, individual pre-pandemic concentration, and test performance. Random-effects logistic regression identified risk factors for seropositivity. RESULTS: Overall seroprevalence in the Netherlands was 2.8% (95% CI 2.1 to 3.7), with no differences between sexes or ethnic background, and regionally ranging between 1.3 and 4.0%. Estimates were highest among 18-39 year-olds (4.9%), and lowest in children 2-17 years (1.7%). Multivariable analysis revealed that persons taking immunosuppressants and those from the Orthodox-Reformed Protestant community had over four times higher odds of being seropositive compared to others. Anosmia/ageusia was the most discriminative symptom between seropositive (53%) and seronegative persons (4%, p<0.0001). Antibody concentrations in seropositive persons were significantly higher in those with fever or dyspnoea in contrast to those without (p=0.01 and p=0.04, respectively). CONCLUSIONS: In the midst of the first epidemic wave, 2.8% of the Dutch population was estimated to be infected with SARS-CoV-2, that is, 30 times higher than reported. This study identified independent groups with increased odds for seropositivity that may require specific surveillance measures to guide future protective interventions internationally, including vaccination once available.

Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study.

BACKGROUND: In countries with declining numbers of confirmed cases of COVID-19, lockdown measures are gradually being lifted. However, even if most physical distancing measures are continued, other public health measures will be needed to control the epidemic. Contact tracing via conventional methods or mobile app technology is central to control strategies during de-escalation of physical distancing. We aimed to identify key factors for a contact tracing strategy to be successful. METHODS: We evaluated the impact of timeliness and completeness in various steps of a contact tracing strategy using a stochastic mathematical model with explicit time delays between time of infection and symptom onset, and between symptom onset, diagnosis by testing, and isolation (testing delay). The model also includes tracing of close contacts (eg, household members) and casual contacts, followed by testing regardless of symptoms and isolation if testing positive, with different tracing delays and coverages. We computed effective reproduction numbers of a contact tracing strategy (RCTS) for a population with physical distancing measures and various scenarios for isolation of index cases and tracing and quarantine of their contacts. FINDINGS: For the most optimistic scenario (testing and tracing delays of 0 days and tracing coverage of 100%), and assuming that around 40% of transmissions occur before symptom onset, the model predicts that the estimated effective reproduction number of 1·2 (with physical distancing only) will be reduced to 0·8 (95% CI 0·7-0·9) by adding contact tracing. The model also shows that a similar reduction can be achieved when testing and tracing coverage is reduced to 80% (RCTS 0·8, 95% CI 0·7-1·0). A testing delay of more than 1 day requires the tracing delay to be at most 1 day or tracing coverage to be at least 80% to keep RCTS below 1. With a testing delay of 3 days or longer, even the most efficient strategy cannot reach RCTS values below 1. The effect of minimising tracing delay (eg, with app-based technology) declines with decreasing coverage of app use, but app-based tracing alone remains more effective than conventional tracing alone even with 20% coverage, reducing the reproduction number by 17·6% compared with 2·5%. The proportion of onward transmissions per index case that can be prevented depends on testing and tracing delays, and given a 0-day tracing delay, ranges from up to 79·9% with a 0-day testing delay to 41·8% with a 3-day testing delay and 4·9% with a 7-day testing delay. INTERPRETATION: In our model, minimising testing delay had the largest impact on reducing onward transmissions. Optimising testing and tracing coverage and minimising tracing delays, for instance with app-based technology, further enhanced contact tracing effectiveness, with the potential to prevent up to 80% of all transmissions. Access to testing should therefore be optimised, and mobile app technology might reduce delays in the contact tracing process and optimise contact tracing coverage. FUNDING: ZonMw, Fundação para a Ciência e a Tecnologia, and EU Horizon 2020 RECOVER.