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BackgroundSurveillance of SARS-CoV-2 in wastewater offers an unbiased and near real-time tool to track circulation of SARS-CoV-2 at a local scale, next to other epidemic indicators such as hospital admissions and test data. However, individual measurements of SARS-CoV-2 in sewage are noisy, inherently variable, and can be left-censored. AimWe aimed to infer latent virus loads in a comprehensive sewage surveillance program that includes all sewage treatment plants (STPs) in the Netherlands and covers 99.6% of the Dutch population. MethodsA multilevel Bayesian penalized spline model was developed and applied to estimate time- and STP-specific virus loads based on water flow adjusted SARS-CoV-2 qRT-PCR data from 1-4 sewage samples per week for each of the >300 STPs. ResultsThe model provided an adequate fit to the data and captured the epidemic upsurges and downturns in the Netherlands, despite substantial day-to-day measurement variation. Estimated STP virus loads varied by more than two orders of magnitude, from approximately 1012 (virus particles per 100,000 persons per day) in the epidemic trough in August 2020 to almost 1015 in many STPs in January 2022. Epidemics at the local levels were slightly shifted between STPs and municipalities, which resulted in less pronounced peaks and troughs at the national level. ConclusionAlthough substantial day-to-day variation is observed in virus load measurements, wastewater-based surveillance of SARS-CoV-2 can track long-term epidemic progression at a local scale in near real-time, especially at high sampling frequency.
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We propose a mathematical framework to analyze and interpret the outcomes of human challenge trials. We present plausible infection risks with HCoV-229E and SARS-CoV-2 over a wide range of infectious dose, and suggest ways to improve the design of future trials and to translate its outcomes to the general population. One sentence summaryWe rephrase dose-response models in terms of heterogeneity in susceptibility in order to present the possible range of infection risks for endemic coronaviruses and SARS-CoV-2
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IntroductionDespite the high COVID-19 vaccination coverage among adults, there is concern over a peak in SARS-CoV-2 infections in the coming months. To help ensure that healthcare systems are not overwhelmed in the event of a new wave of SARS-CoV-2 infections, many countries have extended vaccination to adolescents (those aged 12-17 years) and may consider further extending to children aged 5-11 years. However, there is considerable debate about whether or not to vaccinate healthy adolescents and children against SARS-CoV-2 because, while vaccination of children and adolescents may limit transmission from these groups to other, more vulnerable groups, adolescents and children themselves have limited risk of severe disease if infected and may experience adverse events from vaccination. To quantify the benefits of extending COVID-19 vaccination beyond adults we compare daily cases, hospital admissions, and intensive care (IC) admissions for vaccination in adults only, those 12 years and above, and those 5 years and above. Methods and FindingsWe developed a deterministic, age-structured susceptible-exposed-infectious-recovered (SEIR) model to simulate disease outcomes (e.g., cases, hospital admissions, IC admissions) under different vaccination scenarios. The model is partitioned into 10-year age bands (0-9, 10-19, ..., 70-79, 80+) and accounts for differences in susceptibility and infectiousness by age group, seasonality in transmission rate, modes of vaccine protection (e.g., infection, transmission), and vaccine characteristics (e.g., vaccine effectiveness). Model parameters are estimated by fitting the model piecewise to daily cases from the Dutch notification database Osiris from 01 January 2020 to 22 June 2021. Forward simulations are performed from 22 June 2021 to 31 March 2022. We performed sensitivity analyses in which vaccine-induced immunity waned. We found that upon relaxation of all non-pharmaceutical control measures a large wave occurred regardless of vaccination strategy. We found overall reductions of 5.7% (4.4%, 6.9%) of cases, 2.0% (0.7%, 3.2%) of hospital admissions, and 1.7% (0.6%, 2.8%) of IC admissions when those 12 years and above were vaccinated compared to vaccinating only adults. When those 5 years and above were vaccinated we observed reductions of 8.7% (7.5%, 9.9%) of cases, 3.2% (2.0%, 4.5%) of hospital admissions, and 2.4% (1.2%, 3.5%) of IC admissions compared to vaccination in adults only. Benefits of extending vaccination were larger within the age groups included in the vaccination program extension than in other age groups. The benefits of vaccinating adolescents and children were smaller if vaccine protection against infection, hospitalization, and transmission (once infected) wanes. DiscussionOur results highlight the benefits of extending COVID-19 vaccination programs beyond adults to reduce infections and severe outcomes in adolescents and children and in the wider population. A reduction of infections in school-aged children/adolescents may have the added benefit of reducing the need for school closures during a new wave. Additional control measures may be required in future to prevent a large wave despite vaccination program extensions. While the results presented here are based on population characteristics and the COVID-19 vaccination program in The Netherlands, they may provide valuable insights for other countries who are considering COVID-19 vaccination program extensions.
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For the control of COVID-19, vaccination programmes provide a long-term solution. The amount of available vaccines is often limited, and thus it is crucial to determine the allocation strategy. While mathematical modelling approaches have been used to find an optimal distribution of vaccines, there is an excessively large number of possible schemes to be simulated. Here, we propose an algorithm to find a near-optimal allocation scheme given an intervention objective such as minimization of new infections, hospitalizations, or deaths, where multiple vaccines are available. The proposed principle for allocating vaccines is to target subgroups with the largest reduction in the outcome of interest, such as new infections, due to vaccination that fully immunizes a single individual. We express the expected impact of vaccinating each subgroup in terms of the observed incidence of infection and force of infection. The proposed approach is firstly evaluated with a simulated epidemic and then applied to the epidemiological data on COVID-19 in the Netherlands. Our results reveal how the optimal allocation depends on the objective of infection control. In the case of COVID-19, if we wish to minimize deaths, the optimal allocation strategy is not efficient for minimizing other outcomes, such as infections. In simulated epidemics, an allocation strategy optimized for an outcome outperforms other strategies such as the allocation from young to old, from old to young, and at random. Our simulations clarify that the current policy in the Netherlands (i.e., allocation from old to young) was concordant with the allocation scheme that minimizes deaths. The proposed method provides an optimal allocation scheme, given routine surveillance data that reflect ongoing transmissions. The principle of allocation is useful for providing plausible simulation scenarios for complex models, which give a more robust basis to determine intervention strategies. Author summaryVaccination is the key to controlling the ongoing COVID-19 pandemic. In the early stages of an epidemic, there is shortage of vaccine stocks. Here, we propose an algorithm that computes an optimal vaccine distribution among groups for each intervention objective (e.g., minimizing new infections, hospitalizations, or deaths). Unlike existing approaches that use detailed information on at-risk contacts between and among groups, the proposed algorithm requires only routine surveillance data on the number of cases. This method is applicable even when multiple vaccines are available. Simulation results show that the allocation scheme optimized by our algorithm performed the best compared with other strategies such as allocating vaccines at random and in the order of age. Our results also reveal that an allocation scheme optimized for one specific objective is not necessarily efficient for another, indicating the importance of the decision-making at the early phase of distributions.
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The true number of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is difficult to estimate using a case-reporting system (i.e., passive surveillance) alone because of asymptomatic infection. While wastewater-based epidemiology has been implemented as an alternative/additional monitoring tool to reduce reporting bias, the relationship between passive and wastewater surveillance data has yet to be explicitly examined. Since there is strong age dependency in the symptomatic ratio of SARS-CoV-2 infections, this study aimed to estimate i) an age-dependent association between the number of reported cases and the viral load in wastewater and ii) the time lag between those time series. The viral load in wastewater was modeled as a combination of contributions from different age groups virus shedding, incorporating the delay, and fitted with daily case count data collected from the Massachusetts Department of Public Health and SARS-CoV-2 RNA concentrations in wastewater collected by the Massachusetts Water Resources Authority. The estimated lag between the time series of viral loads in wastewater and of reported cases was 10.8 days (95% confidence interval =[10.2, 11.6]) for wastewater treatment plants northern area and 8.8 days [8.4, 9.1] for southern area. The estimated contribution rate of a reported case to the viral load in wastewater in the 0-19 yr age group was 0.38 [0.35, 0.41] for northern area and 0.40 [0.37, 0.43] for southern area, that in the 80+ yr age group was 0.67 [0.65, 0.69] for northern area and 0.51 [0.49, 0.52] for southern area. The estimated lag between those time series suggested the predictability of reported cases ten days later using viral loads in wastewater. The contribution of a reported case in passive surveillance to the viral load in wastewater differed by age, suggesting a large variation in viral shedding kinetics among age groups.
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BackgroundThe 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. MethodsBased on a large population-based serological study comprising test results on seropositivity and self-reported symptom history conducted in April/May 2020 in the Netherlands (n=3147), 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. ResultsUsing age-aggregated data, the estimated AP was 70% (95% CI: 65-77%). The estimated AP decreased with age, from 80% (95% CI: 67-100%) for the <20 years age-group, to 55% (95% CI: 48-68%) for the 70+ years age-group. ConclusionWhereas 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.
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BackgroundWastewater-based epidemiology (WBE) is one of the most promising approaches to effectively monitor the spread of the novel coronavirus disease 2019 (COVID-19). The virus concentration in faeces and its temporal variations are essential information for WBE. While some clinical studies have reported severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) concentrations in faeces, the value varies amongst patients and changes over time. AimThe present study aimed to examine how the temporal variations in the concentration of virus in faeces affect the monitoring of disease incidence. We re-analysed the experimental findings of clinical studies to estimate the duration of virus shedding and the faecal virus concentration. MethodAvailable experimental data as of 23 October, 2020 were collected and patient data reported in Germany were included for further analysis. The viral shedding kinetics was modelled, and the dynamic model was fitted to the collected experimental data by a Bayesian framework. Using samples of posterior distributions, the duration of viral shedding and the concentration of virus copies in faeces over time were computed. ResultsWe estimated the median concentration of SARS-CoV-2 in faeces as 2.6 (95% Credible Interval (CrI): 0.22-4.8) log copies per gram (g) of faeces over the shedding period, and our model implied that the duration of viral shedding was 23.2 days (95% CrI: 19.5-31.5), given the current standard quantification limit (Ct = 40). With simulated incidences, our results also indicated that a one-week delay between symptom onset and wastewater sampling increased the estimation of incidence by 13.5%. ConclusionsOur results demonstrated that the temporal variation in virus concentration in faeces affects microbial monitoring systems such as WBE. The present study also implied the need for adjusting the estimates of virus concentration in faeces by incorporating the kinetics of unobserved concentrations. The method used in this study is easily implemented in further simulations; therefore, the results of this study might contribute to enhancing disease surveillance and risk assessments that require quantities of virus to be excreted into the environment.
