ABSTRACT
Measuring the severity of the disease of SARS-CoV-2 is complicated by the lack of valid estimations for the prevalence of infection. Self-administered rapid antigen diagnostic tests (Ag-RDTs) were available in the Netherlands since March 2021, requiring confirmation by reverse-transcription polymerase chain reaction (RT-PCR) for positive results. We explored the possibility of utilizing the positive predictive value (PPV) of Ag-RDTs to estimate SARS-CoV-2 prevalence. We used data from all Public Health service testing facilities between 3 May 2021 and 10 April 2022. The PPV was calculated by dividing the number of positive RT-PCR results by the total number of confirmation tests performed, and used to estimate the prevalence and compared with the number of COVID-19 hospital admissions. In total 3,599,894 cases were included. The overall PPV was 91.8% and 88.8% were symptomatic. During our study period, the estimated prevalence ranged between 2-22% in symptomatic individuals and 2-14% in asymptomatic individuals, with a correlation between the estimated prevalence and hospital admissions two weeks later (r = 0.68 (p<0.01) and r = 0.60 (p<0.01) for symptomatic/asymptomatic individuals). The PPV of Ag-RDTs can help estimate changes in SARS-CoV-2 prevalence, especially when used in conjunction with other surveillance systems. However, the used method probably overestimated the true prevalence because of unmonitored differences in test propensity between individuals.
Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Netherlands/epidemiology , COVID-19/diagnosis , COVID-19/epidemiology , Predictive Value of Tests , Prevalence , Sensitivity and SpecificityABSTRACT
Wastewater-based surveillance enables tracking of SARS-CoV-2 circulation at a local scale in near-real time. Here we investigate the relation between virus loads and the number of hospital admissions in the Netherlands. Inferred virus loads from August 2020 until February 2022 in each of the 344 Dutch municipalities are analysed in a Bayesian multilevel Poisson regression to relate virus loads to daily age-stratified (in groups of 20 years) hospital admissions. Covariates include municipal vaccination coverages stratified by age and dose (first, second, and booster) and prevalence of the circulating coronavirus variants (wildtype, Alpha, Delta, and Omicron (BA.1 and BA.2)). Our model captures the relation between hospital admissions and virus loads well. Estimated hospitalisation rates per 1,000,000 persons per day at a virus load of 1013 particles range from 0.18 (95 % Prediction Interval (PI): 0.046-0.48) in children (0-19 years) to 20.1 (95 % PI: 9.46-36.8) in the oldest age group (80 years and older) in an unvaccinated population with only wildtype SARS-CoV-2 circulation. The analyses indicate a nearly twofold (1.92 (95 % PI: 1.78-2.05)) decrease in the expected number of hospitalisations at a given virus load between the Alpha and the Omicron variant. Our analyses show that virus load estimates in wastewater are closely related to the expected number of hospitalisations and provide an attractive tool to detect increased SARS-CoV-2 circulation at a local scale, even when there are few hospital admissions. Our analyses enable integration of data at the municipality level into meaningful conversion rates to translate virus loads at a local level into expected numbers of hospital admissions, which would allow for a better interpretation of virus loads detected in wastewater.
Subject(s)
COVID-19 , Wastewater , Adult , Aged, 80 and over , Child , Humans , Young Adult , Bayes Theorem , COVID-19/epidemiology , Hospitalization , Hospitals , Netherlands/epidemiology , SARS-CoV-2 , Infant, Newborn , Infant , Child, Preschool , AdolescentABSTRACT
BACKGROUND: Children play a key role in the transmission of many infectious diseases. They have many of their close social encounters at home or at school. We hypothesized that most of the transmission of respiratory infections among children occur in these two settings and that transmission patterns can be predicted by a bipartite network of schools and households. AIM AND METHODS: To confirm transmission over a school-household network, SARS-CoV-2 transmission pairs in children aged 4-17 years were analyzed by study year and primary/secondary school. Cases with symptom onset between 1 March 2021 and 4 April 2021 identified by source and contact-tracing in the Netherlands were included. In this period, primary schools were open and secondary school students attended class at least once per week. Within pairs, spatial distance between the postcodes was calculated as the Euclidean distance. RESULTS: A total of 4059 transmission pairs were identified; 51.9% between primary schoolers; 19.6% between primary and secondary schoolers; 28.5% between secondary schoolers. Most (68.5%) of the transmission for children in the same study year occurred at school. In contrast, most of the transmission of children from different study years (64.3%) and most primary-secondary transmission (81.7%) occurred at home. The average spatial distance between infections was 1.2 km (median 0.4) for primary school pairs, 1.6 km (median 0) for primary-secondary school pairs and 4.1 km (median 1.2) for secondary school pairs. CONCLUSION: The results provide evidence of transmission on a bipartite school-household network. Schools play an important role in transmission within study years, and households play an important role in transmission between study years and between primary and secondary schools. Spatial distance between infections in a transmission pair reflects the smaller school catchment area of primary schools versus secondary schools. Many of these observed patterns likely hold for other respiratory pathogens.
Subject(s)
COVID-19 , SARS-CoV-2 , Child , Humans , COVID-19/epidemiology , COVID-19 Testing , Family Characteristics , SchoolsABSTRACT
BACKGROUND: Vaccines against COVID-19 have proven effective in preventing COVID-19 hospitalisation. In this study, we aimed to quantify part of the public health impact of COVID-19 vaccination by estimating the number of averted hospitalisations. We present results from the beginning of the vaccination campaign ('entire period', January 6, 2021) and a subperiod starting at August 2, 2021 ('subperiod') when all adults had the opportunity to complete their primary series, both until August 30, 2022. METHODS: Using calendar-time specific vaccine effectiveness (VE) estimates and vaccine coverage (VC) by round (primary series, first booster and second booster) and the observed number of COVID-19 associated hospitalisations, we estimated the number of averted hospitalisations per age group for the two study periods. From January 25, 2022, when registration of the indication of hospitalisation started, hospitalisations not causally related to COVID-19 were excluded. RESULTS: In the entire period, an estimated 98,170 (95 % confidence interval (CI) 96,123-99,928) hospitalisations were averted, of which 90,753 (95 % CI 88,790-92,531) were in the subperiod, representing 57.0 % and 67.9 % of all estimated hospital admissions. Estimated averted hospitalisations were lowest for 12-49-year-olds and highest for 70-79-year-olds. More admissions were averted in the Delta period (72.3 %) than in the Omicron period (63.4 %). CONCLUSION: COVID-19 vaccination prevented a large number of hospitalisations. Although the counterfactual of having had no vaccinations while maintaining the same public health measures is unrealistic, these findings underline the public health importance of the vaccination campaign to policy makers and the public.