Automating COVID-19 epidemiological situation reports based on multiple data sources, the Netherlands, 2020 to 2023.

BACKGROUND: During the COVID-19 pandemic, the National Institute for Public Health and the Environment in the Netherlands developed a pipeline of scripts to automate and streamline the production of epidemiological situation reports (epi­sitrep). The pipeline was developed for the Automation of Data Import, Summarization, and Communication (hereafter called the A-DISC pipeline). OBJECTIVE: This paper describes the A-DISC pipeline and provides a customizable scripts template that may be useful for other countries wanting to automate their infectious disease surveillance processes. METHODS: The A-DISC pipeline was developed using the open-source statistical software R. It is organized in four modules: Prepare, Process data, Produce report, and Communicate. The Prepare scripts set the working environment (e.g., load packages). The (data-specific) Process data scripts import, validate, verify, transform, save, analyze, and summarize data as tables and figures and store these data summaries. The Produce report scripts gather summaries from multiple data sources and integrate them into a RMarkdown document - the epi­sitrep. The Communicate scripts send e-mails to stakeholders with the epi­sitrep. RESULTS: As of March 2023, up to ten data sources were automatically summarized into tables and figures by A-DISC. These data summaries were featured in routine extensive COVID-19 epi­sitreps, shared as open data, plotted on RIVM's website, sent to stakeholders and submitted to European Centre for Disease Prevention and Control via the European Surveillance System -TESSy [38]. DISCUSSION: In the face of an unprecedented high number of cases being reported during the COVID-19 pandemic, the A-DISC pipeline was essential to produce frequent and comprehensive epi­sitreps. A-DISC's modular and intuitive structure allowed for the integration of data sources of varying complexities, encouraged collaboration among people with various R-scripting capabilities, and improved data lineage. The A-DISC pipeline remains under active development and is currently being used in modified form for the automatization and professionalization of various other disease surveillance processes at the RIVM, with high acceptance from the participant epidemiologists. CONCLUSION: The A-DISC pipeline is an open-source, robust, and customizable tool for automating epi­sitreps based on multiple data sources.

Neighbourhood sociodemographic factors and COVID-19 vaccine uptake in the Netherlands: an ecological analysis.

BACKGROUND: While overall COVID-19 vaccine uptake is high in the Netherlands, it lags behind in certain subpopulations. AIM: We aimed to explore the characteristics of groups with lower COVID-19 vaccine uptake at neighbourhood level to inform the strategy to improve uptake and guide research into barriers for vaccination. METHODS: We performed an ecological study using national vaccination register and socio-demographic data at neighbourhood level. Using univariate and multivariable generalized additive models we examined the (potentially non-linear) effect of each determinant on uptake. We focused on those aged 50 years and older, since they are at highest risk of severe disease. RESULTS: In those over 50 years of age, a higher proportion of individuals with a non-Western migration background and higher voting proportions for right-wing Christian and conservative political parties were at neighbourhood level univariately associated with lower COVID-19 vaccine uptake. In contrast, higher socioeconomic status and higher voting proportions for right-wing liberal, progressive liberal and Christian middle political parties were associated with higher uptake. Multivariable results differed from univariate results in that a higher voting proportion for progressive left-wing political parties was also associated with higher uptake. In addition, with regard to migration background only a Turkish background remained significant. CONCLUSION: We identified determinants associated with COVID-19 vaccine uptake at neighbourhood level and observed heterogeneity in uptake between different subpopulations. Since the goal of vaccination is not only to reduce suffering and death by improving the average uptake, but also to reduce health inequity, it is important to focus on subpopulations with lower uptake.

Elevated risk of infection with SARS-CoV-2 Beta, Gamma, and Delta variants compared with Alpha variant in vaccinated individuals.

The extent to which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) break through infection- or vaccine-induced immunity is not well understood. We analyzed 28,578 sequenced SARS-CoV-2 samples from individuals with known immune status obtained through national community testing in the Netherlands from March to August 2021. We found evidence of an increased risk of infection by the Beta (B.1.351), Gamma (P.1), or Delta (B.1.617.2) variants compared with the Alpha (B.1.1.7) variant after vaccination. No clear differences were found between vaccines. However, the effect was larger in the first 14 to 59 days after complete vaccination compared with ≥60 days. In contrast to vaccine-induced immunity, there was no increased risk for reinfection with Beta, Gamma, or Delta variants relative to the Alpha variant in individuals with infection-induced immunity.

Vaccine effectiveness against SARS-CoV-2 transmission and infections among household and other close contacts of confirmed cases, the Netherlands, February to May 2021.

Several studies report high effectiveness of COVID-19 vaccines against SARS-CoV-2 infection and severe disease, however an important knowledge gap is the vaccine effectiveness against transmission (VET). We present estimates of the VET to household and other close contacts in the Netherlands, from February to May 2021, using contact monitoring data. The secondary attack rate among household contacts was lower for fully vaccinated than unvaccinated index cases (11% vs 31%), with an adjusted VET of 71% (95% confidence interval: 63-77).