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medrxiv; 2023.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2023.01.31.23285233


Background: Recent studies have identified important social inequalities in SARS-CoV-2 infection and related COVID-19 outcomes in the Belgian population. The aim of our study was to investigate the sociodemographic and socioeconomic characteristics associated with the uptake of COVID-19 vaccine in Belgium. Methods: We conducted a cross-sectional analysis of the uptake of a first COVID-19 vaccine dose among 5,342,110 adults ([≥]18 years) in Belgium from December 28th 2020 (official starting date of the vaccination campaign) until August 31st 2021. We integrated data from four national data sources: the Belgian vaccine register (vaccination status), COVID-19 Healthdata (laboratory test results), DEMOBEL (sociodemographic/socioeconomic data), and the Common Base Registry for HealthCare Actors (individuals licensed to practice a healthcare profession in Belgium). We used multivariable logistic regression analysis for identifying characteristics associated with not having obtained a first COVID-19 vaccine dose in Belgium and for each of its three regions (Flanders, Brussels, and Wallonia). Results: During the study period, 10% (536,716/5,342,110) of the Belgian adult population included in our study sample was not vaccinated with a first COVID-19 vaccine dose. A lower COVID-19 vaccine uptake was found among young individuals, men, migrants, single parents, one-person households, and disadvantaged socioeconomic groups (with lower levels of income and education, unemployed). Overall, the sociodemographic and socioeconomic disparities were comparable for all regions. Conclusions: The identification of sociodemographic and socioeconomic disparities in COVID-19 vaccination uptake is critical to develop strategies guaranteeing a more equitable vaccination coverage of the Belgian adult population.

authorea preprints; 2021.


Background: . This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves. Methods: . EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold. Results: . Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online. Interpretation . EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act fast and optimize containment of outbreaks.

Encephalitis, Arbovirus , Syndrome , COVID-19