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This work presents a joint spatial modeling framework to improve estimation of the spatial distribution of the latent COVID-19 incidence in Belgium, based on test-confirmed COVID-19 cases and crowd-sourced symptoms data as reported in a large-scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID-19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide valuable auxiliary data to optimize spatial COVID-19 incidence estimation based on confirmed cases in situations with limited testing capacity. Furthermore, it is shown that an online survey on COVID-19 symptoms with a sufficiently large sample size per spatial entity is capable of pinpointing the same locations that appear as test-confirmed clusters, approximately 1 week earlier. We conclude that a large-scale online study provides an inexpensive and flexible method to collect timely information of an epidemic during its early phase, which can be used by policy makers in an early phase of an epidemic and in conjunction with other monitoring systems. The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government. The data used in this manuscript were provided by the Belgian public health institute (Sciensano). This research received funding from the Flemish Government (AI Research Program). Authors Beutels, Faes, and Hens acknowledge funding from the European Union’s Horizon 2020 research and innovation programme - project EpiPose (No. 101003688). Authors Beutels, Hens, Neyens, and Van Damme acknowledge funding from the Research Foundation Flanders (No. G0G1920N). Authors Faes and Neyens acknowledge funding from the Research Foundation Flanders (No. G0G9820N). Author Neyens acknowledges funding by the Internal Funds KU Leuven (project number 3M190682).

Understanding the local dynamics of COVID-19 transmission calls for an approach that characterizes the incidence curve in a small geographical unit. Given that incidence curves exhibit considerable day-to-day variation, the fractal structure of the time series dynamics is investigated for the Flanders and Brussels Regions of Belgium. For each statistical sector, the smallest administrative geographical entity in Belgium, fractal dimensions of COVID-19 incidence rates, based on rolling time spans of 7, 14, and 21 days were estimated using four different estimators: box-count, Hall-Wood, variogram, and madogram. We found varying patterns of fractal dimensions across time and location. The fractal dimension is further summarized by its mean, variance, and autocorrelation over time. These summary statistics are then used to cluster regions with different incidence rate patterns using k-means clustering. Fractal dimension analysis of COVID-19 incidence thus offers important insight into the past, current, and arguably future evolution of an infectious disease outbreak. ispartof: SCIENTIFIC REPORTS vol:13 issue:1 ispartof: location:England status: published

Introduction: Long term care facilities for elderly (LTCFs) in Europe encountered a high disease burden at the start of the COVID-19 pandemic. Therefore, these facilities were the first to receive COVID-19 vaccines in many European countries. A limited COVID-19 vaccine supply early 2021 resulted in a majority of residents and healthcare workers (HCWs) in LTCFs being vaccinated compared to a minority in the general population. This study exploits this imbalance to assess the efficiency of COVID-19 vaccination in containing outbreaks in LTCFs. Methods: Exploratory statistics were performed using data from a COVID-19 surveillance system covering all 842 LTCFs in Flanders (the northern region of Belgium). The number and size of COVID-19 outbreaks in LTCFs were compared (1) before and after introducing vaccines and (2) with the status of the pandemic in the general population. Based on individual data from 15 LTCFs, the infection rate and symptoms of vaccinated and unvaccinated residents and HCWs were compared during a COVID-19 outbreak. Results: 95.8% of the residents and 90.9% of the HCWs in Flemish LTCFs were vaccinated before May 30, 2021. Before vaccine introduction, residents in LTCFs were 10 times more likely to test positive for COVID-19 than the general population of Flanders. This ratio reversed after vaccination. Furthermore, after vaccination fewer and shorter outbreaks were observed involving fewer residents. During these outbreaks, vaccinated and unvaccinated residents were equally likely to test positive, but positive vaccinated residents were less likely to develop severe symptoms. In contrast, unvaccinated HCWs were more likely to test positive. Conclusion: In the first half of 2021, two-dose vaccination was highly efficient in preventing and containing outbreaks in LTCFs, reducing COVID-19 hospitalizations and deaths. The high likelihood of unvaccinated HCWs to be involved in COVID-19 outbreaks in vaccinated LTCFs emphasizes the importance of vaccinating HCWs. (c) 2022 Elsevier Ltd. All rights reserved. TN gratefully acknowledges funding by the Internal Funds KU Leuven (project number 3M190682). NH acknowledges support from the European Union’s Horizon 2020 research and innovation program - project EpiPose (No 101003688) and from the Flemish Research Fund (FWO 1150017N), BV acknowledges support from Ghent University Special Research Fund (COV021-20 BOF) and from the Flemish Research Fund (G.0H44.20N). The funders had no role in study design, data collection, data analysis, data interpretation, writing or submitting of the report.

From the beginning of the COVID-19 pandemic, researchers advised policy makers to make informed decisions towards the adoption of mitigating interventions. Key easy-to-interpret metrics applied over time can measure the public health impact of epidemic outbreaks. We propose a novel method which quantifies the effect of hospitalizations or mortality when the number of COVID-19 cases doubles. Two analyses are used, a country-by-country analysis and a multi-country approach which considers all countries simultaneously. The new measure is applied to several European countries, where the presence of different variants, vaccination rates and intervention measures taken over time leads to a different risk. Based on our results, the vaccination campaign has a clear effect for all countries analyzed, reducing the risk over time. However, the constant emergence of new variants combined with distinct intervention measures impacts differently the risk per country.

These datasets contain model results based on a synthetic data analysis and can be downloaded to be used in the supplementary material code of thispaper,with the following abstract: This work presents a joint spatial modelling framework to improve estimation of the spatial distribution of the latent COVID-19 incidence in Belgium, based on test-confirmed COVID-19 cases and crowd-sourced symptoms data as reported in a large-scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID-19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide valuable auxiliary data to optimise spatial COVID-19 incidence estimation based on confirmed cases in situations with limited testing capacity. Furthermore, it is shown that an online survey on COVID-19 symptoms with a sufficiently large sample size per spatial entity is capable of pinpointing the same locations that appear as test-confirmed clusters, approximately one week earlier. We conclude that a large-scale online study provides an inexpensive and flexible method to collect timely information of an epidemic during its early phase, which can be used by policy makers in an early phase of an epidemic and in conjunction with other monitoring systems.

Soon after SARS-CoV-2 emerged in late 2019, Belgium was confronted with a first COVID-19 wave in March-April 2020. SARS-CoV-2 circulation declined in the summer months (late May to early July 2020). Following a successfully trumped late July-August peak, COVID-19 incidence fell slightly, to then enter two successive phases of rapid incline: in the first half of September, and then again in October 2020. The first of these coincided with the peak period of returning summer travelers; the second one coincided with the start of higher education's academic year. The largest observed COVID-19 incidence occurred in the period 16-31 October, particularly in the Walloon Region, the southern, French-speaking part of Belgium. We examine the potential association of the higher education population with spatio-temporal spread of COVID-19, using Bayesian spatial Poisson models for confirmed test cases, accounting for socio-demographic heterogeneity in the population. We find a significant association between the number of COVID-19 cases in the age groups 18-29 years and 30-39 years and the size of the higher education student population at the municipality level. These results can be useful towards COVID-19 mitigation strategies, particularly in areas where virus transmission from higher education students into the broader community could exacerbate morbidity and mortality of COVID-19 among populations with prevalent underlying conditions associated with more severe outcomes following infection. ispartof: PLOS ONE vol:17 issue:2 ispartof: location:United States status: published