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Willem L, Abrams S, Franco N, Coletti P, Libin PJK, Wambua J, Couvreur S, André E, Wenseleers T, Mao Z, Torneri A, Faes C, Beutels P, Hens N. The impact of quality-adjusted life years on evaluating COVID-19 mitigation strategies: lessons from age-specific vaccination roll-out and variants of concern in Belgium (2020-2022). BMC Public Health 2024; 24:1171. [PMID: 38671366 PMCID: PMC11047051 DOI: 10.1186/s12889-024-18576-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND When formulating and evaluating COVID-19 vaccination strategies, an emphasis has been placed on preventing severe disease that overburdens healthcare systems and leads to mortality. However, more conventional outcomes such as quality-adjusted life years (QALYs) and inequality indicators are warranted as additional information for policymakers. METHODS We adopted a mathematical transmission model to describe the infectious disease dynamics of SARS-COV-2, including disease mortality and morbidity, and to evaluate (non)pharmaceutical interventions. Therefore, we considered temporal immunity levels, together with the distinct transmissibility of variants of concern (VOCs) and their corresponding vaccine effectiveness. We included both general and age-specific characteristics related to SARS-CoV-2 vaccination. Our scenario study is informed by data from Belgium, focusing on the period from August 2021 until February 2022, when vaccination for children aged 5-11 years was initially not yet licensed and first booster doses were administered to adults. More specifically, we investigated the potential impact of an earlier vaccination programme for children and increased or reduced historical adult booster dose uptake. RESULTS Through simulations, we demonstrate that increasing vaccine uptake in children aged 5-11 years in August-September 2021 could have led to reduced disease incidence and ICU occupancy, which was an essential indicator for implementing non-pharmaceutical interventions and maintaining healthcare system functionality. However, an enhanced booster dose regimen for adults from November 2021 onward could have resulted in more substantial cumulative QALY gains, particularly through the prevention of elevated levels of infection and disease incidence associated with the emergence of Omicron VOC. In both scenarios, the need for non-pharmaceutical interventions could have decreased, potentially boosting economic activity and mental well-being. CONCLUSIONS When calculating the impact of measures to mitigate disease spread in terms of life years lost due to COVID-19 mortality, we highlight the impact of COVID-19 on the health-related quality of life of survivors. Our study underscores that disease-related morbidity could constitute a significant part of the overall health burden. Our quantitative findings depend on the specific setup of the interventions under review, which is open to debate or should be contextualised within future situations.
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Affiliation(s)
- Lander Willem
- Department of Family Medicine and Population Health, Antwerp, Belgium.
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium.
| | - Steven Abrams
- Department of Family Medicine and Population Health, Antwerp, Belgium
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Nicolas Franco
- Data Science Institute, Hasselt University, Hasselt, Belgium
- Namur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
| | - Pietro Coletti
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Pieter J K Libin
- Data Science Institute, Hasselt University, Hasselt, Belgium
- Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, Belgium
- Rega Institute for Medical Research, Clinical and Epidemiological Virology, University of Leuven, Leuven, Belgium
| | - James Wambua
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Simon Couvreur
- Department of Epidemiology and public health, Sciensano, Brussel, Belgium
| | - Emmanuel André
- National Reference Centre for Respiratory Pathogens, University Hospitals Leuven, Leuven, Belgium
- Department of Microbiology, Immunology and Transplantation, University of Leuven, Leuven, Belgium
| | - Tom Wenseleers
- Laboratory of Socioecology and Social Evolution, University of Leuven, Leuven, Belgium
| | - Zhuxin Mao
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
| | - Andrea Torneri
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Christel Faes
- Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Philippe Beutels
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Niel Hens
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- Data Science Institute, Hasselt University, Hasselt, Belgium
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Vandendijck Y, Gressani O, Faes C, Camarda CG, Hens N. Cohort-based smoothing methods for age-specific contact rates. Biostatistics 2024; 25:521-540. [PMID: 36940671 DOI: 10.1093/biostatistics/kxad005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 02/24/2023] [Accepted: 03/05/2023] [Indexed: 03/23/2023] Open
Abstract
The use of social contact rates is widespread in infectious disease modeling since it has been shown that they are key driving forces of important epidemiological parameters. Quantification of contact patterns is crucial to parameterize dynamic transmission models and to provide insights on the (basic) reproduction number. Information on social interactions can be obtained from population-based contact surveys, such as the European Commission project POLYMOD. Estimation of age-specific contact rates from these studies is often done using a piecewise constant approach or bivariate smoothing techniques. For the latter, typically, smoothness is introduced in the dimensions of the respondent's and contact's age (i.e., the rows and columns of the social contact matrix). We propose a smoothing constrained approach-taking into account the reciprocal nature of contacts-introducing smoothness over the diagonal (including all subdiagonals) of the social contact matrix. This modeling approach is justified assuming that when people age their contact behavior changes smoothly. We call this smoothing from a cohort perspective. Two approaches that allow for smoothing over social contact matrix diagonals are proposed, namely (i) reordering of the diagonal components of the contact matrix and (ii) reordering of the penalty matrix ensuring smoothness over the contact matrix diagonals. Parameter estimation is done in the likelihood framework by using constrained penalized iterative reweighted least squares. A simulation study underlines the benefits of cohort-based smoothing. Finally, the proposed methods are illustrated on the Belgian POLYMOD data of 2006. Code to reproduce the results of the article can be downloaded on this GitHub repository https://github.com/oswaldogressani/Cohort_smoothing.
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Affiliation(s)
- Yannick Vandendijck
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Oswaldo Gressani
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Carlo G Camarda
- French Institute for Demographic Studies (INED), Aubervilliers, France
| | - Niel Hens
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium and Centre for Health Economics Research and Modelling Infectious Diseases, Vaxinfectio, University of Antwerp, Antwerp, Belgium
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De Muylder G, Laisnez V, Stefani G, Boulouffe C, Faes C, Hammami N, Hubin P, Molenberghs G, Sans J, van de Konijnenburg C, Van der Borght S, Brondeel R, Stassijns J, Lernout T. Translating the COVID-19 epidemiological situation into policies and measures: the Belgian experience. Front Public Health 2024; 12:1306361. [PMID: 38645450 PMCID: PMC11026715 DOI: 10.3389/fpubh.2024.1306361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 03/20/2024] [Indexed: 04/23/2024] Open
Abstract
The COVID-19 pandemic led to sustained surveillance efforts, which made unprecedented volumes and types of data available. In Belgium, these data were used to conduct a targeted and regular assessment of the epidemiological situation. In addition, management tools were developed, incorporating key indicators and thresholds, to define risk levels and offer guidance to policy makers. Categorizing risk into various levels provided a stable framework to monitor the COVID-19 epidemiological situation and allowed for clear communication to authorities. Although translating risk levels into specific public health measures has remained challenging, this experience was foundational for future evaluation of the situation for respiratory infections in general, which, in Belgium, is now based on a management tool combining different data sources.
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Affiliation(s)
| | - Valeska Laisnez
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Giulietta Stefani
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | | | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Naïma Hammami
- Department of Care, Team Infection Prevention and Vaccination, Brussels, Belgium
| | - Pierre Hubin
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Geert Molenberghs
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Faculty of Medicine, Department of Public Health and Primary Care, L-BioStat, Leuven, Belgium
| | - Jasper Sans
- Department of Infectious Disease Prevention, Brussels, Belgium
| | | | | | - Ruben Brondeel
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | | | - Tinne Lernout
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
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Sherratt K, Srivastava A, Ainslie K, Singh DE, Cublier A, Marinescu MC, Carretero J, Garcia AC, Franco N, Willem L, Abrams S, Faes C, Beutels P, Hens N, Müller S, Charlton B, Ewert R, Paltra S, Rakow C, Rehmann J, Conrad T, Schütte C, Nagel K, Abbott S, Grah R, Niehus R, Prasse B, Sandmann F, Funk S. Characterising information gains and losses when collecting multiple epidemic model outputs. Epidemics 2024; 47:100765. [PMID: 38643546 DOI: 10.1016/j.epidem.2024.100765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 01/25/2024] [Accepted: 03/26/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. METHODS We compared projections from the European COVID-19 Scenario Modelling Hub. Five teams modelled incidence in Belgium, the Netherlands, and Spain. We compared July 2022 projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model's quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. RESULTS By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models' quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. CONCLUSIONS We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort's aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts.
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Affiliation(s)
| | | | - Kylie Ainslie
- Dutch National Institute of Public Health and the Environment (RIVM), Bilthoven, Netherlands; School of Public Health, University of Hong Kong, Hong Kong Special Administrative Region
| | | | | | | | | | | | | | | | - Steven Abrams
- University of Antwerp, Antwerp, Belgium; UHasselt, Hasselt, Belgium
| | | | | | - Niel Hens
- University of Antwerp, Antwerp, Belgium; UHasselt, Hasselt, Belgium
| | | | | | | | | | | | | | - Tim Conrad
- Zuse Institute Berlin (ZIB), Berlin, Germany
| | | | - Kai Nagel
- Technische Universität Berlin, Berlin, Germany
| | - Sam Abbott
- London School of Hygiene & Tropical Medicine, London, UK
| | | | | | | | | | - Sebastian Funk
- London School of Hygiene & Tropical Medicine, London, UK
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5
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Claes J, Agten A, Blázquez-Moreno A, Crabbe M, Tuefferd M, Goehlmann H, Geys H, Peng CY, Neyens T, Faes C. The influence of resolution on the predictive power of spatial heterogeneity measures as biomarkers of liver fibrosis. Comput Biol Med 2024; 171:108231. [PMID: 38422965 DOI: 10.1016/j.compbiomed.2024.108231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/23/2024] [Accepted: 02/25/2024] [Indexed: 03/02/2024]
Abstract
Spatial heterogeneity of cells in liver biopsies can be used as biomarker for disease severity of patients. This heterogeneity can be quantified by non-parametric statistics of point pattern data, which make use of an aggregation of the point locations. The method and scale of aggregation are usually chosen ad hoc, despite values of the aforementioned statistics being heavily dependent on them. Moreover, in the context of measuring heterogeneity, increasing spatial resolution will not endlessly provide more accuracy. The question then becomes how changes in resolution influence heterogeneity indicators, and subsequently how they influence their predictive abilities. In this paper, cell level data of liver biopsy tissue taken from chronic Hepatitis B patients is used to analyze this issue. Firstly, Morisita-Horn indices, Shannon indices and Getis-Ord statistics were evaluated as heterogeneity indicators of different types of cells, using multiple resolutions. Secondly, the effect of resolution on the predictive performance of the indices in an ordinal regression model was investigated, as well as their importance in the model. A simulation study was subsequently performed to validate the aforementioned methods. In general, for specific heterogeneity indicators, a downward trend in predictive performance could be observed. While for local measures of heterogeneity a smaller grid-size is outperforming, global measures have a better performance with medium-sized grids. In addition, the use of both local and global measures of heterogeneity is recommended to improve the predictive performance.
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Affiliation(s)
- Jari Claes
- Data Science Institute, UHasselt - Hasselt University, Agoralaan 1, Diepenbeek, 3590, Belgium.
| | - Annelies Agten
- Data Science Institute, UHasselt - Hasselt University, Agoralaan 1, Diepenbeek, 3590, Belgium
| | - Alfonso Blázquez-Moreno
- Discovery Statistics, Global Development, Janssen Research and Development, Turnhoutseweg 30, Beerse, 2340, Belgium
| | - Marjolein Crabbe
- Discovery Statistics, Global Development, Janssen Research and Development, Turnhoutseweg 30, Beerse, 2340, Belgium
| | - Marianne Tuefferd
- Translational Biomarkers, Infectious Diseases, Janssen Research and Development, Turnhoutseweg 30, Beerse, 2340, Belgium
| | - Hinrich Goehlmann
- Translational Biomarkers, Infectious Diseases, Janssen Research and Development, Turnhoutseweg 30, Beerse, 2340, Belgium
| | - Helena Geys
- Discovery Statistics, Global Development, Janssen Research and Development, Turnhoutseweg 30, Beerse, 2340, Belgium
| | | | - Thomas Neyens
- Data Science Institute, UHasselt - Hasselt University, Agoralaan 1, Diepenbeek, 3590, Belgium; L-BioStat, KU Leuven, Kapucijnenvoer 35, Leuven, 3000, Belgium
| | - Christel Faes
- Data Science Institute, UHasselt - Hasselt University, Agoralaan 1, Diepenbeek, 3590, Belgium
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Vandeninden B, De Clercq EM, Devleesschauwer B, Otavova M, Bouland C, Faes C. Cluster pattern analysis of environmental stressors and quantifying their impact on all-cause mortality in Belgium. BMC Public Health 2024; 24:536. [PMID: 38378493 DOI: 10.1186/s12889-024-18011-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 02/06/2024] [Indexed: 02/22/2024] Open
Abstract
Environmental stress represents an important burden on health and leads to a considerable number of diseases, hospitalisations, and excess mortality. Our study encompasses a representative sample size drawn from the Belgian population in 2016 (n = 11.26 million, with a focus on n = 11.15 million individuals). The analysis is conducted at the geographical level of statistical sectors, comprising a total of n = 19,794 sectors, with a subset of n = 18,681 sectors considered in the investigation. We integrated multiple parameters at the finest spatial level and constructed three categories of environmental stress through clustering: air pollution, noise stress and stress related to specific land-use types. We observed identifiable patterns in the spatial distribution of stressors within each cluster category. We assessed the relationship between age-standardized all-cause mortality rates (ASMR) and environmental stressors. Our research found that especially very high air pollution values in areas where traffic is the dominant local component of air pollution (ASMR + 14,8%, 95% CI: 10,4 - 19,4%) and presence of industrial land (ASMR + 14,7%, 95% CI: 9,4 - 20,2%) in the neighbourhood are associated with an increased ASMR. Cumulative exposure to multiple sources of unfavourable environmental stress (simultaneously high air pollution, high noise, presence of industrial land or proximity of primary/secondary roads and lack of green space) is associated with an increase in ASMR (ASMR + 26,9%, 95% CI: 17,1 - 36,5%).
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Affiliation(s)
- Bram Vandeninden
- School of Public Health, Université Libre de Bruxelles, Brussels, Belgium.
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
- Department of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium.
| | - Eva M De Clercq
- Department of Chemical and Physical Health Risks, Sciensano, Brussels, Belgium
| | - Brecht Devleesschauwer
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
- Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium
| | - Martina Otavova
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
- Center for Demographic Research, UCLouvain, Louvain-La-Neuve, Belgium
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science, Diepenbeek, Hasselt, Belgium
| | - Catherine Bouland
- School of Public Health, Université Libre de Bruxelles, Brussels, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science, Diepenbeek, Hasselt, Belgium
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7
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Otavova M, Masquelier B, Faes C, van den Borre L, Vandeninden B, de Clercq E, Devleesschauwer B. Trends in socioeconomic inequalities in cause-specific premature mortality in Belgium, 1998-2019. BMC Public Health 2024; 24:470. [PMID: 38355531 PMCID: PMC10868013 DOI: 10.1186/s12889-024-17933-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Higher levels of socioeconomic deprivation have been consistently associated with increased risk of premature mortality, but a detailed analysis by causes of death is lacking in Belgium. We aim to investigate the association between area deprivation and all-cause and cause-specific premature mortality in Belgium over the period 1998-2019. METHODS We used the 2001 and 2011 Belgian Indices of Multiple Deprivation to assign statistical sectors, the smallest geographical units in the country, into deprivation deciles. All-cause and cause-specific premature mortality rates, population attributable fraction, and potential years of life lost due to inequality were estimated by period, sex, and deprivation deciles. RESULTS Men and women living in the most deprived areas were 1.96 and 1.78 times more likely to die prematurely compared to those living in the least deprived areas over the period under study (1998-2019). About 28% of all premature deaths could be attributed to socioeconomic inequality and about 30% of potential years of life lost would be averted if the whole population of Belgium faced the premature mortality rates of the least deprived areas. CONCLUSION Premature mortality rates have declined over time, but inequality has increased due to a faster pace of decrease in the least deprived areas compared to the most deprived areas. As the causes of death related to poor lifestyle choices contribute the most to the inequality gap, more effective, country-level interventions should be put in place to target segments of the population living in the most deprived areas as they are facing disproportionately high risks of dying.
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Affiliation(s)
- Martina Otavova
- Center for Demographic Research, UCLouvain, Louvain-la-Neuve, Belgium.
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium.
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
| | - Bruno Masquelier
- Center for Demographic Research, UCLouvain, Louvain-la-Neuve, Belgium
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Laura van den Borre
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
- Interface Demography, Department of Sociology, Vrije Universiteit Brussels, Brussels, Belgium
| | - Bram Vandeninden
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Research Centre on Environmental and Occupational Health, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium
- Department of Risk and Health Impact Assessment, Sciensano, Brussels, Belgium
| | - Eva de Clercq
- Department of Risk and Health Impact Assessment, Sciensano, Brussels, Belgium
| | - Brecht Devleesschauwer
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
- Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium
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8
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Jit M, Ainslie K, Althaus C, Caetano C, Colizza V, Paolotti D, Beutels P, Willem L, Edmunds J, Nunes B, Namorado S, Faes C, Low N, Wallinga J, Hens N. Reflections On Epidemiological Modeling To Inform Policy During The COVID-19 Pandemic In Western Europe, 2020-23. Health Aff (Millwood) 2023; 42:1630-1636. [PMID: 38048502 DOI: 10.1377/hlthaff.2023.00688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
We reflect on epidemiological modeling conducted throughout the COVID-19 pandemic in Western Europe, specifically in Belgium, France, Italy, the Netherlands, Portugal, Switzerland, and the United Kingdom. Western Europe was initially one of the worst-hit regions during the COVID-19 pandemic. Western European countries deployed a range of policy responses to the pandemic, which were often informed by mathematical, computational, and statistical models. Models differed in terms of temporal scope, pandemic stage, interventions modeled, and analytical form. This diversity was modulated by differences in data availability and quality, government interventions, societal responses, and technical capacity. Many of these models were decisive to policy making at key junctures, such as during the introduction of vaccination and the emergence of the Alpha, Delta, and Omicron variants. However, models also faced intense criticism from the press, other scientists, and politicians around their accuracy and appropriateness for decision making. Hence, evaluating the success of models in terms of accuracy and influence is an essential task. Modeling needs to be supported by infrastructure for systems to collect and share data, model development, and collaboration between groups, as well as two-way engagement between modelers and both policy makers and the public.
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Affiliation(s)
- Mark Jit
- Mark Jit , London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kylie Ainslie
- Kylie Ainslie, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | | | - Constantino Caetano
- Constantino Caetano, National Institute of Health Doutor Ricardo Jorge, Lisbon, Portugal
| | | | | | | | | | - John Edmunds
- John Edmunds, London School of Hygiene and Tropical Medicine
| | - Baltazar Nunes
- Baltazar Nunes, National Institute of Health Doutor Ricardo Jorge
| | - Sónia Namorado
- Sónia Namorado, National Institute of Health Doutor Ricardo Jorge
| | | | | | - Jacco Wallinga
- Jacco Wallinga, National Institute for Public Health and the Environment (RIVM)
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9
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Natalia YA, Faes C, Neyens T, Hammami N, Molenberghs G. Key risk factors associated with fractal dimension based geographical clustering of COVID-19 data in the Flemish and Brussels region, Belgium. Front Public Health 2023; 11:1249141. [PMID: 38026374 PMCID: PMC10654974 DOI: 10.3389/fpubh.2023.1249141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction COVID-19 remains a major concern globally. Therefore, it is important to evaluate COVID-19's rapidly changing trends. The fractal dimension has been proposed as a viable method to characterize COVID-19 curves since epidemic data is often subject to considerable heterogeneity. In this study, we aim to investigate the association between various socio-demographic factors and the complexity of the COVID-19 curve as quantified through its fractal dimension. Methods We collected population indicators data (ethnic composition, socioeconomic status, number of inhabitants, population density, the older adult population proportion, vaccination rate, satisfaction, and trust in the government) at the level of the statistical sector in Belgium. We compared these data with fractal dimension indicators of COVID-19 incidence between 1 January - 31 December 2021 using canonical correlation analysis. Results Our results showed that these population indicators have a significant association with COVID-19 incidences, with the highest explanatory and predictive power coming from the number of inhabitants, population density, and ethnic composition. Conclusion It is important to monitor these population indicators during a pandemic, especially when dealing with targeted interventions for a specific population.
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Affiliation(s)
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Thomas Neyens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- I-BioStat, Leuven Biostatistics and Statistical Bioinformatics Centre, KU Leuven, Leuven, Belgium
| | - Naïma Hammami
- Department of Care, Team Infection Prevention and Vaccination, Brussels, Belgium
| | - Geert Molenberghs
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- I-BioStat, Leuven Biostatistics and Statistical Bioinformatics Centre, KU Leuven, Leuven, Belgium
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10
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Kremer C, Willem L, Boone J, Arrazola de Oñate W, Hammami N, Faes C, Hens N. Key performance indicators of COVID-19 contact tracing in Belgium from September 2020 to December 2021. PLoS One 2023; 18:e0292346. [PMID: 37862313 PMCID: PMC10588862 DOI: 10.1371/journal.pone.0292346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 09/18/2023] [Indexed: 10/22/2023] Open
Abstract
The goal of tracing, testing, and quarantining contacts of infected individuals is to contain the spread of infectious diseases, a strategy widely used during the COVID-19 pandemic. However, limited research exists on the effectiveness of contact tracing, especially with regard to key performance indicators (KPIs), such as the proportion of cases arising from previously identified contacts. In our study, we analyzed contact tracing data from Belgium collected between September 2020 and December 2021 to assess the impact of contact tracing on SARS-CoV-2 transmission and understand its characteristics. Among confirmed cases involved in contact tracing in the Flemish and Brussels-Capital regions, 19.1% were previously identified as close contacts and were aware of prior exposure. These cases, referred to as 'known' to contact tracing operators, reported on average fewer close contacts compared to newly identified individuals (0.80 versus 1.05), resulting in fewer secondary cases (0.23 versus 0.28). Additionally, we calculated the secondary attack rate, representing infections per contact, which was on average lower for the 'known' cases (0.22 versus 0.25) between December 2020 and August 2021. These findings indicate the effectiveness of contact tracing in Belgium in reducing SARS-CoV-2 transmission. Although we were unable to quantify the exact number of prevented cases, our findings emphasize the importance of contact tracing as a public health measure. In addition, contact tracing data provide indications of potential shifts in transmission patterns among different age groups associated with emerging variants of concern and increasing vaccination rates.
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Affiliation(s)
- Cécile Kremer
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium
| | - Jorden Boone
- KPMG Advisory, Public Sector Practice, Zaventem, Belgium
| | - Wouter Arrazola de Oñate
- Belgian Lung and Tuberculosis Association, Brussels, Belgium
- Flemish Association for Respiratory Health and Tuberculosis, Leuven, Belgium
| | - Naïma Hammami
- Department of Infectious Disease Prevention and Control, Department of Care, Flemish Region, Brussels, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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Agten A, Blázquez-Moreno A, Crabbe M, Tuefferd M, Goehlmann H, Geys H, Peng CY, Claes J, Neyens T, Faes C. Measures of spatial heterogeneity in the liver tissue micro-environment as predictive factors for fibrosis score. Comput Biol Med 2023; 165:107382. [PMID: 37634463 DOI: 10.1016/j.compbiomed.2023.107382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/02/2023] [Accepted: 08/14/2023] [Indexed: 08/29/2023]
Abstract
The organization and interaction between hepatocytes and other hepatic non-parenchymal cells plays a pivotal role in maintaining normal liver function and structure. Although spatial heterogeneity within the tumor micro-environment has been proven to be a fundamental feature in cancer progression, the role of liver tissue topology and micro-environmental factors in the context of liver damage in chronic infection has not been widely studied yet. We obtained images from 110 core needle biopsies from a cohort of chronic hepatitis B patients with different fibrosis stages according to METAVIR score. The tissue sections were immunofluorescently stained and imaged to determine the locations of CD45 positive immune cells and HBsAg-negative and HBsAg-positive hepatocytes within the tissue. We applied several descriptive techniques adopted from ecology, including Getis-Ord, the Shannon Index and the Morisita-Horn Index, to quantify the extent to which immune cells and different types of liver cells co-localize in the tissue biopsies. Additionally, we modeled the spatial distribution of the different cell types using a joint log-Gaussian Cox process and proposed several features to quantify spatial heterogeneity. We then related these measures to the patient fibrosis stage by using a linear discriminant analysis approach. Our analysis revealed that the co-localization of HBsAg-negative hepatocytes with immune cells and the co-localization of HBsAg-positive hepatocytes with immune cells are equally important factors for explaining the METAVIR score in chronic hepatitis B patients. Moreover, we found that if we allow for an error of 1 on the METAVIR score, we are able to reach an accuracy of around 80%. With this study we demonstrate how methods adopted from ecology and applied to the liver tissue micro-environment can be used to quantify heterogeneity and how these approaches can be valuable in biomarker analyses for liver topology.
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Affiliation(s)
- Annelies Agten
- Data Science Institute, UHasselt - Hasselt University, Agoralaan 1, BE 3590 Diepenbeek, Belgium.
| | - Alfonso Blázquez-Moreno
- Discovery Statistics, Global Development, Janssen Research and Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Marjolein Crabbe
- Discovery Statistics, Global Development, Janssen Research and Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Marianne Tuefferd
- Translational Biomarkers, Infectious Diseases, Janssen Research and Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Hinrich Goehlmann
- Translational Biomarkers, Infectious Diseases, Janssen Research and Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | - Helena Geys
- Discovery Statistics, Global Development, Janssen Research and Development, Turnhoutseweg 30, 2340 Beerse, Belgium
| | | | - Jari Claes
- Data Science Institute, UHasselt - Hasselt University, Agoralaan 1, BE 3590 Diepenbeek, Belgium
| | - Thomas Neyens
- Data Science Institute, UHasselt - Hasselt University, Agoralaan 1, BE 3590 Diepenbeek, Belgium; L-BioStat, KU Leuven, Kapucijnenvoer 35, 3000 Leuven, Belgium
| | - Christel Faes
- Data Science Institute, UHasselt - Hasselt University, Agoralaan 1, BE 3590 Diepenbeek, Belgium
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Wambua J, Loedy N, Jarvis CI, Wong KLM, Faes C, Grah R, Prasse B, Sandmann F, Niehus R, Johnson H, Edmunds W, Beutels P, Hens N, Coletti P. The influence of COVID-19 risk perception and vaccination status on the number of social contacts across Europe: insights from the CoMix study. BMC Public Health 2023; 23:1350. [PMID: 37442987 PMCID: PMC10347859 DOI: 10.1186/s12889-023-16252-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 07/06/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND The SARS-CoV-2 transmission dynamics have been greatly modulated by human contact behaviour. To curb the spread of the virus, global efforts focused on implementing both Non-Pharmaceutical Interventions (NPIs) and pharmaceutical interventions such as vaccination. This study was conducted to explore the influence of COVID-19 vaccination status and risk perceptions related to SARS-CoV-2 on the number of social contacts of individuals in 16 European countries. METHODS We used data from longitudinal surveys conducted in the 16 European countries to measure social contact behaviour in the course of the pandemic. The data consisted of representative panels of participants in terms of gender, age and region of residence in each country. The surveys were conducted in several rounds between December 2020 and September 2021 and comprised of 29,292 participants providing a total of 111,103 completed surveys. We employed a multilevel generalized linear mixed effects model to explore the influence of risk perceptions and COVID-19 vaccination status on the number of social contacts of individuals. RESULTS The results indicated that perceived severity played a significant role in social contact behaviour during the pandemic after controlling for other variables (p-value < 0.001). More specifically, participants who had low or neutral levels of perceived severity reported 1.25 (95% Confidence intervals (CI) 1.13 - 1.37) and 1.10 (95% CI 1.00 - 1.21) times more contacts compared to those who perceived COVID-19 to be a serious illness, respectively. Additionally, vaccination status was also a significant predictor of contacts (p-value < 0.001), with vaccinated individuals reporting 1.31 (95% CI 1.23 - 1.39) times higher number of contacts than the non-vaccinated. Furthermore, individual-level factors played a more substantial role in influencing contact behaviour than country-level factors. CONCLUSION Our multi-country study yields significant insights on the importance of risk perceptions and vaccination in behavioral changes during a pandemic emergency. The apparent increase in social contact behaviour following vaccination would require urgent intervention in the event of emergence of an immune escaping variant.
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Affiliation(s)
- James Wambua
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Neilshan Loedy
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT London, UK
| | - Kerry L. M. Wong
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT London, UK
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Rok Grah
- European Centre for Disease Prevention and Control (ECDC), Gustav III:s Boulevard 40, 169 73 Solna, Sweden
| | - Bastian Prasse
- European Centre for Disease Prevention and Control (ECDC), Gustav III:s Boulevard 40, 169 73 Solna, Sweden
| | - Frank Sandmann
- European Centre for Disease Prevention and Control (ECDC), Gustav III:s Boulevard 40, 169 73 Solna, Sweden
| | - Rene Niehus
- European Centre for Disease Prevention and Control (ECDC), Gustav III:s Boulevard 40, 169 73 Solna, Sweden
| | - Helen Johnson
- European Centre for Disease Prevention and Control (ECDC), Gustav III:s Boulevard 40, 169 73 Solna, Sweden
- Current Address: Health Emergency Preparedness and Response Authority (HERA), European Commission, 1049, Brussels, Belgium
| | - W.John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT London, UK
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- The University of New South Wales, School of Public Health and Community Medicine, Sydney, NSW 2033 Australia
| | - Niel Hens
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Pietro Coletti
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
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Loedy N, Coletti P, Wambua J, Hermans L, Willem L, Jarvis CI, Wong KLM, Edmunds W, Robert A, Leclerc QJ, Gimma A, Molenberghs G, Beutels P, Faes C, Hens N. Longitudinal social contact data analysis: insights from 2 years of data collection in Belgium during the COVID-19 pandemic. BMC Public Health 2023; 23:1298. [PMID: 37415096 PMCID: PMC10326964 DOI: 10.1186/s12889-023-16193-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/26/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants' "survey fatigue", which may impact inferences. METHODS A negative binomial generalized additive model for location, scale, and shape (NBI GAMLSS) was adopted to estimate the number of contacts reported between age groups and to deal with under-reporting due to fatigue within the study. The dropout process was analyzed with first-order auto-regressive logistic regression to identify factors that influence dropout. Using the so-called next generation principle, we calculated the effect of under-reporting due to fatigue on estimating the reproduction number. RESULTS Fewer contacts were reported as people participated longer in the survey, which suggests under-reporting due to survey fatigue. Participant dropout is significantly affected by household size and age categories, but not significantly affected by the number of contacts reported in any of the two latest waves. This indicates covariate-dependent missing completely at random (MCAR) in the dropout pattern, when missing at random (MAR) is the alternative. However, we cannot rule out more complex mechanisms such as missing not at random (MNAR). Moreover, under-reporting due to fatigue is found to be consistent over time and implies a 15-30% reduction in both the number of contacts and the reproduction number ([Formula: see text]) ratio between correcting and not correcting for under-reporting. Lastly, we found that correcting for fatigue did not change the pattern of relative incidence between age groups also when considering age-specific heterogeneity in susceptibility and infectivity. CONCLUSIONS CoMix data highlights the variability of contact patterns across age groups and time, revealing the mechanisms governing the spread/transmission of COVID-19/airborne diseases in the population. Although such longitudinal contact surveys are prone to the under-reporting due to participant fatigue and drop-out, we showed that these factors can be identified and corrected using NBI GAMLSS. This information can be used to improve the design of similar, future surveys.
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Affiliation(s)
- Neilshan Loedy
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Pietro Coletti
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - James Wambua
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Lisa Hermans
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Kerry L. M. Wong
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Alexis Robert
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Quentin J. Leclerc
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Public Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Epidemiology and Modelling of Bacterial Escape to Antimicrobials, Institut Pasteur, Paris, France
| | - Amy Gimma
- Centre for Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Geert Molenberghs
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- L-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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Nguyen MH, Nguyen THT, Molenberghs G, Abrams S, Hens N, Faes C. The impact of national and international travel on spatio-temporal transmission of SARS-CoV-2 in Belgium in 2021. BMC Infect Dis 2023; 23:428. [PMID: 37355572 DOI: 10.1186/s12879-023-08368-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 06/02/2023] [Indexed: 06/26/2023] Open
Abstract
BACKGROUND The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has rapidly spread over the world and caused tremendous impacts on global health. Understanding the mechanism responsible for the spread of this pathogen and the impact of specific factors, such as human mobility, will help authorities to tailor interventions for future SARS-CoV-2 waves or newly emerging airborne infections. In this study, we aim to analyze the spatio-temporal transmission of SARS-CoV-2 in Belgium at municipality level between January and December 2021 and explore the effect of different levels of human travel on disease incidence through the use of counterfactual scenarios. METHODS We applied the endemic-epidemic modelling framework, in which the disease incidence decomposes into endemic, autoregressive and neighbourhood components. The spatial dependencies among areas are adjusted based on actual connectivity through mobile network data. We also took into account other important factors such as international mobility, vaccination coverage, population size and the stringency of restriction measures. RESULTS The results demonstrate the aggravating effect of international travel on the incidence, and simulated counterfactual scenarios further stress the alleviating impact of a reduction in national and international travel on epidemic growth. It is also clear that local transmission contributed the most during 2021, and municipalities with a larger population tended to attract a higher number of cases from neighboring areas. CONCLUSIONS Although transmission between municipalities was observed, local transmission was dominant. We highlight the positive association between the mobility data and the infection spread over time. Our study provides insight to assist health authorities in decision-making, particularly when the disease is airborne and therefore likely influenced by human movement.
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Affiliation(s)
- Minh Hanh Nguyen
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium.
| | | | - Geert Molenberghs
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- I-BioStat, Katholieke Universiteit Leuven, BE-3000, Leuven, Belgium
| | - Steven Abrams
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- Global Health Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
| | - Niel Hens
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- Global Health Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, BE-2000, Antwerpen, Belgium
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, BE-3500, Hasselt, Belgium
- I-BioStat, Katholieke Universiteit Leuven, BE-3000, Leuven, Belgium
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Mohamad MS, Abdul Maulud KN, Faes C. A practical illustration of spatial smoothing methods for disconnected regions with INLA: spatial survey on overweight and obesity in Malaysia. Int J Health Geogr 2023; 22:14. [PMID: 37344913 DOI: 10.1186/s12942-023-00336-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/01/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND National prevalence could mask subnational heterogeneity in disease occurrence, and disease mapping is an important tool to illustrate the spatial pattern of disease. However, there is limited information on techniques for the specification of conditional autoregressive models in disease mapping involving disconnected regions. This study explores available techniques for producing district-level prevalence estimates for disconnected regions, using as an example childhood overweight in Malaysia, which consists of the Peninsular and Borneo regions separated by the South China Sea. We used data from Malaysia National Health and Morbidity Survey conducted in 2015. We adopted Bayesian hierarchical modelling using the integrated nested Laplace approximation (INLA) program in R-software to model the spatial distribution of overweight among 6301 children aged 5-17 years across 144 districts located in two disconnected regions. We illustrate different types of spatial models for prevalence mapping across disconnected regions, taking into account the survey design and adjusting for district-level demographic and socioeconomic covariates. RESULTS The spatial model with split random effects and a common intercept has the lowest Deviance and Watanabe Information Criteria. There was evidence of a spatial pattern in the prevalence of childhood overweight across districts. An increasing trend in smoothed prevalence of overweight was observed when moving from the east to the west of the Peninsular and Borneo regions. The proportion of Bumiputera ethnicity in the district had a significant negative association with childhood overweight: the higher the proportion of Bumiputera ethnicity in the district, the lower the prevalence of childhood overweight. CONCLUSION This study illustrates different available techniques for mapping prevalence across districts in disconnected regions using survey data. These techniques can be utilized to produce reliable subnational estimates for any areas that comprise of disconnected regions. Through the example, we learned that the best-fit model was the one that considered the separate variations of the individual regions. We discovered that the occurrence of childhood overweight in Malaysia followed a spatial pattern with an east-west gradient trend, and we identified districts with high prevalence of overweight. This information could help policy makers in making informed decisions for targeted public health interventions in high-risk areas.
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Affiliation(s)
- Maria Safura Mohamad
- Faculty of Social Sciences, Unit of Health Sciences, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland.
| | - Khairul Nizam Abdul Maulud
- Department of Civil Engineering, Faculty of Engineering & Built Environment, National University of Malaysia, 43600, Bangi, Selangor, Malaysia
- Earth Observation Centre, Institute of Climate Change, National University of Malaysia, 43600, Bangi, Selangor, Malaysia
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium
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Lajot A, Wambua J, Coletti P, Franco N, Brondeel R, Faes C, Hens N. How contact patterns during the COVID-19 pandemic are related to pre-pandemic contact patterns and mobility trends. BMC Infect Dis 2023; 23:410. [PMID: 37328811 DOI: 10.1186/s12879-023-08369-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 06/02/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Non-pharmaceutical interventions (NPIs) were adopted in Belgium in order to decrease social interactions between people and as such decrease viral transmission of SARS-CoV-2. With the aim to better evaluate the impact of NPIs on the evolution of the pandemic, an estimation of social contact patterns during the pandemic is needed when social contact patterns are not available yet in real time. METHODS In this paper we use a model-based approach allowing for time varying effects to evaluate whether mobility and pre-pandemic social contact patterns can be used to predict the social contact patterns observed during the COVID-19 pandemic between November 11, 2020 and July 4, 2022. RESULTS We found that location-specific pre-pandemic social contact patterns are good indicators for estimating social contact patterns during the pandemic. However, the relationship between both changes with time. Considering a proxy for mobility, namely the change in the number of visitors to transit stations, in interaction with pre-pandemic contacts does not explain the time-varying nature of this relationship well. CONCLUSION In a situation where data from social contact surveys conducted during the pandemic are not yet available, the use of a linear combination of pre-pandemic social contact patterns could prove valuable. However, translating the NPIs at a given time into appropriate coefficients remains the main challenge of such an approach. In this respect, the assumption that the time variation of the coefficients can somehow be related to aggregated mobility data seems unacceptable during our study period for estimating the number of contacts at a given time.
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Affiliation(s)
- Adrien Lajot
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium.
| | - James Wambua
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
| | - Pietro Coletti
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
| | - Nicolas Franco
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
- Namur Institute for Complex Systems (naXys) and Department of Mathematics, University of Namur, Namur, Belgium
| | - Ruben Brondeel
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Christel Faes
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
| | - Niel Hens
- Data Science Institute, I-BioStat, University of Hasselt, Hasselt, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and infectious disease institute, University of Antwerp, Antwerp, Belgium
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Otavova M, Masquelier B, Faes C, Van den Borre L, Bouland C, De Clercq E, Vandeninden B, De Bleser A, Devleesschauwer B. Measuring small-area level deprivation in Belgium: The Belgian Index of Multiple Deprivation. Spat Spatiotemporal Epidemiol 2023; 45:100587. [PMID: 37301602 DOI: 10.1016/j.sste.2023.100587] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/02/2023] [Accepted: 04/17/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND In the past, deprivation has been mostly captured through simple and univariate measures such as low income or poor educational attainment in research on health and social inequalities in Belgium. This paper presents a shift towards a more complex, multidimensional measure of deprivation at the aggregate level and describes the development of the first Belgian Indices of Multiple Deprivation (BIMDs) for the years 2001 and 2011. METHODS The BIMDs are constructed at the level of the smallest administrative unit in Belgium, the statistical sector. They are a combination of six domains of deprivation: income, employment, education, housing, crime and health. Each domain is built on a suite of relevant indicators representing individuals that suffer from a certain deprivation in an area. The indicators are combined to create the domain deprivation scores, and these scores are then weighted to create the overall BIMDs scores. The domain and BIMDs scores can be ranked and assigned to deciles from 1 (the most deprived) to 10 (the least deprived). RESULTS We show geographical variations in the distribution of the most and least deprived statistical sectors in terms of individual domains and overall BIMDs, and we identify hotspots of deprivation. The majority of the most deprived statistical sectors are located in Wallonia, whereas most of the least deprived statistical sectors are in Flanders. CONCLUSION The BIMDs offer a new tool for researches and policy makers for analyzing patterns of deprivation and identifying areas that would benefit from special initiatives and programs.
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Affiliation(s)
- Martina Otavova
- Center for Demographic Research, UCLouvain, Louvain-la-Neuve, Belgium; Data Science Institute, I-BioStat, Hasselt University, Belgium; Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium.
| | - Bruno Masquelier
- Center for Demographic Research, UCLouvain, Louvain-la-Neuve, Belgium
| | - Christel Faes
- Data Science Institute, I-BioStat, Hasselt University, Belgium
| | - Laura Van den Borre
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium; Interface Demography, Department of Sociology, Vrije Universiteit Brussels, Belgium
| | - Catherine Bouland
- Research Centre on Environmental and Occupational Health, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium
| | - Eva De Clercq
- Department of Risk and Health Impact Assessment, Sciensano, Brussels, Belgium
| | - Bram Vandeninden
- Data Science Institute, I-BioStat, Hasselt University, Belgium; Research Centre on Environmental and Occupational Health, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium; Department of Risk and Health Impact Assessment, Sciensano, Brussels, Belgium
| | | | - Brecht Devleesschauwer
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium; Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium
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Semakula M, Niragire F, Nsanzimana S, Remera E, Faes C. Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda. BMC Public Health 2023; 23:930. [PMID: 37221533 DOI: 10.1186/s12889-023-15888-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 05/12/2023] [Indexed: 05/25/2023] Open
Abstract
INTRODUCTION Africa was threatened by the coronavirus disease 2019 (COVID-19) due to the limited health care infrastructure. Rwanda has consistently used non-pharmaceutical strategies, such as lockdown, curfew, and enforcement of prevention measures to control the spread of COVID-19. Despite the mitigation measures taken, the country has faced a series of outbreaks in 2020 and 2021. In this paper, we investigate the nature of epidemic phenomena in Rwanda and the impact of imported cases on the spread of COVID-19 using endemic-epidemic spatio-temporal models. Our study provides a framework for understanding the dynamics of the epidemic in Rwanda and monitoring its phenomena to inform public health decision-makers for timely and targeted interventions. RESULTS The findings provide insights into the effects of lockdown and imported infections in Rwanda's COVID-19 outbreaks. The findings showed that imported infections are dominated by locally transmitted cases. The high incidence was predominant in urban areas and at the borders of Rwanda with its neighboring countries. The inter-district spread of COVID-19 was very limited due to mitigation measures taken in Rwanda. CONCLUSION The study recommends using evidence-based decisions in the management of epidemics and integrating statistical models in the analytics component of the health information system.
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Affiliation(s)
- Muhammed Semakula
- I-BioStat, Hasselt University, Hasselt, Belgium.
- College of Business and Economics, Centre of excellence in Data Science, Bio-statistics, University of Rwanda, Kigali, Kigali, Rwanda.
- Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda.
| | - François Niragire
- Department of Applied Statistics, University of Rwanda, Kigali, Kigali, Rwanda
| | | | - Eric Remera
- Rwanda Biomedical Centre, Ministry of Health, Kigali, Rwanda
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19
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Wong KLM, Gimma A, Coletti P, Faes C, Beutels P, Hens N, Jaeger VK, Karch A, Johnson H, Edmunds WJ, Jarvis CI. Social contact patterns during the COVID-19 pandemic in 21 European countries - evidence from a two-year study. BMC Infect Dis 2023; 23:268. [PMID: 37101123 PMCID: PMC10132446 DOI: 10.1186/s12879-023-08214-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 03/31/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Most countries have enacted some restrictions to reduce social contacts to slow down disease transmission during the COVID-19 pandemic. For nearly two years, individuals likely also adopted new behaviours to avoid pathogen exposure based on personal circumstances. We aimed to understand the way in which different factors affect social contacts - a critical step to improving future pandemic responses. METHODS The analysis was based on repeated cross-sectional contact survey data collected in a standardized international study from 21 European countries between March 2020 and March 2022. We calculated the mean daily contacts reported using a clustered bootstrap by country and by settings (at home, at work, or in other settings). Where data were available, contact rates during the study period were compared with rates recorded prior to the pandemic. We fitted censored individual-level generalized additive mixed models to examine the effects of various factors on the number of social contacts. RESULTS The survey recorded 463,336 observations from 96,456 participants. In all countries where comparison data were available, contact rates over the previous two years were substantially lower than those seen prior to the pandemic (approximately from over 10 to < 5), predominantly due to fewer contacts outside the home. Government restrictions imposed immediate effect on contacts, and these effects lingered after the restrictions were lifted. Across countries, the relationships between national policy, individual perceptions, or personal circumstances determining contacts varied. CONCLUSIONS Our study, coordinated at the regional level, provides important insights into the understanding of the factors associated with social contacts to support future infectious disease outbreak responses.
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Affiliation(s)
- Kerry L M Wong
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
| | - Amy Gimma
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Pietro Coletti
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, 3590, Diepenbeek, Belgium
| | - Christel Faes
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, 3590, Diepenbeek, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Niel Hens
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, 3590, Diepenbeek, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Veronika K Jaeger
- Institute of Epidemiology and Social Medicine, University of Muenster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Andre Karch
- Institute of Epidemiology and Social Medicine, University of Muenster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Helen Johnson
- European Centre for Disease Prevention and Control (ECDC), Solna, Sweden
| | - WJohn Edmunds
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Christopher I Jarvis
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
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20
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Verbeeck J, Faes C, Neyens T, Hens N, Verbeke G, Deboosere P, Molenberghs G. A linear mixed model to estimate COVID-19-induced excess mortality. Biometrics 2023; 79:417-425. [PMID: 34694627 PMCID: PMC8652760 DOI: 10.1111/biom.13578] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 09/06/2021] [Accepted: 09/30/2021] [Indexed: 11/30/2022]
Abstract
The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.
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Affiliation(s)
- Johan Verbeeck
- Data Science Institute (DSI)Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat)Hasselt UniversityHasseltBE‐3500Belgium
| | - Christel Faes
- Data Science Institute (DSI)Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat)Hasselt UniversityHasseltBE‐3500Belgium
| | - Thomas Neyens
- Data Science Institute (DSI)Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat)Hasselt UniversityHasseltBE‐3500Belgium
- Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat)KULeuvenLeuvenBE‐3000Belgium
| | - Niel Hens
- Data Science Institute (DSI)Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat)Hasselt UniversityHasseltBE‐3500Belgium
- Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID)Vaccine & Infectious Disease Institute (VAXINFECTIO)University of AntwerpAntwerpBE‐2000Belgium
| | - Geert Verbeke
- Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat)KULeuvenLeuvenBE‐3000Belgium
| | - Patrick Deboosere
- Interface Demography (ID)Department of SociologyVrije Universiteit BrusselBrusselsBE‐1050Belgium
| | - Geert Molenberghs
- Data Science Institute (DSI)Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat)Hasselt UniversityHasseltBE‐3500Belgium
- Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat)KULeuvenLeuvenBE‐3000Belgium
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21
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Semakula M, Niragire F, Faes C. Spatio-Temporal Bayesian Models for Malaria Risk Using Survey and Health Facility Routine Data in Rwanda. Int J Environ Res Public Health 2023; 20:4283. [PMID: 36901291 PMCID: PMC10001932 DOI: 10.3390/ijerph20054283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/24/2023] [Accepted: 02/26/2023] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Malaria is a life-threatening disease ocuring mainly in developing countries. Almost half of the world's population was at risk of malaria in 2020. Children under five years age are among the population groups at considerably higher risk of contracting malaria and developing severe disease. Most countries use Demographic and Health Survey (DHS) data for health programs and evaluation. However, malaria elimination strategies require a real-time, locally-tailored response based on malaria risk estimates at the lowest administrative levels. In this paper, we propose a two-step modeling framework using survey and routine data to improve estimates of malaria risk incidence in small areas and enable quantifying malaria trends. METHODS To improve estimates, we suggest an alternative approach to modeling malaria relative risk by combining information from survey and routine data through Bayesian spatio-temporal models. We model malaria risk using two steps: (1) fitting a binomial model to the survey data, and (2) extracting fitted values and using them in the Poison model as nonlinear effects in the routine data. We modeled malaria relative risk among under-five-year old children in Rwanda. RESULTS The estimation of malaria prevalence among children who are under five years old using Rwanda demographic and health survey data for the years 2019-2020 alone showed a higher prevalence in the southwest, central, and northeast of Rwanda than the rest of the country. Combining with routine health facility data, we detected clusters that were undetected based on the survey data alone. The proposed approach enabled spatial and temporal trend effect estimation of relative risk in local/small areas in Rwanda. CONCLUSIONS The findings of this analysis suggest that using DHS combined with routine health services data for active malaria surveillance may provide provide more precise estimates of the malaria burden, which can be used toward malaria elimination targets. We compared findings from geostatistical modeling of malaria prevalence among under-five-year old children using DHS 2019-2020 and findings from malaria relative risk spatio-temporal modeling using both DHS survey 2019-2020 and health facility routine data. The strength of routinely collected data at small scales and high-quality data from the survey contributed to a better understanding of the malaria relative risk at the subnational level in Rwanda.
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Affiliation(s)
- Muhammed Semakula
- I-BioStat, Hasselt University, 3500 Hasselt, Belgium
- Centre of Excellence in Data Science, Bio-Statistics, College of Business and Economics, University of Rwanda, Kigali 4285, Rwanda
- Rwanda Biomedical Center, Kigali 7162, Rwanda
- KIT Royal Tropical Institute of Amsterdam, 1092 AD Amsterdam, The Netherlands
| | - François Niragire
- Department of Applied Statistics, University of Rwanda, Kigali 4285, Rwanda
| | - Christel Faes
- I-BioStat, Hasselt University, 3500 Hasselt, Belgium
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22
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Gressani O, Faes C, Hens N. An approximate Bayesian approach for estimation of the instantaneous reproduction number under misreported epidemic data. Biom J 2023:e2200024. [PMID: 36639234 DOI: 10.1002/bimj.202200024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 11/04/2022] [Accepted: 11/18/2022] [Indexed: 01/15/2023]
Abstract
In epidemic models, the effective reproduction number is of central importance to assess the transmission dynamics of an infectious disease and to orient health intervention strategies. Publicly shared data during an outbreak often suffers from two sources of misreporting (underreporting and delay in reporting) that should not be overlooked when estimating epidemiological parameters. The main statistical challenge in models that intrinsically account for a misreporting process lies in the joint estimation of the time-varying reproduction number and the delay/underreporting parameters. Existing Bayesian approaches typically rely on Markov chain Monte Carlo algorithms that are extremely costly from a computational perspective. We propose a much faster alternative based on Laplacian-P-splines (LPS) that combines Bayesian penalized B-splines for flexible and smooth estimation of the instantaneous reproduction number and Laplace approximations to selected posterior distributions for fast computation. Assuming a known generation interval distribution, the incidence at a given calendar time is governed by the epidemic renewal equation and the delay structure is specified through a composite link framework. Laplace approximations to the conditional posterior of the spline vector are obtained from analytical versions of the gradient and Hessian of the log-likelihood, implying a drastic speed-up in the computation of posterior estimates. Furthermore, the proposed LPS approach can be used to obtain point estimates and approximate credible intervals for the delay and reporting probabilities. Simulation of epidemics with different combinations for the underreporting rate and delay structure (one-day, two-day, and weekend delays) show that the proposed LPS methodology delivers fast and accurate estimates outperforming existing methods that do not take into account underreporting and delay patterns. Finally, LPS is illustrated in two real case studies of epidemic outbreaks.
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Affiliation(s)
- Oswaldo Gressani
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium.,Centre for Health Economics Research and Modelling Infectious Diseases, Vaxinfectio, University of Antwerp, Antwerp, Belgium
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23
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Otavova M, Faes C, Bouland C, De Clercq E, Vandeninden B, Eggerickx T, Sanderson JP, Devleesschauwer B, Masquelier B. Inequalities in mortality associated with housing conditions in Belgium between 1991 and 2020. BMC Public Health 2022; 22:2397. [PMID: 36539802 PMCID: PMC9769013 DOI: 10.1186/s12889-022-14819-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Poor housing conditions have been associated with increased mortality. Our objective is to investigate the association between housing inequality and increased mortality in Belgium and to estimate the number of deaths that could be prevented if the population of the whole country faced the mortality rates experienced in areas that are least deprived in terms of housing. METHODS We used individual-level mortality data extracted from the National Register in Belgium and relative to deaths that occurred between Jan. 1, 1991, and Dec. 31, 2020. Spatial and time-specific housing deprivation indices (1991, 2001, and 2011) were created at the level of the smallest geographical unit in Belgium, with these units assigned into deciles from the most to the least deprived. We calculated mortality associated with housing inequality as the difference between observed and expected deaths by applying mortality rates of the least deprived decile to other deciles. We also used standard life table calculations to estimate the potential years of life lost due housing inequality. RESULTS Up to 18.5% (95% CI 17.7-19.3) of all deaths between 1991 and 2020 may be associated with housing inequality, corresponding to 584,875 deaths. Over time, life expectancy at birth increased for the most and least deprived deciles by about 3.5 years. The gap in life expectancy between the two deciles remained high, on average 4.6 years. Life expectancy in Belgium would increase by approximately 3 years if all deciles had the mortality rates of the least deprived decile. CONCLUSIONS Thousands of deaths in Belgium could be avoided if all Belgian neighborhoods had the mortality rates of the least deprived areas in terms of housing. Hotspots of housing inequalities need to be located and targeted with tailored public actions.
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Affiliation(s)
- Martina Otavova
- grid.7942.80000 0001 2294 713XCenter for Demographic Research, UCLouvain, Louvain-La-Neuve, Belgium ,grid.12155.320000 0001 0604 5662Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium ,grid.508031.fDepartment of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Christel Faes
- grid.12155.320000 0001 0604 5662Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Catherine Bouland
- grid.4989.c0000 0001 2348 0746Research Centre On Environmental and Occupational Health, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium
| | - Eva De Clercq
- grid.508031.fDepartment of Risk and Health Impact Assessment, Sciensano, Brussels, Belgium
| | - Bram Vandeninden
- grid.12155.320000 0001 0604 5662Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium ,grid.4989.c0000 0001 2348 0746Research Centre On Environmental and Occupational Health, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium ,grid.508031.fDepartment of Risk and Health Impact Assessment, Sciensano, Brussels, Belgium
| | - Thierry Eggerickx
- grid.7942.80000 0001 2294 713XCenter for Demographic Research, UCLouvain, Louvain-La-Neuve, Belgium
| | - Jean-Paul Sanderson
- grid.7942.80000 0001 2294 713XCenter for Demographic Research, UCLouvain, Louvain-La-Neuve, Belgium
| | - Brecht Devleesschauwer
- grid.508031.fDepartment of Epidemiology and Public Health, Sciensano, Brussels, Belgium ,grid.5342.00000 0001 2069 7798Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium
| | - Bruno Masquelier
- grid.7942.80000 0001 2294 713XCenter for Demographic Research, UCLouvain, Louvain-La-Neuve, Belgium
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24
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Martins A, Herzog SA, Mugenyi L, Faes C, Hens N, Abrams S. Modelling longitudinal binary outcomes with outcome dependent observation times: an application to a malaria cohort study. Malar J 2022; 21:380. [PMID: 36496382 PMCID: PMC9741489 DOI: 10.1186/s12936-022-04386-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 11/14/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND : In spite of the global reduction of 21% in malaria incidence between 2010 and 2015, the disease still threatens many lives of children and pregnant mothers in African countries. A correct assessment and evaluation of the impact of malaria control strategies still remains quintessential in order to eliminate the disease and its burden. Malaria follow-up studies typically involve routine visits at pre-scheduled time points and/or clinical visits whenever individuals experience malaria-like symptoms. In the latter case, infection triggers outcome assessment, thereby leading to outcome-dependent sampling (ODS). Commonly used methods to analyze such longitudinal data ignore ODS and potentially lead to biased estimates of malaria-specific transmission parameters, hence, inducing an incorrect assessment and evaluation of malaria control strategies. METHODS : In this paper, a new method is proposed to handle ODS by use of a joint model for the longitudinal binary outcome measured at routine visits and the clinical event times. The methodology is applied to malaria parasitaemia data from a cohort of [Formula: see text] Ugandan children aged 0.5-10 years from 3 regions (Walukuba-300 children, Kihihi-355 children and Nagongera-333 children) with varying transmission intensities (entomological inoculation rate equal to 2.8, 32 and 310 infectious bites per unit year, respectively) collected between 2011-2014. RESULTS : The results indicate that malaria parasite prevalence and force of infection (FOI) increase with age in the region of high malaria intensity with highest FOI in age group 5-10 years. For the region of medium intensity, the prevalence slightly increases with age and the FOI for the routine process is highest in age group 5-10 years, yet for the clinical infections, the FOI gradually decreases with increasing age. For the region with low intensity, both the prevalence and FOI peak at the age of 1 year after which the former remains constant with age yet the latter suddenly decreases with age for the clinically observed infections. CONCLUSION : Malaria parasite prevalence and FOI increase with age in the region of high malaria intensity. In all study sites, both the prevalence and FOI are highest among previously asymptomatic children and lowest among their symptomatic counterparts. Using a simulation study inspired by the malaria data at hand, the proposed methodology shows to have the smallest bias, especially when consecutive positive malaria parasitaemia presence results within a time period of 35 days were considered to be due to the same infection.
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Affiliation(s)
- Adelino Martins
- grid.12155.320000 0001 0604 5662Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, UHasselt, Diepenbeek, Belgium ,grid.8295.60000 0001 0943 5818Department of Mathematics and Informatics, Eduardo Mondlane University, Maputo, Mozambique
| | - Sereina A. Herzog
- grid.11598.340000 0000 8988 2476Institute for Medical Informatics, Statistics and Documentation (IMI), Medical University of Graz, Graz, Austria ,grid.5284.b0000 0001 0790 3681Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Levicatus Mugenyi
- grid.463352.50000 0004 8340 3103Infectious Diseases Research Collaboration, Plot 2C Nakasero Hill road, Kampala, Uganda
| | - Christel Faes
- grid.12155.320000 0001 0604 5662Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, UHasselt, Diepenbeek, Belgium
| | - Niel Hens
- grid.12155.320000 0001 0604 5662Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, UHasselt, Diepenbeek, Belgium ,grid.5284.b0000 0001 0790 3681Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium ,grid.5284.b0000 0001 0790 3681Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium
| | - Steven Abrams
- grid.12155.320000 0001 0604 5662Interuniversity Institute for Biostatistics and statistical Bioinformatics, Data Science Institute, UHasselt, Diepenbeek, Belgium ,grid.5284.b0000 0001 0790 3681Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium
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25
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Petrof O, Fajgenblat M, Neyens T, Molenberghs G, Faes C. The doubling effect of COVID-19 cases on key health indicators. PLoS One 2022; 17:e0275523. [PMID: 36417418 PMCID: PMC9683546 DOI: 10.1371/journal.pone.0275523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 09/19/2022] [Indexed: 11/25/2022] Open
Abstract
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.
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Affiliation(s)
- Oana Petrof
- I-Biostat, DSI, Hasselt University, Diepenbeek, Belgium
- * E-mail:
| | - Maxime Fajgenblat
- Laboratory of Freshwater Ecology, Evolution and Conservation, KU Leuven, Leuven, Belgium
| | - Thomas Neyens
- I-Biostat, DSI, Hasselt University, Diepenbeek, Belgium
- Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat), KU Leuven, Leuven, Belgium
| | - Geert Molenberghs
- I-Biostat, DSI, Hasselt University, Diepenbeek, Belgium
- I-BioStat, KU Leuven University of Leuven, Leuven, Belgium
| | - Christel Faes
- I-Biostat, DSI, Hasselt University, Diepenbeek, Belgium
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26
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De Pauw R, Claessens M, Gorasso V, Drieskens S, Faes C, Devleesschauwer B. Future trends of overweight and obesity in Belgium using Bayesian age-period-cohort models. Eur J Public Health 2022. [DOI: 10.1093/eurpub/ckac131.209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Considering the current overweight and obesity epidemic and its associated increase in non-communicable diseases and healthcare costs, the current study aimed to project the trends in prevalence of overweight and obesity in Belgium using a Bayesian age-period-cohort (APC) model to support policy planning.
Methods
Height and weight of 58,369 adults aged 18+ years, collected in six consecutive cross-sectional health interview surveys between 1997 and 2018, were evaluated. Criteria used for overweight and obesity were defined as body mass index (BMI) ≥ 25, and BMI ≥ 30. A Bayesian APC model was applied to evaluate past trends and associated socio-demographic risk factors, and to forecast trends to 2019-2029. All analyses were performed based on integrated nested Laplace approximation (INLA) and took the complex survey design into account.
Results
The prevalence of overweight and obesity has increased between 1997 and 2018. If current trends continue, it is likely to that a further increase in the prevalence of overweight and obesity in the population will be seen by 2029 with a probability of growth of 51.2% and 73.3%, respectively. Forecasts indicated a potential prevalence of 50.1% [16.2%; 84.4%] in 2029 for overweight, and 21.4% [9.0%; 43.4%] for obesity. Among survey participants, middle-aged men with no higher education and a middle income showed the highest risk of overweight and obesity.
Conclusions
We projected an alarming increase in the prevalence of overweight and obesity. A decrease in cases seems very unlikely. There is an urgent need to target younger age groups for prevention and implementation of public educational programs to limit the increasing trend in overweight and obesity.
Key messages
• The occurence of obesity is likely to increase in the following 10 years.
• Projection of trends can serve as a useful tool for policy planning on the mid- and longer term.
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Affiliation(s)
- R De Pauw
- Lifestyle and Chronic Diseases, Sciensano, Brussels, Belgium
- Rehabilitation Sciences, Ghent University , Ghent, Belgium
| | - M Claessens
- Lifestyle and Chronic Diseases, Sciensano, Brussels, Belgium
| | - V Gorasso
- Lifestyle and Chronic Diseases, Sciensano, Brussels, Belgium
- Public Health, Ghent University , Ghent, Belgium
| | - S Drieskens
- Lifestyle and Chronic Diseases, Sciensano, Brussels, Belgium
| | - C Faes
- University of Hasselt Mathematics, , Hasselt, Belgium
| | - B Devleesschauwer
- Lifestyle and Chronic Diseases, Sciensano, Brussels, Belgium
- Veterinary Sciences, Ghent University , Ghent, Belgium
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27
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Gressani O, Wallinga J, Althaus CL, Hens N, Faes C. EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number. PLoS Comput Biol 2022; 18:e1010618. [PMID: 36215319 PMCID: PMC9584461 DOI: 10.1371/journal.pcbi.1010618] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 10/20/2022] [Accepted: 09/30/2022] [Indexed: 11/17/2022] Open
Abstract
In infectious disease epidemiology, the instantaneous reproduction number [Formula: see text] is a time-varying parameter defined as the average number of secondary infections generated by an infected individual at time t. It is therefore a crucial epidemiological statistic that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool (EpiLPS) for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible intervals of [Formula: see text] by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of [Formula: see text] in only a few seconds; and (2) an approach based on a Markov chain Monte Carlo (MCMC) scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a negative binomial distribution to account for potential overdispersion in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a "plug-in'' estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of [Formula: see text] as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and on the SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France.
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Affiliation(s)
- Oswaldo Gressani
- Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium,* E-mail:
| | - Jacco Wallinga
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands,Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
| | - Christian L. Althaus
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Niel Hens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium,Centre for Health Economics Research and Modelling Infectious Diseases, Vaxinfectio, University of Antwerp, Antwerp, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University, Hasselt, Belgium
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Wong KLM, Gimma A, Paixao ES, Faes C, Beutels P, Hens N, Jarvis CI, Edmunds WJ. Pregnancy during COVID-19: social contact patterns and vaccine coverage of pregnant women from CoMix in 19 European countries. BMC Pregnancy Childbirth 2022; 22:757. [PMID: 36209078 PMCID: PMC9547635 DOI: 10.1186/s12884-022-05076-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 09/26/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Evidence and advice for pregnant women evolved during the COVID-19 pandemic. We studied social contact behaviour and vaccine uptake in pregnant women between March 2020 and September 2021 in 19 European countries. METHODS In each country, repeated online survey data were collected from a panel of nationally-representative participants. We calculated the adjusted mean number of contacts reported with an individual-level generalized additive mixed model, modelled using the negative binomial distribution and a log link function. Mean proportion of people in isolation or quarantine, and vaccination coverage by pregnancy status and gender were calculated using a clustered bootstrap. FINDINGS We recorded 4,129 observations from 1,041 pregnant women, and 115,359 observations from 29,860 non-pregnant individuals aged 18-49. Pregnant women made slightly fewer contacts (3.6, 95%CI = 3.5-3.7) than non-pregnant women (4.0, 95%CI = 3.9-4.0), driven by fewer work contacts but marginally more contacts in non-essential social settings. Approximately 15-20% pregnant and 5% of non-pregnant individuals reported to be in isolation and quarantine for large parts of the study period. COVID-19 vaccine coverage was higher in pregnant women than in non-pregnant women between January and April 2021. Since May 2021, vaccination in non-pregnant women began to increase and surpassed that in pregnant women. INTERPRETATION Limited social contact to avoid pathogen exposure during the COVID-19 pandemic has been a challenge to many, especially women going through pregnancy. More recognition of maternal social support desire is needed in the ongoing pandemic. As COVID-19 vaccination continues to remain an important pillar of outbreak response, strategies to promote correct information can provide reassurance and facilitate informed pregnancy vaccine decisions in this vulnerable group.
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Affiliation(s)
- Kerry L M Wong
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
| | - Amy Gimma
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Enny S Paixao
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Christel Faes
- Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Niel Hens
- Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Christopher I Jarvis
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - W John Edmunds
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
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Espinosa L, Wijermans A, Orchard F, Höhle M, Czernichow T, Coletti P, Hermans L, Faes C, Kissling E, Mollet T. Epitweetr: Early warning of public health threats using Twitter data. Euro Surveill 2022; 27:2200177. [PMID: 36177867 PMCID: PMC9524055 DOI: 10.2807/1560-7917.es.2022.27.39.2200177] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
BackgroundThe European Centre for Disease Prevention and Control (ECDC) systematically collates information from sources to rapidly detect early public health threats. The lack of a freely available, customisable and automated early warning tool using data from Twitter prompted the ECDC to develop epitweetr, which collects, geolocates and aggregates tweets generating signals and email alerts.AimThis study aims to compare the performance of epitweetr to manually monitoring tweets for the purpose of early detecting public health threats.MethodsWe calculated the general and specific positive predictive value (PPV) of signals generated by epitweetr between 19 October and 30 November 2020. Sensitivity, specificity, timeliness and accuracy and performance of tweet geolocation and signal detection algorithms obtained from epitweetr and the manual monitoring of 1,200 tweets were compared.ResultsThe epitweetr geolocation algorithm had an accuracy of 30.1% at national, and 25.9% at subnational levels. The signal detection algorithm had 3.0% general PPV and 74.6% specific PPV. Compared to manual monitoring, epitweetr had greater sensitivity (47.9% and 78.6%, respectively), and reduced PPV (97.9% and 74.6%, respectively). Median validation time difference between 16 common events detected by epitweetr and manual monitoring was -48.6 hours (IQR: -102.8 to -23.7).ConclusionEpitweetr has shown sufficient performance as an early warning tool for public health threats using Twitter data. Since epitweetr is a free, open-source tool with configurable settings and a strong automated component, it is expected to increase in usability and usefulness to public health experts.
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Affiliation(s)
- Laura Espinosa
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | - Ariana Wijermans
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden
| | | | | | | | | | | | | | | | - Thomas Mollet
- European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden,Current affiliation: International Federation of Red Cross and Red Crescent Societies, Geneva, Switzerland
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De Wael A, De Backer A, Yu CP, Sentürk DG, Lobato I, Faes C, Van Aert S. Three Approaches for Representing the Statistical Uncertainty on Atom-Counting Results in Quantitative ADF STEM. Microsc Microanal 2022; 29:1-9. [PMID: 36117265 DOI: 10.1017/s1431927622012284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A decade ago, a statistics-based method was introduced to count the number of atoms from annular dark-field scanning transmission electron microscopy (ADF STEM) images. In the past years, this method was successfully applied to nanocrystals of arbitrary shape, size, and composition (and its high accuracy and precision has been demonstrated). However, the counting results obtained from this statistical framework are so far presented without a visualization of the actual uncertainty about this estimate. In this paper, we present three approaches that can be used to represent counting results together with their statistical error, and discuss which approach is most suited for further use based on simulations and an experimental ADF STEM image.
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Affiliation(s)
- Annelies De Wael
- EMAT, University of Antwerp, Antwerp, Belgium
- NANOlab Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Annick De Backer
- EMAT, University of Antwerp, Antwerp, Belgium
- NANOlab Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Chu-Ping Yu
- EMAT, University of Antwerp, Antwerp, Belgium
- NANOlab Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Duygu Gizem Sentürk
- EMAT, University of Antwerp, Antwerp, Belgium
- NANOlab Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Ivan Lobato
- EMAT, University of Antwerp, Antwerp, Belgium
- NANOlab Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Sandra Van Aert
- EMAT, University of Antwerp, Antwerp, Belgium
- NANOlab Center of Excellence, University of Antwerp, Antwerp, Belgium
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31
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Nguyen THT, Nguyen TV, Luong QC, Ho TV, Faes C, Hens N. Understanding the transmission dynamics of a large-scale measles outbreak in Southern Vietnam. Int J Infect Dis 2022; 122:1009-1017. [PMID: 35907478 DOI: 10.1016/j.ijid.2022.07.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 07/21/2022] [Accepted: 07/21/2022] [Indexed: 10/16/2022] Open
Abstract
OBJECTIVES During 2018-2020, Southern Vietnam experienced a large measles outbreak of over 26,000 cases. We aimed to understand and quantify the measles spread in space-time dependence and the transmissibility during the outbreak. METHODS Measles surveillance reported cases between 1/2018 and 6/2020, vaccination coverage, and population data at provincial level were used. To illustrate the spatiotemporal pattern of disease spread, we employed the endemic-epidemic multivariate time series model decomposing measles risk additively into autoregressive, spatiotemporal, and endemic component. Likelihood-based estimation procedures were performed to determine the time-varying reproductive number Re of measles. RESULTS Our analysis shows that measles incidence was associated with vaccination coverage heterogeneity and spatial interaction between provincial units. The risk of infections was dominated by between-province transmission (36.1% to 78.8%), followed by local endogenous transmission (4.1% to 61.5%) whereas the endemic behavior had a relatively small contribution (2.1% to 33.4%) across provinces. In the exponential phase of the epidemic, Re was above the threshold with a maximum value of 2.34 (95%CI: 2.20-2.46). CONCLUSION Local vaccination coverage and human mobility are important factors contributing to the measles dynamics in Southern Vietnam and the high risk of inter-provincial transmission is of most concern. Strengthening disease surveillance is recommended, and further research is essential to understand the relative contribution of population immunity and control measures in measles epidemics.
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Affiliation(s)
- Thi Huyen Trang Nguyen
- Hasselt University, 3500 Hasselt, Belgium; The Pasteur Institute in Ho Chi Minh City, 70000 Ho Chi Minh City, Vietnam.
| | - Thuong Vu Nguyen
- The Pasteur Institute in Ho Chi Minh City, 70000 Ho Chi Minh City, Vietnam
| | - Quang Chan Luong
- The Pasteur Institute in Ho Chi Minh City, 70000 Ho Chi Minh City, Vietnam
| | - Thang Vinh Ho
- The Pasteur Institute in Ho Chi Minh City, 70000 Ho Chi Minh City, Vietnam
| | | | - Niel Hens
- Hasselt University, 3500 Hasselt, Belgium; The University of Antwerp, 2000 Antwerp, Belgium
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Vranckx M, Faes C, Molenberghs G, Hens N, Beutels P, Van Damme P, Aerts J, Petrof O, Pepermans K, Neyens T. A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data. Biom J 2022; 65:e2100186. [PMID: 35818698 PMCID: PMC9349774 DOI: 10.1002/bimj.202100186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 05/06/2022] [Accepted: 05/21/2022] [Indexed: 01/17/2023]
Abstract
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.
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Affiliation(s)
- Maren Vranckx
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium
| | - Christel Faes
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium
| | - Geert Molenberghs
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium,L‐BioStatDepartment of Public Health and Primary CareFaculty of MedicineKU LeuvenLeuvenBelgium
| | - Niel Hens
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium,Center for Health Economics Research and Modeling Infectious DiseasesVaccine and Infectious Disease InstituteUniversity of AntwerpAntwerpBelgium
| | - Philippe Beutels
- Center for Health Economics Research and Modeling Infectious DiseasesVaccine and Infectious Disease InstituteUniversity of AntwerpAntwerpBelgium
| | - Pierre Van Damme
- Center for Health Economics Research and Modeling Infectious DiseasesVaccine and Infectious Disease InstituteUniversity of AntwerpAntwerpBelgium
| | - Jan Aerts
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium
| | - Oana Petrof
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium
| | - Koen Pepermans
- Faculty of Social SciencesUniversity of AntwerpAntwerpBelgium
| | - Thomas Neyens
- I‐BioStatData Science InstituteHasselt UniversityHasseltBelgium,L‐BioStatDepartment of Public Health and Primary CareFaculty of MedicineKU LeuvenLeuvenBelgium
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De Pauw R, Claessens M, Gorasso V, Drieskens S, Faes C, Devleesschauwer B. Past, present, and future trends of overweight and obesity in Belgium using Bayesian age-period-cohort models. BMC Public Health 2022; 22:1309. [PMID: 35799159 PMCID: PMC9263047 DOI: 10.1186/s12889-022-13685-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 06/24/2022] [Indexed: 12/15/2022] Open
Abstract
Background Overweight and obesity are one of the most significant risk factors of the twenty-first century related to an increased risk in the occurrence of non-communicable diseases and associated increased healthcare costs. To estimate the future impact of overweight, the current study aimed to project the prevalence of overweight and obesity to the year 2030 in Belgium using a Bayesian age-period-cohort (APC) model, supporting policy planning. Methods Height and weight of 58,369 adults aged 18+ years, collected in six consecutive cross-sectional health interview surveys between 1997 and 2018, were evaluated. Criteria used for overweight and obesity were defined as body mass index (BMI) ≥ 25, and BMI ≥ 30. Past trends and projections were estimated with a Bayesian hierarchical APC model. Results The prevalence of overweight and obesity has increased between 1997 and 2018 in both men and women, whereby the highest prevalence was observed in the middle-aged group. It is likely that a further increase in the prevalence of obesity will be seen by 2030 with a probability of 84.1% for an increase in cases among men and 56.0% for an increase in cases among women. For overweight, it is likely to see an increase in cases in women (57.4%), while a steady state in cases among men is likely. A prevalence of 52.3% [21.2%; 83.2%] for overweight, and 27.6% [9.9%; 57.4%] for obesity will likely be achieved in 2030 among men. Among women, a prevalence of 49,1% [7,3%; 90,9%] for overweight, and 17,2% [2,5%; 61,8%] for obesity is most likely. Conclusions Our projections show that the WHO target to halt obesity by 2025 will most likely not be achieved. There is an urgent necessity for policy makers to implement effective prevent policies and other strategies in people who are at risk for developing overweight and/or obesity. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-13685-w.
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Affiliation(s)
- Robby De Pauw
- Department of Epidemiology and Public Health, Sciensano, Rue Juliette Wytsmanstraat 14, BE-1050, Brussels, Belgium. .,Department of Rehabilitation Sciences, Ghent University, Ghent, Belgium.
| | - Manu Claessens
- Department of Epidemiology and Public Health, Sciensano, Rue Juliette Wytsmanstraat 14, BE-1050, Brussels, Belgium
| | - Vanessa Gorasso
- Department of Epidemiology and Public Health, Sciensano, Rue Juliette Wytsmanstraat 14, BE-1050, Brussels, Belgium.,Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Sabine Drieskens
- Department of Epidemiology and Public Health, Sciensano, Rue Juliette Wytsmanstraat 14, BE-1050, Brussels, Belgium
| | - Christel Faes
- Data Science Institute, the Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Hasselt, Belgium
| | - Brecht Devleesschauwer
- Department of Epidemiology and Public Health, Sciensano, Rue Juliette Wytsmanstraat 14, BE-1050, Brussels, Belgium.,Department of Translational Physiology, Infectiology and Public Health, Ghent University, Merelbeke, Belgium
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Petrof O, Neyens T, Vranckx M, Nuyts V, Nemery B, Nackaerts K, Faes C. Disease mapping method comparing the spatial distribution of a disease with a control disease. Biom J 2022; 64:733-757. [PMID: 35146789 DOI: 10.1002/bimj.202000246] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 10/13/2021] [Accepted: 10/21/2021] [Indexed: 11/10/2022]
Abstract
Small-area methods are being used in spatial epidemiology to understand the effect of location on health and detect areas where the risk of a disease is significantly elevated. Disease mapping models relate the observed number of cases to an expected number of cases per area. Expected numbers are often calculated by internal standardization, which requires both accurate population numbers and disease rates per gender and/or age group. However, confidentiality issues or the absence of high-quality information about the characteristics of a population-at-risk can hamper those calculations. Based on methods in point process analysis for situations without accurate population data, we propose the use of a case-control approach in the context of lattice data, in which an unrelated, spatially unstructured disease is used as a control disease. We correct for the uncertainty in the estimation of the expected values, which arises by using the control-disease's observed number of cases as a representation of a fraction of the total population. We apply our methods to a Belgian study of mesothelioma risk, where pancreatic cancer serves as the control disease. The analysis results are in close agreement with those coming from traditional disease mapping models based on internally standardized expected counts. The simulation study results confirm our findings for different spatial structures. We show that the proposed method can adequately address the problem of inaccurate or unavailable population data in disease mapping analysis.
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Affiliation(s)
- Oana Petrof
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Thomas Neyens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium.,L-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Maren Vranckx
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Valerie Nuyts
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Benoit Nemery
- Centre for Environment and Health, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
| | - Kristiaan Nackaerts
- Department of Pneumology, University Hospital Leuven, KU Leuven, Leuven, Belgium
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
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Gressani O, Faes C, Hens N. Laplacian‐P‐splines for Bayesian inference in the mixture cure model. Stat Med 2022; 41:2602-2626. [PMID: 35699121 PMCID: PMC9542184 DOI: 10.1002/sim.9373] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 02/17/2022] [Accepted: 02/23/2022] [Indexed: 11/17/2022]
Abstract
The mixture cure model for analyzing survival data is characterized by the assumption that the population under study is divided into a group of subjects who will experience the event of interest over some finite time horizon and another group of cured subjects who will never experience the event irrespective of the duration of follow‐up. When using the Bayesian paradigm for inference in survival models with a cure fraction, it is common practice to rely on Markov chain Monte Carlo (MCMC) methods to sample from posterior distributions. Although computationally feasible, the iterative nature of MCMC often implies long sampling times to explore the target space with chains that may suffer from slow convergence and poor mixing. Furthermore, extra efforts have to be invested in diagnostic checks to monitor the reliability of the generated posterior samples. A sampling‐free strategy for fast and flexible Bayesian inference in the mixture cure model is suggested in this article by combining Laplace approximations and penalized B‐splines. A logistic regression model is assumed for the cure proportion and a Cox proportional hazards model with a P‐spline approximated baseline hazard is used to specify the conditional survival function of susceptible subjects. Laplace approximations to the posterior conditional latent vector are based on analytical formulas for the gradient and Hessian of the log‐likelihood, resulting in a substantial speed‐up in approximating posterior distributions. The spline specification yields smooth estimates of survival curves and functions of latent variables together with their associated credible interval are estimated in seconds. A fully stochastic algorithm based on a Metropolis‐Langevin‐within‐Gibbs sampler is also suggested as an alternative to the proposed Laplacian‐P‐splines mixture cure (LPSMC) methodology. The statistical performance and computational efficiency of LPSMC is assessed in a simulation study. Results show that LPSMC is an appealing alternative to MCMC for approximate Bayesian inference in standard mixture cure models. Finally, the novel LPSMC approach is illustrated on three applications involving real survival data.
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Affiliation(s)
- Oswaldo Gressani
- Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat), Data Science Institute Hasselt University Hasselt Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat), Data Science Institute Hasselt University Hasselt Belgium
| | - Niel Hens
- Interuniversity Institute for Biostatistics and statistical Bioinformatics (I‐BioStat), Data Science Institute Hasselt University Hasselt Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaxinfectio University of Antwerp Antwerp Belgium
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Natalia YA, Faes C, Neyens T, Molenberghs G. The COVID-19 wave in Belgium during the Fall of 2020 and its association with higher education. PLoS One 2022; 17:e0264516. [PMID: 35213651 PMCID: PMC8880857 DOI: 10.1371/journal.pone.0264516] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 02/13/2022] [Indexed: 11/30/2022] Open
Abstract
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.
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Affiliation(s)
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Thomas Neyens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- L-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Geert Molenberghs
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- L-BioStat, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
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Molenberghs G, Faes C, Verbeeck J, Deboosere P, Abrams S, Willem L, Aerts J, Theeten H, Devleesschauwer B, Bustos Sierra N, Renard F, Herzog S, Lusyne P, Van der Heyden J, Van Oyen H, Van Damme P, Hens N. COVID-19 mortality, excess mortality, deaths per million and infection fatality ratio, Belgium, 9 March 2020 to 28 June 2020. Euro Surveill 2022; 27. [PMID: 35177167 PMCID: PMC8855510 DOI: 10.2807/1560-7917.es.2022.27.7.2002060] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
BackgroundCOVID-19 mortality, excess mortality, deaths per million population (DPM), infection fatality ratio (IFR) and case fatality ratio (CFR) are reported and compared for many countries globally. These measures may appear objective, however, they should be interpreted with caution.AimWe examined reported COVID-19-related mortality in Belgium from 9 March 2020 to 28 June 2020, placing it against the background of excess mortality and compared the DPM and IFR between countries and within subgroups.MethodsThe relation between COVID-19-related mortality and excess mortality was evaluated by comparing COVID-19 mortality and the difference between observed and weekly average predictions of all-cause mortality. DPM were evaluated using demographic data of the Belgian population. The number of infections was estimated by a stochastic compartmental model. The IFR was estimated using a delay distribution between infection and death.ResultsIn the study period, 9,621 COVID-19-related deaths were reported, which is close to the excess mortality estimated using weekly averages (8,985 deaths). This translates to 837 DPM and an IFR of 1.5% in the general population. Both DPM and IFR increase with age and are substantially larger in the nursing home population.DiscussionDuring the first pandemic wave, Belgium had no discrepancy between COVID-19-related mortality and excess mortality. In light of this close agreement, it is useful to consider the DPM and IFR, which are both age, sex, and nursing home population-dependent. Comparison of COVID-19 mortality between countries should rather be based on excess mortality than on COVID-19-related mortality.
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Affiliation(s)
- Geert Molenberghs
- I-BioStat, KU Leuven, Leuven, Belgium.,Data Science Institute, I-BioStat, Universiteit Hasselt, Hasselt, Belgium
| | - Christel Faes
- Data Science Institute, I-BioStat, Universiteit Hasselt, Hasselt, Belgium
| | - Johan Verbeeck
- Data Science Institute, I-BioStat, Universiteit Hasselt, Hasselt, Belgium
| | - Patrick Deboosere
- Interface Demography (ID), Department of Sociology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Steven Abrams
- Global Health Institute (GHI), Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium.,Data Science Institute, I-BioStat, Universiteit Hasselt, Hasselt, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Jan Aerts
- Data Science Institute, I-BioStat, Universiteit Hasselt, Hasselt, Belgium
| | - Heidi Theeten
- Centre for the Evaluation of Vaccination (CEV), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Brecht Devleesschauwer
- Department of Translational Physiology, Infectiology and Public Health, Ghent University, Ghent, Belgium.,Department of Epidemiology and public health, Sciensano, Brussels, Belgium
| | | | - Françoise Renard
- Department of Epidemiology and public health, Sciensano, Brussels, Belgium
| | - Sereina Herzog
- Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | | | | | - Herman Van Oyen
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium.,Department of Epidemiology and public health, Sciensano, Brussels, Belgium
| | - Pierre Van Damme
- Centre for the Evaluation of Vaccination (CEV), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Niel Hens
- Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium.,Data Science Institute, I-BioStat, Universiteit Hasselt, Hasselt, Belgium
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Tanriver-Ayder E, Faes C, van de Casteele T, McCann SK, Macleod MR. Comparison of commonly used methods in random effects meta-analysis: application to preclinical data in drug discovery research. BMJ Open Sci 2022; 5:e100074. [PMID: 35047696 PMCID: PMC8647574 DOI: 10.1136/bmjos-2020-100074] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 11/26/2020] [Accepted: 01/08/2021] [Indexed: 11/25/2022] Open
Abstract
Background Meta-analysis of preclinical data is used to evaluate the consistency of findings and to inform the design and conduct of future studies. Unlike clinical meta-analysis, preclinical data often involve many heterogeneous studies reporting outcomes from a small number of animals. Here, we review the methodological challenges in preclinical meta-analysis in estimating and explaining heterogeneity in treatment effects. Methods Assuming aggregate-level data, we focus on two topics: (1) estimation of heterogeneity using commonly used methods in preclinical meta-analysis: method of moments (DerSimonian and Laird; DL), maximum likelihood (restricted maximum likelihood; REML) and Bayesian approach; (2) comparison of univariate versus multivariable meta-regression for adjusting estimated treatment effects for heterogeneity. Using data from a systematic review on the efficacy of interleukin-1 receptor antagonist in animals with stroke, we compare these methods, and explore the impact of multiple covariates on the treatment effects. Results We observed that the three methods for estimating heterogeneity yielded similar estimates for the overall effect, but different estimates for between-study variability. The proportion of heterogeneity explained by a covariate is estimated larger using REML and the Bayesian method as compared with DL. Multivariable meta-regression explains more heterogeneity than univariate meta-regression. Conclusions Our findings highlight the importance of careful selection of the estimation method and the use of multivariable meta-regression to explain heterogeneity. There was no difference between REML and the Bayesian method and both methods are recommended over DL. Multiple meta-regression is worthwhile to explain heterogeneity by more than one variable, reducing more variability than any univariate models and increasing the explained proportion of heterogeneity.
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Affiliation(s)
- Ezgi Tanriver-Ayder
- Centre for Clinical Brain Sciences, Edinburgh Medical School, The University of Edinburgh, Edinburgh, Scotland, UK.,Translational Medicine and Early Development Statistics, Janssen Pharmaceutica, Beerse, Antwerpen, Belgium
| | - Christel Faes
- Data Science Institute (DSI), Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Hasselt University, Hasselt, Limburg, Belgium
| | - Tom van de Casteele
- Translational Medicine and Early Development Statistics, Janssen Pharmaceutica, Beerse, Antwerpen, Belgium
| | - Sarah K McCann
- QUEST Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Malcolm R Macleod
- Centre for Clinical Brain Sciences, Edinburgh Medical School, The University of Edinburgh, Edinburgh, Scotland, UK
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Alsaiqali M, De Troeyer K, Casas L, Hamdi R, Faes C, Van Pottelbergh G. The Effects of Heatwaves on Human Morbidity in Primary Care Settings: A Case-Crossover Study. Int J Environ Res Public Health 2022; 19:832. [PMID: 35055653 PMCID: PMC8775418 DOI: 10.3390/ijerph19020832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 01/03/2022] [Accepted: 01/06/2022] [Indexed: 02/04/2023]
Abstract
PURPOSE This study assesses the potential acute effects of heatwaves on human morbidities in primary care settings. METHODS We performed a time-stratified case-crossover study to assess the acute effects of heatwaves on selected morbidities in primary care settings in Flanders, Belgium, between 2000 and 2015. We used conditional logistic regression models. We assessed the effect of heatwaves on the day of the event (lag 0) and X days earlier (lags 1 to X). The associations are presented as Incidence Density Ratios (IDR). RESULTS We included 22,344 events. Heatwaves are associated with increased heat-related morbidities such as heat stroke IDR 3.93 [2.94-5.26] at lag 0, dehydration IDR 3.93 [2.94-5.26] at lag 1, and orthostatic hypotension IDR 2.06 [1.37-3.10] at lag 1. For cardiovascular morbidities studied, there was only an increased risk of stroke at lag 3 IDR 1.45 [1.04-2.03]. There is no significant association with myocardial ischemia/infarction or arrhythmia. Heatwaves are associated with decreased respiratory infection risk. The IDR for upper respiratory infections is 0.82 [0.78-0.87] lag 1 and lower respiratory infections (LRI) is 0.82 [0.74-0.91] at lag 1. There was no significant effect modification by age or premorbid chronic disease (diabetes, hypertesnsion). CONCLUSION Heatwaves are associated with increased heat-related morbidities and decreased respiratory infection risk. The study of heatwaves' effects in primary care settings helps evaluate the impact of heatwaves on the general population. Primary care settings might be not suitable to study acute life-threatening morbidities.
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Affiliation(s)
- Mahmoud Alsaiqali
- Epidemiology and Social Medicine (ESOC), University of Antwerp, 2610 Antwerp, Belgium;
| | - Katrien De Troeyer
- Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium; (K.D.T.); (G.V.P.)
| | - Lidia Casas
- Epidemiology and Social Medicine (ESOC), University of Antwerp, 2610 Antwerp, Belgium;
| | - Rafiq Hamdi
- Royal Meteorological Institute of Belgium, B-1180 Brussels, Belgium;
| | - Christel Faes
- Data Science Institute (DSI), I-BioStat, Hasselt University, BE-3500 Hasselt, Belgium;
| | - Gijs Van Pottelbergh
- Department of Public Health and Primary Care, KU Leuven, 3000 Leuven, Belgium; (K.D.T.); (G.V.P.)
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40
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Yin N, Dellicour S, Daubie V, Franco N, Wautier M, Faes C, Van Cauteren D, Nymark L, Hens N, Gilbert M, Hallin M, Vandenberg O. Leveraging of SARS-CoV-2 PCR Cycle Thresholds Values to Forecast COVID-19 Trends. Front Med (Lausanne) 2021; 8:743988. [PMID: 34790677 PMCID: PMC8591051 DOI: 10.3389/fmed.2021.743988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 10/05/2021] [Indexed: 11/20/2022] Open
Abstract
Introduction: We assessed the usefulness of SARS-CoV-2 RT-PCR cycle thresholds (Ct) values trends produced by the LHUB-ULB (a consolidated microbiology laboratory located in Brussels, Belgium) for monitoring the epidemic's dynamics at local and national levels and for improving forecasting models. Methods: SARS-CoV-2 RT-PCR Ct values produced from April 1, 2020, to May 15, 2021, were compared with national COVID-19 confirmed cases notifications according to their geographical and time distribution. These Ct values were evaluated against both a phase diagram predicting the number of COVID-19 patients requiring intensive care and an age-structured model estimating COVID-19 prevalence in Belgium. Results: Over 155,811 RT-PCR performed, 12,799 were positive and 7,910 Ct values were available for analysis. The 14-day median Ct values were negatively correlated with the 14-day mean daily positive tests with a lag of 17 days. In addition, the 14-day mean daily positive tests in LHUB-ULB were strongly correlated with the 14-day mean confirmed cases in the Brussels-Capital and in Belgium with coinciding start, peak, and end of the different waves of the epidemic. Ct values decreased concurrently with the forecasted phase-shifts of the diagram. Similarly, the evolution of 14-day median Ct values was negatively correlated with daily estimated prevalence for all age-classes. Conclusion: We provide preliminary evidence that trends of Ct values can help to both follow and predict the epidemic's trajectory at local and national levels, underlining that consolidated microbiology laboratories can act as epidemic sensors as they gather data that are representative of the geographical area they serve.
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Affiliation(s)
- Nicolas Yin
- Department of Microbiology, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Simon Dellicour
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium.,Department of Microbiology, Immunology and Transplantation, Division of Clinical and Epidemiological Virology, Rega Institute, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Valery Daubie
- Department of Microbiology, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Nicolas Franco
- Department of Mathematics, Namur Centre for Complex Systems (Naxys), University of Namur, Namur, Belgium.,Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University (UHasselt), Hasselt, Belgium
| | - Magali Wautier
- Department of Microbiology, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Christel Faes
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University (UHasselt), Hasselt, Belgium
| | - Dieter Van Cauteren
- Scientific Directorate of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - Liv Nymark
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway.,Department of Health Management and Health Economics, University of Oslo, Oslo, Norway
| | - Niel Hens
- Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Data Science Institute, Hasselt University (UHasselt), Hasselt, Belgium.,Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Marius Gilbert
- Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium
| | - Marie Hallin
- Department of Microbiology, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Centre for Environmental Health and Occupational Health, School of Public Health, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Olivier Vandenberg
- Centre for Environmental Health and Occupational Health, School of Public Health, Université Libre de Bruxelles (ULB), Brussels, Belgium.,Clinical Research and Innovation Unit, Laboratoire Hospitalier Universitaire de Bruxelles - Universitair Laboratorium Brussel (LHUB-ULB), Université Libre de Bruxelles (ULB), Brussels, Belgium.,Division of Infection and Immunity, Faculty of Medical Sciences, University College London, London, United Kingdom
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41
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Otavova M, Faes C, Masquelier B, Devleesschauwer B. Mortality attributable to housing deprivation in Belgium between 1991 and 2015. Eur J Public Health 2021. [DOI: 10.1093/eurpub/ckab165.661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Background
Poor housing condition is associated with increased mortality. Our aim is to measure the inequality in mortality attributable to housing deprivation in Belgium.
Methods
We utilized data on housing conditions from the 1991 population census, and data on mortality from the National Register in Belgium between Jan 1, 1991 and Dec 31, 2015. An area-level composite score was developed and computed for 18 267 statistical sectors in Belgium. The score was based on indicators encompassing multiple dimensions: occupancy status and its density, absence of central heating, bathroom, toilet, kitchen, double glazing. These indicators were combined to a score, ranked and assigned to deciles. The mortality attributable to housing inequality was calculated as the difference between the observed and expected deaths. The expected deaths were computed by applying mortality in the least deprived decile to other deciles, stratified by 5-year age group, sex, and time.
Results
Our results show that 20% of all deaths, equating to 2 564 289 deaths, occurring between 1991-2015 can be attributable to inequalities in housing. The proportion of deaths attributable to inequality in housing increased over time and was higher for men (26%) than for women (14%). The difference in age-standardized mortality rates between the most and the least deprived groups increased over time from 31 to 37% and from 10 to 26% for men and women, respectively.
Conclusions
Housing conditions play an important role in mortality inequalities and ensuring good housing conditions is necessary for reducing inequalities.
Key messages
Poor housing condition is associated with increased mortality. Our results show that 20% of all deaths, equating to 2 564 289 deaths, occurring between 1991-2015 can be attributable to inequalities in housing.
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Affiliation(s)
- M Otavova
- Center for Demographic Research, UCLouvain, Louvain-la-Neuve, Belgium
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
| | - C Faes
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - B Masquelier
- Center for Demographic Research, UCLouvain, Louvain-la-Neuve, Belgium
| | - B Devleesschauwer
- Department of Epidemiology and Public Health, Sciensano, Brussels, Belgium
- Department of Veterinary Public Health and Food Safety, Ghent University, Ghent, Belgium
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Meuris C, Kremer C, Geerinck A, Locquet M, Bruyère O, Defêche J, Meex C, Hayette MP, Duchene L, Dellot P, Azarzar S, Maréchal N, Sauvage AS, Frippiat F, Giot JB, Léonard P, Fombellida K, Moutschen M, Durkin K, Artesi M, Bours V, Faes C, Hens N, Darcis G. Transmission of SARS-CoV-2 After COVID-19 Screening and Mitigation Measures for Primary School Children Attending School in Liège, Belgium. JAMA Netw Open 2021; 4:e2128757. [PMID: 34636913 PMCID: PMC8511974 DOI: 10.1001/jamanetworkopen.2021.28757] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
IMPORTANCE Recent data suggest a relatively low incidence of COVID-19 among children. The possible role that children attending primary school may play in the transmission of SARS-CoV-2 remains poorly understood. OBJECTIVE To gain a better understanding of the possible role of children in the transmission of SARS-CoV-2. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study was conducted from September 21 to December 31, 2020, in a primary school in Liège, Belgium, among a volunteer sample of 181 children, parents, and school employees. EXPOSURES Participants were tested for SARS-CoV-2 infection once a week for 15 weeks through throat washing, performed with 5 mL of saline and collected in a sterile tube after approximately 30 seconds of gargling. Quantitative reverse transcription-polymerase chain reaction was performed to detect SARS-CoV-2 infection. MAIN OUTCOMES AND MEASURES In case of test positivity, participants were asked to complete a questionnaire aimed at determining the timing of symptom onset and symptom duration. SARS-CoV-2 genetic sequencing was also performed. Confirmed cases were linked based on available information on known contacts and viral sequences. RESULTS A total of 181 individuals participated in this study, including 63 children (34 girls [54.0%]; mean [SD] age, 8.6 [1.9] years [range, 5-13 years]) and 118 adults (75 women [63.6%]; mean [SD] age, 42.5 [5.7] years [range, 30-59 years]). Forty-five individuals (24.9%) tested positive: 13 children (20.6%; 95% CI, 10.6%-30.6%) and 32 adults (27.1%; 95% CI, 19.1%-35.7%) (P = .34). Children were more often asymptomatic compared with adults (6 [46.2%; 95% CI, 19.1%-73.3%] vs 4 of 31 [12.9%; 95% CI, 1.3%-24.5%]; P = .04). The median duration of symptoms was shorter in children than in adults (0.00 days [IQR, 0.00-1.00 days] vs 15.00 days [IQR, 7.00-22.00 days]). A reconstruction of the outbreak revealed that most transmission events occurred between teachers and between children within the school. Of the observed household transmission events, most seemed to have originated from a child or teacher who acquired the infection at school. CONCLUSIONS AND RELEVANCE Despite the implementation of several mitigation measures, the incidence of COVID-19 among children attending primary school in this study was comparable to that observed among teachers and parents. Transmission tree reconstruction suggests that most transmission events originated from within the school. Additional measures should be considered to reduce the transmission of SARS-CoV-2 at school, including intensified testing.
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Affiliation(s)
- Christelle Meuris
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Cécile Kremer
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Anton Geerinck
- World Health Organization Collaborating Center for Public Health Aspects of Musculo-Skeletal Health and Ageing, Division of Public Health, Epidemiology and Health Economics, University of Liège, Liège, Belgium
| | - Medea Locquet
- World Health Organization Collaborating Center for Public Health Aspects of Musculo-Skeletal Health and Ageing, Division of Public Health, Epidemiology and Health Economics, University of Liège, Liège, Belgium
| | - Olivier Bruyère
- World Health Organization Collaborating Center for Public Health Aspects of Musculo-Skeletal Health and Ageing, Division of Public Health, Epidemiology and Health Economics, University of Liège, Liège, Belgium
| | - Justine Defêche
- Department of Clinical Microbiology, University of Liège, Liège, Belgium
| | - Cécile Meex
- Department of Clinical Microbiology, University of Liège, Liège, Belgium
| | | | - Loic Duchene
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Patricia Dellot
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Samira Azarzar
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Nicole Maréchal
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Anne-Sophie Sauvage
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Frederic Frippiat
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Jean-Baptiste Giot
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Philippe Léonard
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Karine Fombellida
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Michel Moutschen
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
| | - Keith Durkin
- Department of Human Genetics, Centre Hospitalier Universitaire Liège, Medical Genomics, Groupe Interdisciplinaire et Génoprotéomique Appliquée Research Center, University of Liège, Liège, Belgium
| | - Maria Artesi
- Department of Human Genetics, Centre Hospitalier Universitaire Liège, Medical Genomics, Groupe Interdisciplinaire et Génoprotéomique Appliquée Research Center, University of Liège, Liège, Belgium
| | - Vincent Bours
- Department of Human Genetics, Centre Hospitalier Universitaire Liège, Medical Genomics, Groupe Interdisciplinaire et Génoprotéomique Appliquée Research Center, University of Liège, Liège, Belgium
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Gilles Darcis
- Department of Infectious Diseases, Liège University Hospital, Liège, Belgium
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Verelst F, Hermans L, Vercruysse S, Gimma A, Coletti P, Backer JA, Wong KLM, Wambua J, van Zandvoort K, Willem L, Bogaardt L, Faes C, Jarvis CI, Wallinga J, Edmunds WJ, Beutels P, Hens N. SOCRATES-CoMix: a platform for timely and open-source contact mixing data during and in between COVID-19 surges and interventions in over 20 European countries. BMC Med 2021; 19:254. [PMID: 34583683 PMCID: PMC8478607 DOI: 10.1186/s12916-021-02133-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 09/16/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND SARS-CoV-2 dynamics are driven by human behaviour. Social contact data are of utmost importance in the context of transmission models of close-contact infections. METHODS Using online representative panels of adults reporting on their own behaviour as well as parents reporting on the behaviour of one of their children, we collect contact mixing (CoMix) behaviour in various phases of the COVID-19 pandemic in over 20 European countries. We provide these timely, repeated observations using an online platform: SOCRATES-CoMix. In addition to providing cleaned datasets to researchers, the platform allows users to extract contact matrices that can be stratified by age, type of day, intensity of the contact and gender. These observations provide insights on the relative impact of recommended or imposed social distance measures on contacts and can inform mathematical models on epidemic spread. CONCLUSION These data provide essential information for policymakers to balance non-pharmaceutical interventions, economic activity, mental health and wellbeing, during vaccine rollout.
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Affiliation(s)
- Frederik Verelst
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Lisa Hermans
- Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium.
| | - Sarah Vercruysse
- Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium
| | - Amy Gimma
- London School of Hygiene and Tropical Medicine, London, UK
| | - Pietro Coletti
- Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium
| | - Jantien A Backer
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Kerry L M Wong
- London School of Hygiene and Tropical Medicine, London, UK
| | - James Wambua
- Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium
| | | | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Laurens Bogaardt
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Christel Faes
- Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium
| | | | - Jacco Wallinga
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands
- Dept Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - W John Edmunds
- London School of Hygiene and Tropical Medicine, London, UK
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Niel Hens
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
- Data Science Institute and I-BioStat, Hasselt University, Hasselt, Belgium
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Faes C, Hens N, Gilbert M. On the timing of interventions to preserve hospital capacity: lessons to be learned from the Belgian SARS-CoV-2 pandemic in 2020. Arch Public Health 2021; 79:164. [PMID: 34517923 PMCID: PMC8436011 DOI: 10.1186/s13690-021-00685-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 08/29/2021] [Indexed: 01/22/2023] Open
Abstract
Using publicly available data on the number of new hospitalisations we use a newly developed statistical model to produce a phase portrait to monitor the epidemic allowing for assessing whether or not intervention measures are needed to keep hospital capacity under control. The phase portrait is called a cliquets' diagram, referring to the discrete alarm phases it points to. Using this cliquets' diagram we show that intervention measures were associated with an effective mitigation of a Summer resurgence but that too little too late was done to prevent a large autumn wave in Belgium.
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Affiliation(s)
- Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium.
| | - Niel Hens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Marius Gilbert
- Spatial Epidemiology Laboratory, Université Libre de Bruxelles, Brussels, Belgium
- Fonds National de la Recherche Scientifiques, Brussels, Belgium
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Darcis G, Meuris C, Kremer C, Faes C, Hens N. The Risk of Underestimating the Contribution of Children to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Pandemic. Clin Infect Dis 2021; 74:747. [DOI: 10.1093/cid/ciab571] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Gilles Darcis
- Department of Infectious Diseases, Liège University Hospital, Belgium
| | - Christelle Meuris
- Department of Infectious Diseases, Liège University Hospital, Belgium
| | - Cécile Kremer
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Christel Faes
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - Niel Hens
- I-BioStat, Data Science Institute, Hasselt University, Hasselt, Belgium
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Semakula M, Niragire F, Umutoni A, Nsanzimana S, Ndahindwa V, Rwagasore E, Nyatanyi T, Remera E, Faes C. The secondary transmission pattern of COVID-19 based on contact tracing in Rwanda. BMJ Glob Health 2021; 6:bmjgh-2020-004885. [PMID: 34103325 PMCID: PMC8189754 DOI: 10.1136/bmjgh-2020-004885] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/01/2021] [Accepted: 04/05/2021] [Indexed: 12/18/2022] Open
Abstract
Introduction COVID-19 has shown an exceptionally high spread rate across and within countries worldwide. Understanding the dynamics of such an infectious disease transmission is critical for devising strategies to control its spread. In particular, Rwanda was one of the African countries that started COVID-19 preparedness early in January 2020, and a total lockdown was imposed when the country had only 18 COVID-19 confirmed cases known. Using intensive contact tracing, several infections were identified, with the majority of them being returning travellers and their close contacts. We used the contact tracing data in Rwanda for understanding the geographic patterns of COVID-19 to inform targeted interventions. Methods We estimated the attack rates and identified risk factors associated to COVID-19 spread. We used Bayesian disease mapping models to assess the spatial pattern of COVID-19 and to identify areas characterised by unusually high or low relative risk. In addition, we used multiple variable conditional logistic regression to assess the impact of the risk factors. Results The results showed that COVID-19 cases in Rwanda are localised mainly in the central regions and in the southwest of Rwanda and that some clusters occurred in the northeast of Rwanda. Relationship to the index case, being male and coworkers are the important risk factors for COVID-19 transmission in Rwanda. Conclusion The analysis of contact tracing data using spatial modelling allowed us to identify high-risk areas at subnational level in Rwanda. Estimating risk factors for infection with SARS-CoV-2 is vital in identifying the clusters in low spread of SARS-CoV-2 subnational level. It is imperative to understand the interactions between the index case and contacts to identify superspreaders, risk factors and high-risk places. The findings recommend that self-isolation at home in Rwanda should be reviewed to limit secondary cases from the same households and spatiotemporal analysis should be introduced in routine monitoring of COVID-19 in Rwanda for policy making decision on real time.
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Affiliation(s)
- Muhammed Semakula
- Center for Excellence in Data Science, University of Rwanda - Kigali Campus, Kigali, Rwanda .,Centre for Statistics, Hasselt Biostatistics and statistical Bioinformatics Center, Diepenbeek, Limburg, Belgium
| | - FranÇois Niragire
- Applied Statistics, University of Rwanda College of Business and Economics - Gikondo Campus, Kigali, Rwanda
| | - Angela Umutoni
- Institute for HIV, Diseases Prevention and Control, Rwanda Biomedical Center, Kigali, Rwanda
| | - Sabin Nsanzimana
- Institute for HIV, Diseases Prevention and Control, Rwanda Biomedical Center, Kigali, Rwanda
| | - Vedaste Ndahindwa
- College of Medicine and Health Sciences, University of Rwanda, Kigali, Rwanda
| | - Edison Rwagasore
- Rwanda Biomedical Center, Rwanda Ministry of Health, Kigali, Rwanda
| | - Thierry Nyatanyi
- Institute for HIV, Diseases Prevention and Control, Rwanda Biomedical Center, Kigali, Rwanda
| | - Eric Remera
- Institute for HIV, Diseases Prevention and Control, Rwanda Biomedical Center, Kigali, Rwanda
| | - Christel Faes
- BioStat, Hasselt Biostatistics and statistical Bioinformatics Center, Diepenbeek, Limburg, Belgium
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Abrams S, Wambua J, Santermans E, Willem L, Kuylen E, Coletti P, Libin P, Faes C, Petrof O, Herzog SA, Beutels P, Hens N. Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories. Epidemics 2021; 35:100449. [PMID: 33799289 PMCID: PMC7986325 DOI: 10.1016/j.epidem.2021.100449] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 02/09/2021] [Accepted: 03/04/2021] [Indexed: 02/08/2023] Open
Abstract
Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months.
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Affiliation(s)
- Steven Abrams
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium; Global Health Institute, Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium.
| | - James Wambua
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium
| | - Eva Santermans
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium
| | - Lander Willem
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Elise Kuylen
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Pietro Coletti
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium
| | - Pieter Libin
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium; Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium; Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, University of Leuven, Leuven, Belgium
| | - Christel Faes
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium
| | - Oana Petrof
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium
| | - Sereina A Herzog
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Philippe Beutels
- Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
| | - Niel Hens
- Data Science Institute, Interuniversity Institute of Biostatistics and statistical Bioinformatics, UHasselt, Hasselt, Belgium; Centre for Health Economic Research and Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Antwerp, Belgium
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Coletti P, Libin P, Petrof O, Willem L, Abrams S, Herzog SA, Faes C, Kuylen E, Wambua J, Beutels P, Hens N. A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies. BMC Infect Dis 2021; 21:503. [PMID: 34053446 PMCID: PMC8164894 DOI: 10.1186/s12879-021-06092-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 04/20/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks. METHODS We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown. RESULTS Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period. CONCLUSIONS Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.
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Affiliation(s)
- Pietro Coletti
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium.
| | - Pieter Libin
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
- Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium
- Rega Institute for Medical Research, Katholieke Universiteit Leuven, Herestraat 49, Leuven, 3000, Belgium
| | - Oana Petrof
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
| | - Lander Willem
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
| | - Steven Abrams
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
- Global Health Institute, Family Medicine and Population Health, University of Antwerp, Wilrijk, Belgium
| | - Sereina A Herzog
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
- Institute for Medical Informatics, Statistics and Documentation, Auenbruggerplatz 2, Graz, 8036, Austria
| | - Christel Faes
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
| | - Elise Kuylen
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
| | - James Wambua
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
| | - Philippe Beutels
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
- School of Public Health and Community Medicine, The University of New South Wales, Sydney, Australia
| | - Niel Hens
- Data Science Institute, I-Biostat, Hasselt University, Agoralaan Gebouw D, Diepenbeek, 3590, Belgium
- Centre for Health Economics Research and Modelling Infectious Diseases, Vaccine and Infectious Disease Institute, University of Antwerp, Universiteitsplein 1, Wilrijk, 2610, Belgium
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Torneri A, Libin P, Scalia Tomba G, Faes C, Wood JG, Hens N. On realized serial and generation intervals given control measures: The COVID-19 pandemic case. PLoS Comput Biol 2021; 17:e1008892. [PMID: 33780436 PMCID: PMC8031880 DOI: 10.1371/journal.pcbi.1008892] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 04/08/2021] [Accepted: 03/18/2021] [Indexed: 01/08/2023] Open
Abstract
The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof. The generation and serial intervals are epidemiological quantities used to describe and predict an ongoing epidemic outbreak. These quantities are related to the contact pattern of individuals, since infection events can take place if infectious and susceptible individuals have a contact. Therefore, intervention measures that reduce the interactions between members of the population are expected to affect both the realized generation and serial intervals. For the current COVID-19 pandemic unprecedented interventions have been adopted worldwide, e.g. strict lockdown, isolation and quarantine, which influence the realized value of generation and serial intervals. The extent of the effect thereof depends on the efficacy of the control measure in place, on the relationship between symptom onset and infectiousness and on the proportion of infectious individuals that can be detected. To get more insight on this, we present an investigation that highlights the effect of quarantine and isolation on realized generation and serial intervals. In particular, we show that not only their variances but also their mean values can differ, suggesting that the use of the mean serial interval as a proxy for the mean generation time can lead to biased estimates of epidemiological quantities.
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Affiliation(s)
- Andrea Torneri
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- Interuniversity Institute of Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
- * E-mail:
| | - Pieter Libin
- Interuniversity Institute of Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
- Artificial Intelligence Lab, Department of Computer Science, Vrije Universiteit Brussel, Brussels, Belgium
- KU Leuven, Department of Microbiology and Immunology, Rega Institute for Medical Research, University of Leuven, Leuven, Belgium
| | | | - Christel Faes
- Interuniversity Institute of Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
| | - James G. Wood
- School of Public Health and Community Medicine, UNSW Sydney, Sydney, Australia
| | - Niel Hens
- Centre for Health Economic Research and Modelling Infectious Diseases, University of Antwerp, Antwerp, Belgium
- Interuniversity Institute of Biostatistics and statistical Bioinformatics, Data Science Institute, Hasselt University, Hasselt, Belgium
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Demoury C, Faes C, De Schutter H, Carbonnelle S, Rosskamp M, Francart J, Van Damme N, Van Bladel L, Van Nieuwenhuyse A, De Clercq EM. Childhood leukemia near nuclear sites in Belgium: An ecological study at small geographical level. Cancer Epidemiol 2021; 72:101910. [PMID: 33735659 DOI: 10.1016/j.canep.2021.101910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/14/2021] [Accepted: 02/14/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND A previous investigation of the occurrence of childhood acute leukemia around the Belgian nuclear sites has shown positive associations around one nuclear site (Mol-Dessel). In the following years, the Belgian Cancer Registry has made data available at the smallest administrative unit for which demographic information exists in Belgium, i.e. the statistical sector. This offers the advantage to reduce the potential misclassification due to large geographical scales. METHODS The current study performed for the period 2006-2016 uses Poisson models to investigate (i) the incidence of childhood acute leukemia within 20 km around the four Belgian nuclear sites, (ii) exposure-response relationships between cancer incidence and surrogate exposures from the nuclear sites (distance, wind direction frequency and exposure by hypothetical radioactive discharges taking into account historical meteorological conditions). All analyses are carried out at statistical sector level. RESULTS Higher incidence rate ratios were found for children <15 years (7 cases, RR = 3.01, 95% CI: 1.43;6.35) and children <5 years (< 5 cases, RR = 3.62, 95% CI: 1.35;9.74) living less than 5 km from the site of Mol-Dessel. In addition, there was an indication for positive exposure-response relationships with the different types of surrogate exposures. CONCLUSION Results confirm an increased incidence of acute childhood leukemia around Mol-Dessel, but the number of cases remains very small. Random variation cannot be excluded and the ecological design does not allow concluding on causality. These findings emphasize the need for more in-depth research into the risk factors of childhood leukemia, for a better understanding of the etiology of this disease.
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