This paper shows how to model seroprevalence data using change-point fractional polynomials (FPs). The inclusion of a change point in the FP framework allows to detect distortions arising from common (often untestable) assumptions made in the estimation of the age-specific prevalence and force of infection from cross-sectional data. The method is motivated using seroprevalence data on the parvovirus B19 and the varicella zoster virus in Belgium. ; We gratefully acknowledge two referees and an associate editor for provoking thoughts that have led to an improved version of the manuscript. This work was based on a serum sample collected for the European Commission's ESEN2-project. We are grateful to the Institute of Public Health, Brussels (Dr Robert Vranckx, Dr Veronik Hutse) for assistance with PVB19 testing. This work is part of the research project MSM 0021620839 that has been funded by 'SIMID', a strategic basic research project funded by the institute for the Promotion of Innovation by Science and Technology in Flanders, project number 060081, by the Fund of Scientific Research (Research Grant G039304) in Flanders, Belgium, by the IAP research network number P6/03 of the Belgian Government (Belgian Science Policy) and by the Grant Agency of Charles University, project number 252387/2007. This work benefited from discussions held in POLYMOD, a European Commission project funded within the Sixth Framework Programme, contract number: SSP22-CT-2004-502084.
The force of infection, describing the rate at which a susceptible person acquires an infection, is a key parameter in models estimating the infectious disease burden, and the effectiveness and cost-effectiveness of infectious disease prevention. Since Muench formulated the first catalytic model to estimate the force of infection from current status data in 1934, exactly 75 years ago, several authors addressed the estimation of this parameter by more advanced statistical methods, while applying these to seroprevalence and reported incidence/case notification data. In this paper we present an historical overview, discussing the relevance of Muench's work, and we explain the wide array of newer methods with illustrations on pre-vaccination serological survey data of two airborne infections: rubella and parvovirus B19. We also provide guidance on deciding which method(s) to apply to estimate the force of infection, given a particular set of data. ; We thank the editor and both referees for their valuable suggestions that have led to an improved version of the manuscript. This work was supported by research project (MSM 0021620839), funded by 'SIMID', a strategic basic research project funded by the institute for the Promotion of Innovation by Science and Technology in Flanders (IWT) (project number 06008); by the Fund of Scientific Research (FWO, Research Grant G039304) in Flanders, Belgium; and by the TAP research network (no. P6/03) of the Belgian Government (Belgian Science Policy). The R-code used to analyse the datasets in this manuscript is available from the authors.
The basic reproduction number R0 and the effective reproduction number R are pivotal parameters in infectious disease epidemiology, quantifying the transmission potential of an infection in a population. We estimate both parameters from 13 pre-vaccination serological data sets on varicella zoster virus (VZV) in 12 European countries and from population-based social contact surveys under the commonly made assumptions of endemic and demographic equilibrium. The fit to the serology is evaluated using the inferred effective reproduction number R as a model eligibility criterion combined with AIC as a model selection criterion. For only 2 out of 12 countries, the common choice of a constant proportionality factor is sufficient to provide a good fit to the seroprevalence data. For the other countries, an age-specific proportionality factor provides a better fit, assuming physical contacts lasting longer than 15 min are a good proxy for potential varicella transmission events. In all countries, primary infection with VZV most often occurs in early childhood, but there is substantial variation in transmission potential with R0 ranging from 2.8 in England and Wales to 7.6 in The Netherlands. Two non-parametric methods, the maximal information coefficient (MIC) and a random forest approach, are used to explain these differences in R0 in terms of relevant country-specific characteristics. Our results suggest an association with three general factors: inequality in wealth, infant vaccination coverage and child care attendance. This illustrates the need to consider fundamental differences between European countries when formulating and parameterizing infectious disease models. ; ES acknowledges support from a Methusalem research grant from the Flemish Government. NG is beneficiary of a postdoctoral grant from the AXA Research Fund. NH acknowledges support from the Antwerp University scientific chair in Evidence-Based Vaccinology, financed in 2009-2014 by an unrestricted gift from Pfizer. AM is currently receiving funding from the European Research Council under the European Union's Seventh Framework Program(FP7/2007-2013)/ERC Starting Grant [Agreement No. 283955]. Support from the IAP Research Network P7/06 of the Belgian State(Belgian Science Policy) is gratefully acknowledged. The computational resources and services used in this work were provided by the Hercules Foundation and the Flemish Government - department EWI. This study was initiated as part of POLYMOD, a European Commission project funded within the Sixth Framework Programme, Contract number: SSP22-CT-2004-502084.
Objective To assess between-hospital variations in standardized in-hospital mortality ratios of community-acquired pneumonia (CAP), and identify possible leads for quality improvement. Design We used an administrative database to estimate standardized in-hospital mortality ratios for 111 Belgian hospitals, by carrying out a set of hierarchical logistic regression models, intended to disentangle therapeutic attitudes and biases. To facilitate the detection of false-negative/positive results, we added an inconclusive zone to the funnel plots, derived from the results of the study. Data quality was validated by comparison with (i) alternative data from the largest Belgian Sickness Fund, (ii) published German hospital data and (iii) the results of an on-site audit. Setting All Belgian hospital discharge records from 2004 to 2007. Study participants A total of 111 776 adult patients were admitted for CAP. Main outcome measure Risk-adjusted standardized in-hospital mortality ratios. Results Out of the 111 hospitals, we identified five and six outlying hospitals, with standardized mortality ratios of CAP consistently on the extremes of the distribution, as providing possibly better or worse care, respectively, and 18 other hospitals as having possible quality weaknesses/strengths. At the individuals' level of the analysis, adjusted odds ratios showed the paramount importance of old age, comorbidity and mechanical ventilation. The data compared well with the different validation sources. Conclusions Despite the limitations inherent to administrative data, it seemed possible to establish inter-hospital differences in standardized in-hospital mortality ratios of CAP and to identify leads for quality improvement. Monitoring is needed to assess progress in quality. ; A.B.'s participation is funded by the University of Antwerp (UA)'s concerted research action number 23405 (BOF-GOA). N.H. holds the UA Scientific Chair in Evidence Based Vaccinology, financed in 2011-2014 by a gift from Pfizer. G.M., P.B., N.H. and A.B. acknowledge support from a Methusalem research grant from the Flemish government. G.M. and N.H. received funding from IAP research Network P7/06 of the Belgian Government (Belgian Science Policy).
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. ; This work received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (PC and NH, grant number 682540 – TransMID project, PL, NH, PB grant number 101003688 – EpiPose project). SA and NH gratefully acknowledge support from the Fonds voor Wetenschappelijk Onderzoek (FWO) (RESTORE project – G0G2920N). LW received funding from the Research Foundation Flanders (1234620N). PL received funding from the Research Foundation Flanders (post-doctoral grant 1242021N). The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government. We thank several researchers from the SIMID COVID-19 consortium from the University of Antwerp and Hasselt University for numerous constructive discussions and meetings. We thank Giulia Pullano, Laura Di Domenico and Vittoria Colizza for useful discussions. The authors are also very grateful for access to the data from the Belgian Scientific Institute for Public Health, Sciensano.
In 2006, Belgium was the first country in the European Union to recommend rotavirus vaccination in the routine infant vaccination schedule and rapidly achieved high vaccine uptake (86-89% in 2007). We used regional and national data sources up to 7 years post-vaccination to study the impact of vaccination on laboratory-confirmed rotavirus cases and rotavirus-related hospitalisations and deaths. We showed that (i) from 2007 until 2013, vaccination coverage remained at 79-88% for a complete course, (ii) in children 0-2 years, rotavirus cases decreased by 79% (95% confidence intervals (CI): 68-89%) in 2008-2014 compared to the pre-vaccination period (1999-2006) and by 50% (95% CI: 14-82%) in the age group >= 10 years, (iii) hospitalisations for rotavirus gastroenteritis decreased by 87% (95% CI: 84-90%) in 2008-2012 compared to the pre-vaccination period (2002-2006), (iv) median age of rotavirus cases increased from 12 months to 17 months and (v) the rotavirus seasonal peak was reduced and delayed in all post-vaccination years. The substantial decline in rotavirus gastroenteritis requiring hospitalisations and in rotavirus activity following introduction of rotavirus vaccination is sustained over time and more pronounced in the target age group, but with evidence of herd immunity. ; JB is supported by a postdoctoral grant from the Research Foundation - Flanders (FWO). BO is supported by a PhD fellowship from the Research Foundation - Flanders (FWO). AB is supported by the University of Antwerp's concerted research action number 23405 (BOF-GOA). The authors thank the sentinel laboratory network for their collaboration.
Introduction: The emergence of Plasmodium falciparum (Pf) resistance to artemisinin (PfART-R) in Africa is a worrisome situation that would annihilate the progress made in reducing the global burden of malaria. The discovery of mutations occurring in portions of the Pf gene sequence encoding kelch 13 (PfK13) – propeller domain and inducing PfART-R, has provided unprecedented opportunities for monitoring such resistance at large scale. Aim: This study aimed to review the PfK13 allelic polymorphism and its spatial distribution in Africa for drawing a baseline for subsequent epidemiological surveillance and containment efforts of PfART-R. Methods: A systematic review was performed according to PRISMA guidelines through six electronic databases consulted up to December 2018. Studies assessing the PfK13 gene in any of the 54 African countries were explored and data related to individual single nucleotide polymorphisms from each report and sampling location were geo-referenced and locus-referenced to be uploaded on maps displaying spatial and molecular patterns. Results: From 8,678 screened records, 50 reports were identified as eligible providing 22,739 Pf isolates successfully sequenced for the PfK13 and originating from 109 sites surveyed in 41 African countries. Overall 619 nonsynonymous (NS) mutants (2.7% of sequenced isolates) were reported at varied relative frequencies (0.5 to 50%) most often being K189T and A578S.Intermediate proportions (30 to 50%) of NS mutants were found in Western and Eastern Africa, moderate proportions (10 to 20%) in Middle Africa and low to very low proportions (<5%), elsewhere. NS mutations were not detected in 11 of 41 sampling countries. A total of 8 PfK13 NS mutations (F446I, C469Y, R515K, S522C, P553L, V568G, P574L, and A675V) out of 24 known as "associated molecular markers" for PfART-R were noticed at relative frequencies from 0.08 to 10.2%. One NS mutation (M476I) out of 6 established as "validated molecular markers" for PfART-R was reported at a relative frequency of 0.42%. Possible foci of NS mutations were noticed in Eastern, Western, and Middle Africa. Conclusion: Africa has noticed rare but alarming signals of possible emergence of Pf-ART-R. Proactive surveillance strategies are needed to be established in different African regions to refrain from massive development of resistance. ; Peer reviewed
