Search results

Dynamic transmission models are essential to design and evaluate control strategies for airborne infections. Our objective was to develop a dynamic transmission model for seasonal influenza allowing to evaluate the impact of vaccinating specific age groups on the incidence of infection, disease and mortality. Projections based on such models heavily rely on assumed 'input' parameter values. In previous seasonal influenza models, these parameter values were commonly chosen ad hoc, ignoring between-season variability and without formal model validation or sensitivity analyses. We propose to directly estimate the parameters by fitting the model to age-specific influenza-like illness (ILI) incidence data over multiple influenza seasons. We used a weighted least squares (WLS) criterion to assess model fit and applied our method to Belgian ILI data over six influenza seasons. After exploring parameter importance using symbolic regression, we evaluated a set of candidate models of differing complexity according to the number of season-specific parameters. The transmission parameters (average R0, seasonal amplitude and timing of the seasonal peak), waning rates and the scale factor used for WLS optimization, influenced the fit to the observed ILI incidence the most. Our results demonstrate the importance of between-season variability in influenza transmission and our estimates are in line with the classification of influenza seasons according to intensity and vaccine matching. ; NG is beneficiary of a postdoctoral grant from the AXA Research Fund. LW acknowledges support from an interdisciplinary doctoral grant of the University of Antwerp (Bijzonder Onderzoeksfonds, BOF). YV acknowledges support from a doctoral grant of Hasselt University (BOF11D04FAEC). NH acknowledges support from the Antwerp University Scientific Chair in Evidence-Based Vaccinology, financed in 2009-2014 by an unrestricted gift from Pfizer. Support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy) is gratefully acknowledged. This study was co-financed by the Health Care Knowledge Centre (KCE) of the Belgian Federal government and benefited from discussions held as part of the KCE's expert committee and with Joke Bilcke, Adriaan Blommaert and Pieter Neels. We thank Francoise Wuillaume, Viviane van Casteren and Isabelle Thomas (Scientific Institute for Public Health) for collecting and providing sentinel data on ILI and influenza, and Nancy Thiry for useful discussions. The computational resources and services used in this work were provided by the Hercules Foundation and the Flemish Government - Department EWI. We thank Geert Jan Bex for support with using the VSC cluster.

Governments worldwide are relying on the COVID-19 vaccines as the solution for ending the coronavirus pandemic and the resulting crisis. Although scientific progress in the development of a vaccine has been astonishing, policymakers are facing an extra hurdle as increasingly more people appear to be hesitant in their intention to take such a vaccine. Based on a large Corona survey in Belgium, this study aims to explain the vaccination intention by linking it to trust in government and experts, while accounting for individuals' risk perceptions and prosocialness.

Governments worldwide are relying on the COVID-19 vaccines as the solution for ending the coronavirus pandemic and the resulting crisis. Although scientific progress in the development of a vaccine has been astonishing, policymakers are facing an extra hurdle as increasingly more people appear to be hesitant in their intention to take such a vaccine. Based on a large Corona survey in Belgium, this study aims to explain the vaccination intention by linking it to trust in government and experts, while accounting for individuals' risk perceptions and prosocialness.