Members of SIMID are present at the 40th Annual Conference of the International Society for Clinical Biostatistics in Leuven from July 14-17, 2019. We are happy to promote our work and to meet other researchers to discuss future research opportunities.
Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling (Sereina Herzog)
The epidemiology of infectious diseases is commonly studied by cross-sectional serological surveys. The restriction on serum availability and/or financial resources raises the following question: Which samples in a serum bank should be tested, e.g. how should the age-based sampling be structured? We showed that simulation-based calculations in combination with mathematical modelling can be used for choosing the optimal allocation of a given number of samples. [poster]
A computationally-efficient method for probabilistic parameter threshold analysis for health economic evaluations (Zoë Pieters)
Currently, threshold analysis is performed in a deterministic way. In the presence of a non-linear relationship between input parameters and cost-effectiveness measures in the health economic model, the resulting threshold value might be biased. We propose a probabilistic approach for performing threshold analysis that is based on generalized additive models. [poster]
The analysis of Belgian serial serological survey data on mumps using a Bayesian mixture approach (Steven Abrams)
Serosurveillance is of quintessential importance, even in highly vaccinated populations, to prevent resurgence of vaccinepreventable diseases. Our novel methodology for serial serology, which is collected more often nowadays, enables the study of age and timespecific effects on the seropositivity in populations with vaccination strategies. Based on our findings, we conclude that targeted vaccination campaigns could be considered to lower the susceptibility to mumps infection in specific age groups and to avoid future outbreaks. [oral presentation]
Estimating the force of infection and incidence using routine data affected by outcome dependent sampling (Sereina Herzog)
In routine data, we are often faced with outcome dependent sampling (ODS), i.e. the probability to have a further observation from the same person depends on the outcome of the current one. For example, Austrian prenatal care includes screening for the early detection and treatment of toxoplasmosis infection: if the first test is negative there will be up to two further tests but if first test is positive then no further tests will be done. The ODS in this example leads to an overrepresentation of negative test results which needs to be accounted for when estimating the force of infection (FOI). We developed an estimator for the FOI for a Susceptible-Infected (SI) course of infection and thereby dealing with ODS. [poster]
Shifting patterns of seasonal influenza epidemics (Pietro Coletti)
Influenza activity shows a complex spatio-temporal pattern whose complete understanding is still missing. Here we study 30 years of influenza time series in France on a season by season approach. Our aim is to characterize recurrent timing pattern across seasons and assess how they change from the start of the outbreak to the time at which influenza activity reaches its peak. We do that clustering together seasons with similar timing patterns and exploring the possible drivers that lead to similar patterns. We are able to show that mobility drives synchronization only in clustered seasons, with a result that is still significant when considering the whole set of seasons. [poster]
Investigation of temporal changes in social contact rates in Flanders Belgium (Thang Hoang)
There is a lack of knowledge about whether or not mixing patterns change over years within a particular population and how that would affect infectious disease spreading. Our study is the first one that attempts to investigate whether or not contact rates remain stable over time by analysing results from two contact surveys conducted in Belgium in two different years (2006 and 2010). We found social contact patterns are stable over a limited time span of 5 years. [poster]