Using individual-based models to look beyond the horizon: The changing effects of household-based clustering of susceptibility to measles in the next 20 years

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Abstract

Recent measles outbreaks in regions with a high overall vaccination coverage have drawn attention to other factors - aside from the overall immunity level - determining the spread of measles in a population, such as heterogeneous social mixing behavior and vaccination behavior. As households are an important context for measles transmission, the clustering of susceptible individuals within households can have a decisive effect on the risk for measles outbreaks. However, as the population ages and household constitutions change over the next 20 years, that effect may change as well. To adequately plan for the control and eventual elimination of measles, we need to understand how the effect of within-household susceptibility clustering will evolve. Individual-based models enable us to represent the different levels of heterogeneity in a population that are necessary to understand the spread of a disease in a highly immunized population. In this paper, we use such an individual-based model to investigate how the effect of household-based susceptibility clustering is expected to change over the next two decades in Flanders, Belgium. We compare different scenarios regarding the level of within-household susceptibility clustering for three different calendar years between 2020 and 2040, using projections for the age distribution of the population, the constitution of households and age-specific immunity levels. We find that a higher level of susceptibility clustering within households increases the risk for measles outbreaks and their potential to spread through the population, in current as well as in future populations.

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Kuylen, E., Liesenborgs, J., Broeckhove, J., & Hens, N. (2020). Using individual-based models to look beyond the horizon: The changing effects of household-based clustering of susceptibility to measles in the next 20 years. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12137 LNCS, pp. 385–398). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-50371-0_28

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