Clustering of susceptible individuals within households can drive measles outbreaks: an individual-based model exploration

When estimating important measures such as the herd immunity threshold, and the corresponding efforts required to eliminate measles, it is often assumed that susceptible individuals are uniformly distributed throughout populations. However, unvaccinated individuals may be clustered in a variety of ways, including by geographic location, by age, in schools, or in households. Here, we investigate to which extent different levels of within-household clustering of susceptible individuals may impact the risk and persistence of measles outbreaks. To this end, we apply an individual-based model, Stride, to a population of 600,000 individuals, using data from Flanders, Belgium. We construct a metric to estimate the level of within-household susceptibility clustering in the population. Furthermore, we compare realistic scenarios regarding the distribution of susceptible individuals within households in terms of their impact on epidemiological measures for outbreak risk and persistence. We find that higher levels of within-household clustering of susceptible individuals increase the risk, size and persistence of measles outbreaks. Ignoring within-household clustering thus leads to underestimations of required measles elimination and outbreak mitigation efforts. EK, LW and PB acknowledge support of the Antwerp Study Centre for Infectious Diseases (ASCID) at the University of Antwerp, and the Research Foundation Flanders (FWO) (research project G043815N and a postdoctoral fellowship 1234620N (LW)). NH acknowledges funding received from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement 682540-TransMID). Acknowledgements EK, LW and PB acknowledge support of the Antwerp Study Centre for Infectious Diseases (ASCID) at the University of Antwerp, and the Research Foundation Flanders (FWO) (research project G043815N and a postdoctoral fellowship 1234620N (LW)). NH acknowledges funding received from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement 682540-TransMID). Kuylen, E (corresponding author), Univ Antwerp, Ctr Hlth Econ Res & Modelling Infect Dis CHERMID, Vaccine & Infect Dis Inst, Antwerp, Belgium ; Hasselt Univ, Data Sci Inst DSI, Hasselt, Belgium. elise.kuylen@uantwerpen.be