Seven challenges for metapopulation models of epidemics, including households models

Frank Ball, Tom Britton, Thomas House, Valerie Isham, Denis Mollison, Lorenzo Pellis, Gianpaolo Scalia Tomba

    Research output: Contribution to journalArticlepeer-review

    Abstract

    This paper considers metapopulation models in the general sense, i.e. where the population is partitioned into sub-populations (groups, patches,...), irrespective of the biological interpretation they have, e.g. spatially segregated large sub-populations, small households or hosts themselves modelled as populations of pathogens. This framework has traditionally provided an attractive approach to incorporating more realistic contact structure into epidemic models, since it often preserves analytic tractability (in stochastic as well as deterministic models) but also captures the most salient structural inhomogeneity in contact patterns in many applied contexts. Despite the progress that has been made in both the theory and application of such metapopulation models, we present here several major challenges that remain for future work, focusing on models that, in contrast to agent-based ones, are amenable to mathematical analysis. The challenges range from clarifying the usefulness of systems of weakly-coupled large sub-populations in modelling the spread of specific diseases to developing a theory for endemic models with household structure. They include also developing inferential methods for data on the emerging phase of epidemics, extending metapopulation models to more complex forms of human social structure, developing metapopulation models to reflect spatial population structure, developing computationally efficient methods for calculating key epidemiological model quantities, and integrating within- and between-host dynamics in models.

    Original languageEnglish
    Pages (from-to)63-67
    Number of pages5
    JournalEpidemics
    Volume10
    DOIs
    Publication statusPublished - Mar 2015

    Keywords

    • Metapopulations
    • Large sub-populations
    • Households

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