Seven challenges for metapopulation models of epidemics, including households models
2015 (English)In: Epidemics, ISSN 1755-4365, E-ISSN 1878-0067, Vol. 10, 63-67 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2015. Vol. 10, 63-67 p.
Metapopulations, Large sub-populations, Households
Health Sciences Mathematics
IdentifiersURN: urn:nbn:se:su:diva-117008DOI: 10.1016/j.epidem.2014.08.001ISI: 000352226900015PubMedID: 25843386OAI: oai:DiVA.org:su-117008DiVA: diva2:810700