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Power law approximations of movement network data for modeling infectious disease spread

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Publication date 2014
Author Geilhufe Marc, Held Leonhard, Skrøvseth Stein Olav, Simonsen Gunnar S., Godtliebsen Fred,
Project Statistical methods for spatio-temporal modelling and prediction of infectious diseases
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Original article (peer-reviewed)

Journal Biometrical Journal
Volume (Issue) 56
Page(s) 363 - 382
Title of proceedings Biometrical Journal
DOI 10.1002/bimj.201200262


Globalization and increased mobility of individuals enable person-to-person transmitted infectious diseases to spread faster to distant places around the world, making good models for the spread increasingly important. We study the spatiotemporal pattern of spread in the remotely located and sparsely populated region of North Norway in various models with fixed, seasonal, and random effects. The models are applied to influenza A counts using data from positive microbiology laboratory tests as proxy for the underlying disease incidence. Human travel patterns with local air, road, and sea traffic data are incorporated as well as power law approximations thereof, both with quasi-Poisson regression and based on the adjacency structure of the relevant municipalities. We investigate model extensions using information about the proportion of positive laboratory tests, data on immigration from outside North Norway and by connecting population to the movement network. Furthermore, we perform two separate analyses for nonadults and adults as children are an important driver for influenza A. Comparisons of one-step-ahead predictions generally yield better or comparable results using power law approximations.