Power law approximations of movement network data for modeling infectious disease spread
| Autor: | Marc, Geilhufe, Leonhard, Held, Stein Olav, Skrøvseth, Gunnar S, Simonsen, Fred, Godtliebsen |
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| Přispěvatelé: | University of Zurich, Geilhufe, Marc |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Adult
Aged 80 and over Biometry Models Statistical Adolescent Norway Influenza A Virus H3N2 Subtype Infant Newborn Infant Transportation 610 Medicine & health 10060 Epidemiology Biostatistics and Prevention Institute (EBPI) Emigration and Immigration Middle Aged Communicable Diseases Young Adult Air Travel Influenza A Virus H1N1 Subtype Child Preschool Influenza Human Humans 1804 Statistics Probability and Uncertainty 2613 Statistics and Probability Child Aged |
| Zdroj: | Biometrical Journal |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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