High-risk regions and outbreak modelling of tularemia in humans.
Sweden reports large and variable numbers of human tularemia cases, but the high-risk regions are anecdotally defined and factors explaining annual variations are poorly understood. Here, high-risk regions were identified by spatial cluster analysis on disease surveillance data for 1984-2012. Negative binomial regression with five previously validated predictors (including predicted mosquito abundance and predictors based on local weather data) was used to model the annual number of tularemia cases within the high-risk regions. Seven high-risk regions were identified with annual incidences of 3·8-44 cases/100 000 inhabitants, accounting for 56·4% of the tularemia cases but only 9·3% of Sweden's population. For all high-risk regions, most cases occurred between July and September. The regression models explained the annual variation of tularemia cases within most high-risk regions and discriminated between years with and without outbreaks. In conclusion, tularemia in Sweden is concentrated in a few high-risk regions and shows high annual and seasonal variations. We present reproducible methods for identifying tularemia high-risk regions and modelling tularemia cases within these regions. The results may help health authorities to target populations at risk and lay the foundation for developing an early warning system for outbreaks.
Laboratory for Molecular Infection Medicine Sweden,Department of Clinical Microbiology, Bacteriology,Umeå University,Umeå,Sweden.
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Desvars-Larrive
Laboratory for Molecular Infection Medicine Sweden,Department of Clinical Microbiology, Bacteriology,Umeå University,Umeå,Sweden.
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Liu
Public Health Agency of Sweden,Solna,Sweden.
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Hjertqvist
Laboratory for Molecular Infection Medicine Sweden,Department of Clinical Microbiology, Bacteriology,Umeå University,Umeå,Sweden.
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Sjöstedt
Laboratory for Molecular Infection Medicine Sweden,Department of Clinical Microbiology, Bacteriology,Umeå University,Umeå,Sweden.
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Johansson
Department of Mathematics and Mathematical Statistics,Umeå University,Umeå,Sweden.
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Rydén
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D004196:Disease Outbreaks; D005260:Female; D006801:Humans; D015994:Incidence; D007223:Infant; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D018570:Risk Assessment; D012621:Seasons; D062206:Spatial Analysis; D013548:Sweden / Q000453:epidemiology; D013997:Time Factors; D019371:Topography, Medical; D014406:Tularemia / Q000453:epidemiology*; D055815:Young Adult
Epidemiology; modelling; spatial cluster analysis; tularemia