ArticleEpidemiology and infection2017
High-risk regions and outbreak modelling of tularemia in humans.
Article in Epidemiology and infection, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Tularemia: Historical Perspectives and Current Challenges of a Re-Emerging Zoonosis.Biomedicines · 2026Review
- Animal modelling with theMicrobiology (Reading, England) · 2025Article
- Tularemia resurgence in Sweden 2023: A call to arms for health.New microbes and new infections · 2024Article
- Analyses of human immune responses toFrontiers in immunology · 2023Article
- Tularemia as a Mosquito-Borne Disease.Microorganisms · 2020Review
- Implications of Projected Hydroclimatic Change for Tularemia Outbreaks in High-Risk Areas across Sweden.International journal of environmental research and public health · 2020Article
- Seroepidemiology, Spatial Distribution, and Risk Factors ofThe American journal of tropical medicine and hygiene · 2020Article
- Potential for Hydroclimatically Driven Shifts in Infectious Disease Outbreaks: The Case of Tularemia in High-Latitude Regions.International journal of environmental research and public health · 2019Article
- Tularemia as a waterborne disease: a review.Emerging microbes & infections · 2019Review
- The Tick-Borne Diseases STING study: Real-time PCR analysis of three emerging tick-borne pathogens in ticks that have bitten humans in different regions of Sweden and the Aland islands, Finland.Infection ecology & epidemiology · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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