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Data from: Predicting spatial patterns of Sindbis virus (SINV) infection risk in Finland using vector, host and environmental data

Citation

Uusitalo, Ruut (2021), Data from: Predicting spatial patterns of Sindbis virus (SINV) infection risk in Finland using vector, host and environmental data , Dryad, Dataset, https://doi.org/10.5061/dryad.gxd2547km

Abstract

Pogosta disease is a mosquito-borne infection, caused by the Sindbis virus (SINV) with epidemics of febrile rash and arthritis in Northern Europe and South Africa. The virus is maintained in a transmission cycle between birds and mosquitoes. Resident grouse and migratory birds play a significant role as amplifying hosts and various mosquito species, including Aedes cinereus, Culex pipiens, Cx. torrentium and Culiseta morsitans are documented vectors. As specific treatments are not available for SINV infections and joint symptoms may persist, the public health burden is considerable in endemic areas. To predict the environmental and ecological suitability for SINV infections in Finland, we have applied a suite of geospatial and statistical modelling techniques to disease occurrence data. Using an ensemble approach, we first produced environmental suitability maps for potential SINV vectors in Finland. These suitability maps were then combined with host species and environmental data to identify the influential determinants for SINV infections, and to predict the risk of Pogosta disease in Finnish municipalities. This is the first study to utilise the spatial distribution of potential SINV vectors to predict the occurrence of Pogosta disease. Our predictions suggest that both the environmental suitability for vector species and the high risk of Pogosta disease are focused in geographically restricted areas. This provides evidence that presence of both SINV vector and host species can predict the occurrence of the disease. These results are essential for public health officials to inform risk assessments, and for future research to identify potential high-risk areas to prevent SINV infections.

Methods

This data includes all new data generated in the project, including the following: prediction maps for Ae. cinereus/geminus, Cx. pipiens/torrentium, and Cs. morsitans in Finland, model performance and variable importance comparison and predicted risk maps for Pogosta disease in Finland.