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Lyme disease, the most prevalent vector-borne disease in North America, is increasing in incidence and geographic distribution as the tick vector,Ixodes scapularis , spreads to new regions. We re-construct the spatial-temporal invasion of the tick and human disease in the Midwestern US, a major focus of Lyme disease transmission, from 1967 to 2018, to analyse the influence of spatial factors on the geographic spread. A regression model indicates that three spatial factors—proximity to a previously invaded county, forest cover and adjacency to a river—collectively predict tick occurrence. Validation of the predictive capability of this model correctly predicts counties invaded or uninvaded with 90.6% and 98.5% accuracy, respectively. Reported incidence increases in counties after the first report of the tick; based on this modelled relationship, we identify 31 counties where we suspectI. scapularis already occurs yet remains undetected. Finally, we apply the model to forecast tick establishment by 2021 and predict 42 additional counties whereI. scapularis will probably be detected based upon historical drivers of geographic spread. Our findings leverage resources dedicated to tick and human disease reporting and provide the opportunity to take proactive steps (e.g. educational efforts) to prevent and limit transmission in areas of future geographic spread.

Landscape features predict the current and forecast the future geographic spread of Lyme disease