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

ABSTRACT: 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 suspect I. scapularis already occurs yet remains undetected. Finally, we apply the model to forecast tick establishment by 2021 and predict 42 additional counties where I. 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.

SUBMITTER: Gardner AM

PROVIDER: S-EPMC7779494 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Gardner Allison M AM Pawlikowski Natalie C NC Hamer Sarah A SA Hickling Graham J GJ Miller James R JR Schotthoefer Anna M AM Tsao Jean I JI Allan Brian F BF

Proceedings. Biological sciences 20201223 1941

Lyme disease, the most prevalent vector-borne disease in North America, is increasing in incidence and geographic distribution as the tick vector, <i>Ixodes scapularis</i>, 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 p ...[more]

PMID: 33352074

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Project description:Importance:The incidence and geographic range of Lyme disease continues to increase in the United States because of the expansion of Ixodes scapularis, the species of tick that is the main Lyme disease vector. Currently, no dynamic model for the disease spread exists that integrates information of both acarological and human case surveillance data. Objective:To characterize the spatiotemporal spread of Lyme disease in humans among counties in US endemic regions. Design, Setting, and Participants:This cross-sectional study modeled the spread of Lyme disease county-level case reporting, accounting for county-level demographic factors, environmental factors associated with tick presence and human exposure, and the spatiotemporal association between counties. The analyses were conducted between January and August 2019. The setting was 1405 counties in the following regions of the United States: West North Central, East North Central, New England, Middle Atlantic, and the South. Assessments were based on publicly available Lyme disease case data reported to the US Centers for Disease Control and Prevention (CDC) between January 2000 and December 2017. Main Outcomes and Measures:Probability of reporting the first case of Lyme disease by county by year. Results:Between 2000 and 2017, a total of 497 569 Lyme disease cases were reported to the CDC in the study area. Reporting a first case of Lyme disease was associated with a county's and county's neighbors' forest coverage, elevation, percentage of population living in the wildland-urban interface, tick presence, county's population size, proportion of neighbors reporting cases, and neighbors' years since first reporting. The model that included these variables showed high predictive power, with a mean area under the receiver operating characteristic curve of 81.1 (95% CI, 68.5-86.2). The model predicted the first reported Lyme disease case a mean (SD) of 5.5 (3.5) years earlier than was reported to the CDC, with a mean spread velocity estimated at 27.4 (95% CI, 13.6-54.4) km per year. Among 162 counties without reported cases, 47 (29.0%) had a high probability of reporting Lyme disease by 2018. The estimated mean time lag between the first reported case in a neighboring county and any county was 7 (95% CI, 3-8) years. Conclusions and Relevance:This study's findings suggest that, if updated regularly and expanded geographically, this predictive model could enable states and counties to develop more specific Lyme disease prevention and control plans, including improved sensitization of the general population and medical community.

Dataset Information

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

Publications

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

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