Geographic Expansion of Lyme Disease in Michigan, 2000–2014
Bibliographic record
Abstract
Abstract Background Most Lyme disease cases in the Midwestern United States are reported in Minnesota and Wisconsin. In recent years, however, a widening geographic extent of Lyme disease has been noted with evidence of expansion eastwards into Michigan and neighboring states with historically low incidence rates. Methods We collected confirmed and probable cases of Lyme disease from 2000 through 2014 from the Michigan Department of Health and Human Services, entering them in a geographic information system. We performed spatial focal cluster analyses to characterize Lyme disease expansion. We compared the distribution of human cases with recent Ixodes scapularis tick distribution studies. Results Lyme disease cases in both the Upper and Lower Peninsulas of Michigan expanded more than 5-fold over the study period. Although increases were seen throughout the Upper Peninsula, the Lower Peninsula particularly expanded along the Indiana border north along the eastern shore of Lake Michigan. Human cases corresponded to a simultaneous expansion in established I scapularis tick populations. Conclusions The geographic distribution of Lyme disease cases significantly expanded in Michigan between 2000 and 2014, particularly northward along the Lake Michigan shore. If such dynamic trends continue, Michigan—and possibly neighboring areas of Indiana, Ohio, and Ontario, Canada—can expect a continued increase in Lyme disease cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".