Improving Surveillance of Human Tick-Borne Disease Risks: Spatial Analysis Using Multimodal Databases
Bibliographic record
Abstract
BACKGROUND: The extent of tick-borne disease (TBD) risk in the United States is generally unknown. Active surveillance using entomological measures, such as presence and density of infected nymphal Ixodes scapularis ticks, have served as indicators for assessing human risk, but results have been inconsistent and passive surveillance via public health systems suggests TBDs are underreported. OBJECTIVE: Research using various data sources and collection methods (eg, Google Trends, apps, and tick bite encounters [TBEs] reports) has shown promise for assessing human TBD risk. In that vein, and engaging a One Health perspective, this study used multimodal databases, geographically overlaying patient survey data on TBEs and concomitant reports of TBDs with data drawn from other sources, such as canine serological reports, to glean insights and to determine and assess the use of various indicators as proxies for human TBD risk. METHODS: This study used a mixed methods research strategy, relying on triangulation techniques and drawing on multiple data sources to provide insights into various aspects of human disease risk from TBEs and TBDs in the United States. A web-based survey was conducted over a 15-month period beginning in December 2020 to collect data on TBEs. To maximize the value of the covariate data, related analyses included TBE reports that occurred in the United States between January 1, 2000, and March 31, 2021. TBEs among patients diagnosed with Lyme disease were analyzed at the county level and compared to I scapularis and I pacificus tick presence, human cases identified by the Centers for Disease Control and Prevention (CDC), and canine serological data. Spatial analyses employed multilayer thematic mapping and other techniques. RESULTS: After cleaning, survey results showed a total of 249 (75.7%) TBEs spread across 148 respondents (61.9% of all respondents, 81.7% of TBE-positive respondents); 144 (4.7%) counties in 30 states (60%) remained eligible for analysis, with an average of 1.68 (SD 1.00) and median of 1 (IQR 1) TBEs per respondent. Analysis revealed significant spatial matching at the county level among patient survey reports of TBEs and disease risk indicators from the CDC and other official sources. Thematic mapping results included one-for-one county-level matching of reported TBEs with at least 1 designated source of human disease risk (ie, positive canine serological tests, CDC-reported Lyme disease, or known tick presence). CONCLUSIONS: Use of triangulation methods to integrate patient data on TBE recall with established canine serological reports, tick presence, and official human TBD information offers more granular, county-level information regarding TBD risk to inform clinicians and public health officials. Such data may supplement public health sources to offer improved surveillance and provide bases for developing robust proxies for TBD risk among humans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".