Semantic Mapping of Public Health Legislation and Emergency Legal Responses Using Natural Language Processing Techniques.

Abstract

Background: Public-health legislation is heterogeneous across jurisdictions, creating challenges for reproducible policy surveillance.

Methods: Six publicly available policy-surveillance datasets from the LawAtlas Policy Surveillance Portal were consolidated and analyzed. After excluding records coded as "No bills" and collapsing repeated longitudinal snapshots within source domains, the data comprised 4328 snapshot rows, 2126 jurisdiction-by-bill source-domain records, and 1534 unique bills. Observed legal coverage extended from 6 August 2020 to 20 May 2022. Semantic mapping used TF-IDF, truncated singular-value decomposition, and K-Means clustering. Records containing at least 10 alphabetic tokens formed the semantic-mapping sample (n = 1875). Cluster number was selected using the maximum cosine silhouette coefficient across k = 2-12. Independent validation used the six curated LawAtlas source-domain memberships as multilabel targets for 821 unique bills; unsupervised cluster assignments were not used as supervised ground truth.

Results: The maximum silhouette coefficient was 0.336 at k = 12, with five-seed stability yielding a mean pairwise adjusted Rand index of 0.718 (SD = 0.069). In the held-out validation set (n = 164), Logistic Regression achieved macro-F1 = 0.543 and micro-F1 = 0.752; five-fold cross-validation macro-F1 was 0.551 ± 0.039.

Conclusion: The findings support transparent semantic mapping and source-domain classification of public-health-law surveillance records. Temporal findings are descriptive, and operational use requires human legal review and continuing performance monitoring.