This paper presents EpiCorpus-BR, the first annotated corpus for Named Entity Recognition (NER) in Brazilian Portuguese epidemiological surveillance documents, covering the annual SIREVA-SUS reports (2013–2024) and complementary documents from the Brazilian Ministry of Health. The corpus comprises 8,314 textual units across 22 documents (6,358 narrative text sentences and 1,956 table rows), with 4,159 entity mentions in the silver set. The tagset consists of eight specialized categories (PATOGENO, SOROTIPO, FAIXA_ETARIA, LOCAL, PERIODO_TEMPORAL, METODO_LAB, METRICA_EPI, MANIFESTACAO_CLINICA), annotated via few-shot prompting with GPT-4o-mini and manually reviewed in a stratified sample of 280 units (180 text sentences and 100 table rows) by two independent annotators (κ = 0.76). Strict IOB2 evaluation with seqeval yields a combined micro-F1 of 0.77. METRICA_EPI is absent from narrative text but reaches F1 = 0.89 on table rows, which shows why the tabular sub-corpus must be included in the evaluation. A BERTimbau baseline fine-tuned on the silver corpus, with the gold held out, reaches micro-F1 = 0.73, showing that the corpus supports training a dedicated NER model. The corpus and code are publicly available.

We present an ongoing research project focused on the construction of a Universal Dependencies (UD) corpus of Portuguese epidemiological reports derived from documents published within the Brazilian public health system. We describe findings and challenges to build such a corpus from PDF reports processed through a controlled document extraction pipeline that contrasts layout-aware extraction with raw PDF text extraction, explicitly addressing the impact of tabular content on downstream syntactic analysis. Narrative text is annotated using multiple UD parsers for Portuguese, including widely used and state-of-the-art tools, and their outputs are systematically compared using descriptive structural indicators and targeted qualitative inspection. Our analysis highlights domain-specific challenges in epidemiological texts and shows that document extraction and representation choices have a stronger effect on parsing behavior than parser selection alone. Based on these findings, we identify robust preprocessing configurations and discuss design choices for a UD-epidemiological corpus to support future research on syntactic parsing, domain adaptation, and downstream natural language processing tasks in epidemiology and public health.