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Record W4411025038 · doi:10.1016/j.softx.2025.102206

sivirep: A package for strengthening epidemiological surveillance and report generation—Use case in Colombia

2025· article· en· W4411025038 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSoftwareX · 2025
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsEpidemiological surveillanceComputer scienceEpidemiologyComputer securityMedicine

Abstract

fetched live from OpenAlex

This paper presents sivirep , an open-source R package created to streamline epidemiological surveillance and reporting in public health. sivirep is part of the Epiverse TRACE-LAC initiative, which aims to strengthen the infrastructure and epidemic response to infectious diseases in Latin America and the Caribbean. This package automates the downloading, preprocessing, and preparation of data from Colombia’s epidemiological surveillance and reporting system, “SIVIGILA”. sivirep also provides a customizable R Markdown template for subsequent analysis and automatic generation of epidemiological reports. sivirep was developed in Spanish to improve the engagement of local users, but can be used worldwide, keeping both potential global uses aligned with local needs. This tool also can facilitate teaching and learning on SIVIGILA data source for students and researchers from various fields.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.387
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it