Building a Simple Model to Assess the Impact of Case Investigation and Contact Tracing for Sexually Transmitted Diseases: Lessons From COVID-19
Bibliographic record
Abstract
Introduction: During the COVID-19 pandemic, the U.S. Centers for Disease Control and Prevention developed a simple spreadsheet-based tool to help state and local public health officials assess the performance and impact of COVID-19 case investigation and contact tracing in their jurisdiction. The applicability and feasibility of building such a tool for sexually transmitted diseases were assessed. Methods: The key epidemiologic differences between sexually transmitted diseases and respiratory diseases (e.g., mixing patterns, incubation period, duration of infection, and the availability of treatment) were identified, and their implications for modeling case investigation and contact tracing impact with a simple spreadsheet tool were remarked on. Existing features of the COVID-19 tool that are applicable for evaluating the impact of case investigation and contact tracing for sexually transmitted diseases were also identified. Results: Our findings offer recommendations for the future development of a spreadsheet-based modeling tool for evaluating the impact of sexually transmitted disease case investigation and contact tracing efforts. Generally, we advocate for simplifying sexually transmitted disease-specific complexities and performing sensitivity analyses to assess uncertainty. The authors also acknowledge that more complex modeling approaches might be required but note that it is possible that a sexually transmitted disease case investigation and contact tracing tool could incorporate features from more complex models while maintaining a user-friendly interface. Conclusions: A sexually transmitted disease case investigation and contact tracing tool could benefit from the incorporation of key features of the COVID-19 model, namely its user-friendly interface. The inherent differences between sexually transmitted diseases and respiratory viruses should not be seen as a limitation to the development of such tool.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".