PHIN Preparedness: Outbreak Management
Bibliographic record
Abstract
As a result of the response to 9/11 and the subsequent anthrax attacks, CDC and its partners needed to collect a huge amount of case and specimen data. At that time CDC was faced with this requirement but had no standardized tools available to aid with these investigations. Outbreak Management (OM) is the PHIN functional area intended to support the needs of investigation, monitoring, management, analysis, and reporting of a public health event or act of bioterrorism. This presentation will give an overview of requirements for outbreak management systems that have been defined by the CDC and reviewed and refined with input from local and state public health subject matter experts, as well as demonstrate an application, the Outbreak Management System, that was developed for use in public health investigations that support these requirements. OM aids in the collection and analysis of data to support identifying and containing the outbreak. OM systems should be configurable to meet the needs of different types of outbreaks, and capture data related to cases, contacts, investigations, exposures, relationships, clinical and environmental specimens, laboratory results, vaccinations and treatments, travel history, and conveyance information. One clearly stated need is to have an application that allows for the definition and creation of new objects during the course of an investigation. Central to the functionality of a system supporting OM is the ability to collect data related to possible cases and exposures and to create traceable links between all appropriate entities. By tracing the mechanism of transmission and identifying the source of the outbreak, the appropriate outbreak response staff can more effectively contain the event. Systems supporting OM should also be integrated with early event detection, countermeasure administration, laboratory, and surveillance systems to achieve the primary goal of managing the response to and mitigating the effects of an outbreak. Unless standardized data can be exchanged and linked with other appropriate data (i.e., laboratory data) across public health during public health investigations, public health will not be able to adequately assess the source, methods of transmission, agents, and intervention strategies required for management of outbreaks. Without these data, delays will result in determining the affected and potentially exposed population. The SARS outbreak in Toronto and the MonkeyPox response in the U.S. illustrated the distinct needs to have systems with the ability to trace people possibly exposed to a person ill with a communicable disease, trace exposures to a disease vector including physical locale, or follow theoretical paths of conveyance via specific plane flights. These contact tracing capabilities can be efficiently tracked by systems such as the CDC’s Outbreak Management System (OMS). OMS manages complex relationships tracks linkages and provides public health officials with the ability to know who to investigate, manage and either prophylax or treat. As with all preparedness systems, outbreak management systems must be implemented and regularly used before an event, and be adaptable to handle emergency situations when they occur.
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.
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 | 0.000 | 0.000 |
| 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".