Knowledge Integration to Support Networking for Laboratory Preparedness and Response to Emerging Pathogens
Bibliographic record
Abstract
IntroductionLaboratories play a critical role in facilitating timely recognition of and response to public health threats.However, capabilities and capacities vary widely among laboratories around the world.The scientific community recognizes that: 1) no single laboratory or network can effectively cover all health hazard threats and 2) connecting laboratories through networks enables scientific communities to harness and contribute their expertise in response to public health threats, while adding value and enhancing opportunities to enrich their own work.However, a consolidated and accessible inventory of laboratories that would enable this to happen does not exist.Public health laboratories serve the essential function of identifying etiologic agents of disease in an accurate and timely manner.However, the practicality and potential of these laboratories in the detection, monitoring, and reporting of threats over a wider geographic range is limited by unclear case definitions, inadequate laboratory capacity, and often, limited political will of local authorities to comply with International Health Regulations (IHR) 2005 (Baker andFidler, 2006) to be prepared to respond to public health emergencies of international concern (PHEIC).Furthermore, some countries are not member states of World Health Organization (WHO) and therefore have no obligation to comply with IHR.Global, regional, and national laboratory networks serve to alleviate these issues by streamlining the detection, monitoring, and reporting procedures for communicable diseases in order to effectively and significantly reduce the global or regional burden of disease.Laboratory networks are useful in establishing and maintaining standards such as molecular disease confirmation by providing member laboratories with standardized testing and reporting procedures, reagents, equipment, training, reference materials, quality control indicators, and technical support.Such collaboration between and among laboratories facilitated by networks allows for rapid and accurate provision of information regarding the magnitude of disease and the strains that are circulating in particular regions, leading to faster response and more effective control of the threat.Some example networks include the Global Polio Network (Hull et al, 1997) and the Global Measles and Rubella Network (Featherstone et al, 2003). www.intechopen.com New Research on Knowledge Management Technology 214Although significant scientific knowledge has been developed, tested and translated into successful public health interventions and leading to reduced infectious disease burden, the public remains vulnerable to epidemics and pandemics.Emerging infections such as HIV, SARS, avian influenza and the recent H1N1 pandemic are further exemplified by the emergence and global spread of multi-drug resistant pathogens, which threatens our ability to treat viral and bacterial infections in hospitals and in the community.Taken together these acts of nature have put enormous pressure on governments to act quickly to protect the public's health.The public have in turn, incurred high costs in terms of lives and implementation of countermeasures.These public health problems have resulted in society disturbance, economic loss and political expectations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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 source (direct Gemma or distilled Codex), 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".