ABSTRACT
The importance of vigilance within organizations working with high-risk biological material receives increasing attention. However, an in-depth and comprehensive tool, dedicated to increase awareness of potential risks and to assess an organization's current biosecurity vulnerabilities, has not been available yet. We developed the "Biosecurity Vulnerability Scan," a web tool that identifies biosecurity gaps in an organization based on eight biosecurity pillars of good practice. Although the tool aims primarily to assist biosafety and biosecurity officers, it can also be useful to researchers working with dangerous pathogens, their principal investigators, management, or those responsible for security issues in the life sciences. Results are only stored locally and are provided in an "overview report," which includes information on relevant risks and control measures. This can support well-substantiated decision-making on strengthening biosecurity measures within a specific organization. With this article, we aim to support institutes to increase their overall security resilience and to improve institutional biosecurity in particular by providing practical recommendations. The Biosecurity Vulnerability Scan is available at www.biosecurityvulnerabilityscan.nl.
ABSTRACT
BACKGROUND: Mathematical models are used to explore various possible scenarios with regard to an influenza pandemic. We studied the ranges of parameter values in modelling studies on preparedness prior to 2009 in relation to the estimated parameter values of the influenza A(H1N1) 2009 pandemic. METHODS AND FINDINGS: We conducted two systematic literature searches, one aimed at epidemic parameter values that were used in pre-2009 pandemic influenza models, and the other aimed at estimates of epidemic variables from data collected during the influenza A(H1N1) 2009 pandemic. The range of parameter values used to inform models was broad and covered the range of estimates of these parameters inferred from the influenza A(H1N1) 2009 pandemic. CONCLUSION: The current practice of selecting a range of plausible parameter values for influenza works well for modelling scenarios where effects of different interventions are explored to guide public health decision makers. To narrow down this range of plausible parameter values to the actual value during a pandemic, using incoming data, real-time estimation might provide an additional benefit.
