A model-based tool to predict the propagation of infectious disease via airports.

Epidemics of novel or re-emerging infectious diseases have quickly spread globally via air travel, as highlighted by pandemic H1N1 influenza in 2009 (pH1N1). Federal, state, and local public health responders must be able to plan for and respond to these events at aviation points of entry. The emergence of a novel influenza virus and its spread to the United States were simulated for February 2009 from 55 international metropolitan areas using three basic reproduction numbers (R(0)): 1.53, 1.70, and 1.90. Empirical data from the pH1N1 virus were used to validate our SEIR model. Time to entry to the U.S. during the early stages of a prototypical novel communicable disease was predicted based on the aviation network patterns and the epidemiology of the disease. For example, approximately 96% of origins (R(0) of 1.53) propagated a disease into the U.S. in under 75 days, 90% of these origins propagated a disease in under 50 days. An R(0) of 1.53 reproduced the pH1NI observations. The ability to anticipate the rate and location of disease introduction into the U.S. provides greater opportunity to plan responses based on the scenario as it is unfolding. This simulation tool can aid public health officials to assess risk and leverage resources efficiently.

An analysis on the detection of biological contaminants aboard aircraft.

The spread of infectious disease via commercial airliner travel is a significant and realistic threat. To shed some light on the feasibility of detecting airborne pathogens, a sensor integration study has been conducted and computational investigations of contaminant transport in an aircraft cabin have been performed. Our study took into consideration sensor sensitivity as well as the time-to-answer, size, weight and the power of best available commercial off-the-shelf (COTS) devices. We conducted computational fluid dynamics simulations to investigate three types of scenarios: (1) nominal breathing (up to 20 breaths per minute) and coughing (20 times per hour); (2) nominal breathing and sneezing (4 times per hour); and (3) nominal breathing only. Each scenario was implemented with one or seven infectious passengers expelling air and sneezes or coughs at the stated frequencies. Scenario 2 was implemented with two additional cases in which one infectious passenger expelled 20 and 50 sneezes per hour, respectively. All computations were based on 90 minutes of sampling using specifications from a COTS aerosol collector and biosensor. Only biosensors that could provide an answer in under 20 minutes without any manual preparation steps were included. The principal finding was that the steady-state bacteria concentrations in aircraft would be high enough to be detected in the case where seven infectious passengers are exhaling under scenarios 1 and 2 and where one infectious passenger is actively exhaling in scenario 2. Breathing alone failed to generate sufficient bacterial particles for detection, and none of the scenarios generated sufficient viral particles for detection to be feasible. These results suggest that more sensitive sensors than the COTS devices currently available and/or sampling of individual passengers would be needed for the detection of bacteria and viruses in aircraft.