title: Probability of real-time detection versus probability of infection for aerosolized biowarfare agents: a model study.
abstract: Real-time biosensors are expected to provide significant help in emergency response management should a terrorist attack with the use of biowarfare, BW, agents occur. In spite of recent and spectacular progress in the field of biosensors, several core questions still remain unaddressed. For instance, how sensitive should be a sensor? To what levels of infection would the different sensitivity limits correspond? How the probabilities of identification correspond to the probabilities of infection by an agent? In this paper, an attempt was made to address these questions. A simple probability model was generated for the calculation of risks of infection of humans exposed to different doses of infectious agents and of the probability of their simultaneous real-time detection/identification by a model biosensor and its network. A model biosensor was defined as a single device that included an aerosol sampler and a device for identification by any known (or conceived) method. A network of biosensors was defined as a set of several single biosensors that operated in a similar way and dealt with the same amount of an agent. Neither the particular deployment of sensors within the network, nor the spacious and timely distribution of agent aerosols due to wind, ventilation, humidity, temperature, etc., was considered by the model. Three model biosensors based on PCR-, antibody/antigen-, and MS-technique were used for simulation. A wide range of their metric parameters encompassing those of commercially available and laboratory biosensors, and those of future, theoretically conceivable devices was used for several hundred simulations. Based on the analysis of the obtained results, it is concluded that small concentrations of aerosolized agents that are still able to provide significant risks of infection especially for highly infectious agents (e.g. for small pox those risk are 1, 8, and 37 infected out of 1000 exposed, depending on the viability of the virus preparation) will remain undetected by the present, most advanced, or even future, significantly refined real-time biosensors.
authors.affiliation: Center for Security Studies and Research, East Carolina University, Greenville, NC 27858, USA. sabelnikova@mail.ecu.edu
authors.forename: Alexander
authors.initials: A
authors.lastname: Sabelnikov
authors.forename: Vladimir
authors.initials: V
authors.lastname: Zhukov
authors.forename: Ruth
authors.initials: R
authors.lastname: Kempf
mesh_terms: D000336:Aerosols; D001694:Biological Warfare; D015374:Biosensing Techniques; D003140:Communicable Disease Control; D003141:Communicable Diseases / Q000175:diagnosis / Q000382:microbiology*; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D011336:Probability
chemical_list: D000336:Aerosols