Certain respiratory tract infections are transmitted through air. Coughing and sneezing by an infected person can emit pathogen-containing particles with diameters less than 10 microm that can reach the alveolar region. Based on our analysis of the sparse literature on respiratory aerosols, we estimated that emitted particles quickly decrease in diameter due to water loss to one-half the initial values, and that in one cough the volume in particles with initial diameters less than 20 microm is 60 x 10(-8) mL. The pathogen emission rate from a source case depends on the frequency of expiratory events, the respirable particle volume, and the pathogen concentration in respiratory fluid. Viable airborne pathogens are removed by exhaust ventilation, particle settling, die-off, and air disinfection methods; each removal mechanism can be assigned a first-order rate constant. The pathogen concentration in well-mixed room air depends on the emission rate, the size distribution of respirable particles carrying pathogens, and the removal rate constants. The particle settling rate and the alveolar deposition fraction depend on particle size. Given these inputs plus a susceptible person's breathing rate and exposure duration to room air, an expected alveolar dosemicrois estimated. If the infectious dose is one organism, as appears to be true for tuberculosis, infection risk is estimated by the expression: R = 1-exp(-micro). Using published tuberculosis data concerning cough frequency, bacilli concentration in respiratory fluid, and die-off rate, we illustrate the model via a plausible scenario for a person visiting the room of a pulmonary tuberculosis case. We suggest that patients termed "superspreaders" or "dangerous disseminators" are those infrequently encountered persons with high values of cough and/or sneeze frequency, elevated pathogen concentration in respiratory fluid, and/or increased respirable aerosol volume per expiratory event such that their pathogen emission rate is much higher than average.

A substantial portion of human respiratory tract infection is thought to be transmitted via contaminated hand contact with the mouth, eyes, and/or nostrils. Thus, a key risk factor for infection transmission should be the rate of hand contact with these areas termed target facial membranes. A study was conducted in which 10 subjects were each videotaped for 3 hr while performing office-type work in isolation from other persons. The number of contacts to the eyes, nostrils, and lips was scored during subsequent viewing of the tapes. The total contacts per subject had sample mean x = 47 and sample standard deviation s = 34. The average total contact rate per hour was 15.7. The authors developed a relatively simple algebraic model for estimating the dose of pathogens transferred to target facial membranes during a defined exposure period. The model considers the rate of pathogen transfer to the hands via contact with contaminated environmental surfaces, and the rate of pathogen loss from the hands due to pathogen die-off and transfer from the hands to environmental surfaces and to target facial membranes during touching. The estimation of infection risk due to this dose also is discussed. A hypothetical but plausible example involving influenza A virus transmission is presented to illustrate the model.

The relative contribution of four influenza virus exposure pathways-(1) virus-contaminated hand contact with facial membranes, (2) inhalation of respirable cough particles, (3) inhalation of inspirable cough particles, and (4) spray of cough droplets onto facial membranes-must be quantified to determine the potential efficacy of nonpharmaceutical interventions of transmission. We used a mathematical model to estimate the relative contributions of the four pathways to infection risk in the context of a person attending a bed-ridden family member ill with influenza. Considering the uncertainties in the sparse human subject influenza dose-response data, we assumed alternative ratios of 3,200:1 and 1:1 for the infectivity of inhaled respirable virus to intranasally instilled virus. For the 3,200:1 ratio, pathways (1), (2), and (4) contribute substantially to influenza risk: at a virus saliva concentration of 10(6) mL(-1), pathways (1), (2), (3), and (4) contribute, respectively, 31%, 17%, 0.52%, and 52% of the infection risk. With increasing virus concentrations, pathway (2) increases in importance, while pathway (4) decreases in importance. In contrast, for the 1:1 infectivity ratio, pathway (1) is the most important overall: at a virus saliva concentration of 10(6) mL(-1), pathways (1), (2), (3), and (4) contribute, respectively, 93%, 0.037%, 3.3%, and 3.7% of the infection risk. With increasing virus concentrations, pathway (3) increases in importance, while pathway (4) decreases in importance. Given the sparse knowledge concerning influenza dose and infectivity via different exposure pathways, nonpharmaceutical interventions for influenza should simultaneously address potential exposure via hand contact to the face, inhalation, and droplet spray.

Certain respiratory tract infections can be transmitted by hand-to-mucous-membrane contact, inhalation, and/or direct respiratory droplet spray. In a room occupied by a patient with such a transmissible infection, pathogens present on textile and nontextile surfaces, and pathogens present in the air, provide sources of exposure for an attending health-care worker (HCW); in addition, close contact with the patient when the latter coughs allows for droplet spray exposure. We present an integrated model of pertinent source-environment-receptor pathways, and represent physical elements in these pathways as "states" in a discrete-time Markov chain model. We estimate the rates of transfer at various steps in the pathways, and their relationship to the probability that a pathogen in one state has moved to another state by the end of a specified time interval. Given initial pathogen loads on textile and nontextile surfaces and in room air, we use the model to estimate the expected pathogen dose to a HCW's mucous membranes and respiratory tract. In turn, using a nonthreshold infectious dose model, we relate the expected dose to infection risk. The system is illustrated with a hypothetical but plausible scenario involving a viral pathogen emitted via coughing. We also use the model to show that a biocidal finish on textile surfaces has the potential to substantially reduce infection risk via the hand-to-mucous-membrane exposure pathway.

The well-mixed room model is traditionally used to predict the concentration of contaminants in indoor environments. To account for imperfect air mixing, the room supply/exhaust air rate Q is frequently multiplied by a mixing factor m, where 0 < m < or = 1, and an effective ventilation rate QE = m . Q is used in place of Q in the well-mixed room equations. However, this procedure is inappropriate because a well-mixed room model, albeit with an adjusted ventilation rate, is still used to describe an imperfectly mixed room. To illustrate the errors that may result, a two-zone model is described in which a room is conceptually divided into an upper zone and lower zone, where the latter is the zone of occupancy. Air is supplied to and exhausted from the upper zone at rate Q, and air exchanges between the two zones at rate beta. The lower zone's true ventilation rate is termed its purging flow rate QL, where QL = [beta/(beta + Q)]Q. Expressions for the steady-state contaminant levels in the two zones and for decay from the steady-state levels are presented. In a two-zone room, if one ignores imperfect air mixing and attempts to estimate QE from a decay curve, QE will usually be greater than QL. Given that contaminant is emitted in the lower zone, subsequent use of QE rather than QL to predict steady-state exposure intensity in the room will cause an underestimation error. For a room with an upper- to lower-zone volume ratio of 2:3, the underestimation error can reach 40%. If a room has a single or dominant point source of contaminant, it is recommended that the purging flow rate near the release point be determined, which permits a more accurate prediction of a worker's exposure intensity near the source. Alternative methods for determining the local purging flow rate are described. It is also shown that age-of-air analysis techniques do not provide information directly relevant to estimating exposure intensity.

Diverse control measures can be applied to reduce tuberculosis infection risk in health‐care facilities. Selecting optimal controls requires methods for predicting the dependence of infections risk on underlying parameters. A common model for infection risk only explicitly accounts for control by ventilation. This paper proposes a more complete model for evaluating tuberculosis infection control methods in health‐care settings. An infection risk parameter is defined as the probable number of infectious droplet nuclei inhaled by all susceptible persons from a single infectious person. Algebraic model equations are presented for two exposure cases. In one, the susceptible and infectious persons are together in a well‐mixed indoor environment; in the socond, the infectious person is in respiratory isolation. Model equations are used to explore many common tuberculosis control measures: identification, isolation and treatment of tuberculosis cases; surgical masks and treatment booths applied at the source; environmental controls such as ventilation, air filtration, and ultraviolet germicidal irradiation; and respiratory protection for susceptible persons. Experimental data are limited or lacking on some key variables, such as emissions of infectious droplet nuclei by contagious persons and air leakage rates from isolation rooms. Methods are outlined for collecting additional data.

Engineering controls can be used to reduce the spread of airborne infectious disease, particularly tuberculosis (TB), in high-risk settings. This article evaluates published data on the efficacy of upper-room air ultraviolet germicidal irradiation (UVGI). A three-zone representation of a TB patient room equipped with a germicidal UV lamp is developed. The lamp irradiates the upper-room zone and inactivates airborne mycobacteria; the unirradiated lower-room zone also contains a near-field zone surrounding the TB patient. Infectious particles are generated in the near-field zone and transported throughout the room by air flow between zones. Each zone is independently well-mixed; the whole room, however, is not well-mixed. The three-zone model is applied to a previously published study of UVGI against airborne mycobacteria in a test room. Based on the estimated slopes of the semi-log concentration decay curves for viable mycobacteria, and on the assumption that the test room was essentially well-mixed, the published study reported that UVGI provided 10 to 25 equivalent air changes per hour. However, when the same decay curve slopes are interpreted in the context of the three-zone model, UVGI is seen to be far less effective in reducing exposure intensity near the TB patient. Near-field exposure intensity is relevant because health care workers are usually in close proximity to the TB patients they attend. In general, the interpretation of concentration decay data depends on the specific model of room air mixing that is assumed appropriate. It is recommended that tests of the efficacy of UVGI and other control devices against airborne microorganisms be based on steady-state concentration measurements rather than concentration decay measurements, because the former measurements do not require inferences based on a particular model.

Influenza can be transmitted through respirable (small airborne particles), inspirable (intermediate size), direct-droplet-spray, and contact modes. How these modes are affected by features of the virus strain (infectivity, survivability, transferability, or shedding profiles), host population (behavior, susceptibility, or shedding profiles), and environment (host density, surface area to volume ratios, or host movement patterns) have only recently come under investigation. A discrete-event, continuous-time, stochastic transmission model was constructed to analyze the environmental processes through which a virus passes from one person to another via different transmission modes, and explore which factors increase or decrease different modes of transmission. With the exception of the inspiratory route, each route on its own can cause high transmission in isolation of other modes. Mode-specific transmission was highly sensitive to parameter values. For example, droplet and respirable transmission usually required high host density, while the contact route had no such requirement. Depending on the specific context, one or more modes may be sufficient to cause high transmission, while in other contexts no transmission may result. Because of this, when making intervention decisions that involve blocking environmental pathways, generic recommendations applied indiscriminately may be ineffective; instead intervention choice should be contextualized, depending on the specific features of people, virus strain, or venue in question.