key: cord-261962-sfa9d1ux authors: Lei, H.; Li, Y.; Xiao, S.; Lin, C.‐H.; Norris, S. L.; Wei, D.; Hu, Z.; Ji, S. title: Routes of transmission of influenza A H1N1, SARS CoV, and norovirus in air cabin: Comparative analyses date: 2018-01-06 journal: Indoor Air DOI: 10.1111/ina.12445 sha: doc_id: 261962 cord_uid: sfa9d1ux Identifying the exact transmission route(s) of infectious diseases in indoor environments is a crucial step in developing effective intervention strategies. In this study, we proposed a comparative analysis approach and built a model to simulate outbreaks of 3 different in‐flight infections in a similar cabin environment, that is, influenza A H1N1, severe acute respiratory syndrome (SARS) coronavirus (CoV), and norovirus. The simulation results seemed to suggest that the close contact route was probably the most significant route (contributes 70%, 95% confidence interval [CI]: 67%‐72%) in the in‐flight transmission of influenza A H1N1 transmission; as a result, passengers within 2 rows of the index case had a significantly higher infection risk than others in the outbreak (relative risk [RR]: 13.4, 95% CI: 1.5‐121.2, P = .019). For SARS CoV, the airborne, close contact, and fomite routes contributed 21% (95% CI: 19%‐23%), 29% (95% CI: 27%‐31%), and 50% (95% CI: 48%‐53%), respectively. For norovirus, the simulation results suggested that the fomite route played the dominant role (contributes 85%, 95% CI: 83%‐87%) in most cases; as a result, passengers in aisle seats had a significantly higher infection risk than others (RR: 9.5, 95% CI: 1.2‐77.4, P = .022). This work highlighted a method for using observed outbreak data to analyze the roles of different infection transmission routes. Knowledge about the relative importance of different transmission route(s) is fundamental to developing effective intervention strategies for infectious respiratory and enteric diseases in indoor environments. Epidemiological analysis together with in-depth environmental investigations often provides useful insights, 1 and meta-analysis may also be carried out for a particular disease. In this study, we proposed an alternative comparative analysis approach in which we studied outbreaks of different diseases in the same environment using the same approach, by examining differences in the spatial infection patterns. This approach could partly overcome the limitation of traditional individual outbreak analysis that outbreak cannot be repeatedly observed, because the comparative analysis of different diseases in the same environment is like that one disease happened several times. Our hypothesis is that the different transmission routes of infection lead to different spatial patterns of secondary cases. For example, close contact transmission always happens with 1-2 m of the source, which means that secondary cases infected via close contact route would be close to the index case(s). The airborne transmission may occur over long distance, and the secondary cases infected via airborne route would distribute uniformly in a space when the air is fully mixed. Aircraft cabins were selected as the context for our study. The more or less fixed seating arrangement in aircraft cabins permits a spatial pattern of the secondary cases to be identified in some outbreaks. The Norovirus is the leading cause of nonbacterial gastroenteritis in humans. 5, 6 The 3 major possible routes in aircraft cabins are close contact, airborne, and fomite. 7 In this study, a mathematical model was built to study the in-flight infection transmission process, based on the studies by Atkinson and Wein 8 and Nicas and Jones. 9 This technique enables detailed physical and biological processes to be modeled and the impact of environmental parameters to be easily integrated. We compared the simulated relative importance of different transmission routes in 3 in-flight outbreaks with the reported spatial distribution of the secondary cases. We performed a literature search for in-flight outbreaks of influenza A H1N1, SARS CoV, and norovirus in Appendix S1. All 3 chosen outbreaks occurred in Boeing 737 aircraft cabins with flight duration 2.5 or 3 hours. The main criteria for identifying suitable outbreaks include the availability of detailed seating information for both the infected and noninfected, airplane type and flight duration. Figure 1 illustrates the detailed spatial distribution of the secondary cases in the chosen outbreaks. The definitions for the relevant transmission routes ( Figure 2 ) in our multiroute model are as follows. The airborne route refers to direct inhalation of an infectious agent through small droplet nuclei, that is, the residue of large droplets containing microorganisms that have evaporated to an aerodynamic diameter of less than 10 microns (termed respirable). 9 These respirable droplets can deposit in the respiratory tract. Close contact route includes direct contact and large droplet transmission. Direct contact refers to infection through person-to-person contact with the index source passenger, such as handshaking. We assume there is no body-to-body contact between index source passenger(s) and other passengers during the flight. We only consider large droplet transmission in the model, which refers to the inhalation of the virus carried in respirable airborne particles with a diameter between 10 and 100 microns (termed inspirable), 9 and the droplet spray of large droplets (>100 microns in diameter) onto facial target membranes. The fomite route refers to infection by touching objects or surfaces that have earlier been contaminated by hands or by direct deposition • Our identification of the dominated routes, that is the close contact route (large droplet) for influenza, the fomite route for norovirus, and all 3 routes for SARS CoV, suggested the relative importance of different environment intervention for different infectious diseases in air cabins and probably also in other indoor environments. For minimizing in-flight fomite transmission, the aisle seatbacks and toilets should be cleaned and disinfected effectively. F I G U R E 1 Spatial distribution for 3 in-flight infection outbreaks, (A) norovirus, 26 (B) SARS CoV, 27 and (C) influenza A H1N1 28 of infectious pathogens from the index source passenger, which is also sometimes termed indirect contact route. For respiratory disease, coughing is used as surrogate to model the virus-containing droplets from all respiratory activities such as breathing, talking, and sneezing, as the size distribution of the droplets from coughing, talking, and sneezing is similar, and the amount of droplets generated due to breathing is negligible. Assume that cough frequency for infector is f c per hour and that one cough can produce N c droplets with the size distribution F c r 0 . Then, the generation rate (number/h) of droplets with radius r 0 (μm) from individual i is given by: For enteric disease, such as norovirus, virus-containing droplets are emitted from the infector in vomit and/or diarrhea. A study by simulated vomiting device showed that the volume of the aerosolized droplets ranged from 0.004 to 21 mL, with a mean value of 0.4 mL. 10 To the best of our knowledge, there is no study on the size distribution of the droplets from vomit, and we assume that these droplets have same size distribution as those from coughing. For respirable droplets with aerodynamic diameter of less than 10 microns, they could move a long distance with the airflow and dis- For inspirable droplets with a diameter between 10 and 100 microns, we assumed that the maximum horizontal distance they could move was 2 m because of gravity and the relatively high deposition rate on environmental surfaces. They distributed within 2 m of the source, and the volume of this space was denoted to be A rapid death rate of pathogens atomized into air had been observed, 12, 13 and evaporation of droplets was believed to play an important role. 14 Xie et al 15 found that there was a fast viability decline stage when the droplets completely evaporated, when viability decreased to about 25% and then slowly declined. Here, the survival ratio due to evaporation was defined as e r 0 ,s = L r 0 ,t ∕L r 0 ,0 where L r 0 ,t is the concentration of viable viruses (TCID 50 /mL or genome copies/mL) in droplets with initial radius r 0 (μm) at the time t (s) after being exhaled; T e r 0 is the evaporation time (s) for the drop- Illustration of different transmission routes considered in this study. Note that all sizes of droplets are involved in the fomite route Assume there is no resuspension of droplets from environmental surfaces into the air. In the air cabin, on the one hand, viable virus is generated from index case(s) at rate (7) was The exposure through each route was modeled separately, and then the dose-response model was used to assess an integrated risk. The dose to individual i via the airborne route in the lower and upper respiratory tracts is denoted as D i al and D i au (TCID 50 or genome copies), and for a flight duration T, they can estimated as follows: where r a is the largest radius for airborne droplets and r a = 5 μm 9 ; p is the pulmonary ventilation rate and p = .48 m 3 /h 21 ; r 0 is the droplets' initial radius; and r is the final radius after complete evaporation. Here, we assume that r = r 0 /3 10 ; l (r) and u (r) are the deposition fraction of droplets with radius r in the lower and upper respiratory tracts, respectively. The model from ICRP was used in this study. 22 Transmission by close contact refers to either inhalation of the virus carried in airborne particles with a diameter between 10 and 100 microns, or the spray of large droplets on the susceptible individuals' mucous membranes. For norovirus transmission, it is difficult for the "large" droplets generated from vomiting to move to the inhale air of the seated susceptible passengers, which is always more than one meter above the ground, because of the high downward velocity, gravity, and the relatively high deposition rate. Therefore, the close contact route was not considered in the norovirus transmission. Then the dose via inhalation of inspirable droplets in upper respiratory tract (D i cr (TCID 50 or genome copies)) was where r b was radius of the maximum inspirable droplets and For the spray of large droplets on mucous membranes, because of the seating arrangement we assumed that there was no face-to- The negative exponential dose-response model was used to estimate the infection risk, which implies that a single particle can start an infection, all single particles are independent of each other. The infection risk of individual i during the flight can be calculated according to the following equation where η l η l , η u , and η m are the dose-response rates (/TCID 50 or/genome copy) in lower/upper respiratory tracts and on mucous membranes, respectively. Here it is also assumed that η u = η m . Table 2 (11) D i cm = T ∫ o ∞ ∫ r a A m A V 2 C i c (r) kr 2 V 2 t 4 3 πr 3 0 L r 0 ,t drdt (12) C j s � k + 1 � = � C j s (k) + ∑N p i=1 (C i h (k)A hs τ hs −C j s (k)A hs τ sh )ps i,j (k) A s � e −b s ×ΔT (13) C i h � k + 1 � = � C i h (k) − ∑N p i=1 (C i h (k)A hs τ hs −C j s (k)A hs τ sh )ps i,j (k) A h − pm i (k)A m h τ hm C i h (k) A h � e −b h ×ΔT (14) D i fm = N t ∑ k=1 Pm i (k) A m h τ hm C i h (k) (15) P i = 1 − e −(η l D i al +η u D i au )−(ηuD i cr +η m D i cm )−ηmD i fm In this study, we built a mathematical model to study the inflight transmission of different viruses, using a novel comparative analysis approach. The results suggested that the dominant transmission routes in air cabins are probably the close contact route for influenza, the fomite route for norovirus, and all 3 routes (airborne, close contact, and fomite routes) for SARS CoV. The dominant transmission routes vary for the 3 viruses, mainly depending on the pathogen-specific T A B L E 2 Infection risks of passengers within 2 rows of the index case(s) and others from the simulation results and reported outbreak data, respectively (with the tuned range of the virus shedding magnitudes) Outbreak data within 2 rows (others) As far as we are aware there are no data on the dose-response rate of SARS CoV either on mucous membranes or in the respiratory tracts of humans. We assume that the dose-response rate on human mucous membranes is the same as that on mice mucous membranes, and the dose-response rate in the human respiratory tract is 1000 times that of mucous membranes (similar to the influenza A H1N1 data). We also performed the sensitivity analysis of the dose-response rates, with different ratios of median dose-response rate, that is (in lower respiratory tract)/(on mucous membranes) (see detail in Appendix S5), and we found that all 3 routes were important in in-flight SARS CoV transmission for different ratios of median dose-response rate. The fecal-oral spread is known to be the primary mode of transmission of norovirus. 5 The fomite route transmission of norovirus is well supported by the reported widespread environmental contamination with norovirus. 36, 37 Our simulation of a norovirus outbreak confirms that the fomite route is dominant in transmission. It is also suggested that vomiting can produce aerosol droplets containing norovirus particles, and inhaled by exposed susceptible individuals, depositing in the upper respiratory tract and subsequently swallowed along with the respiratory mucus. 38 Airborne norovirus was detected from an air sample in one outbreak. 39 A study of a norovirus outbreak in a hotel found an inverse relationship between the infection risk and the distance from the person who vomited when a food source was not implicated. 40 This study simulated both the airborne and fomite route transmission of norovirus. And the results showed that in most cases, the fomite route plays the dominant role. The predicted infection risk from the fomite route for aisle seat passengers is 2.2 times higher than that for nonaisle seat passengers. The aisle passenger-tonon-aisle passenger relative risk in the outbreak (9.5) is much higher than 2.2, and may be attributable to a small sample size of secondary cases (6). In conclusion, a mathematical model was built to simulate the physi- Additionally, this study highlights a way to use observation outbreak data to analyze the relative importance of different routes in infection transmission. 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