Author: Lei, H.; Li, Y.; Xiao, S.; Lin, C.â€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 Cord-id: sfa9d1ux Document date: 2018_1_6
ID: sfa9d1ux
Snippet: 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 inâ€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 wa
Document: 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 inâ€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%â€72%) in the inâ€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: 1.5â€121.2, P = .019). For SARS CoV, the airborne, close contact, and fomite routes contributed 21% (95% CI: 19%â€23%), 29% (95% CI: 27%â€31%), and 50% (95% CI: 48%â€53%), respectively. For norovirus, the simulation results suggested that the fomite route played the dominant role (contributes 85%, 95% CI: 83%â€87%) in most cases; as a result, passengers in aisle seats had a significantly higher infection risk than others (RR: 9.5, 95% CI: 1.2â€77.4, P = .022). This work highlighted a method for using observed outbreak data to analyze the roles of different infection transmission routes.
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