Aerosol influenza transmission risk contours: A study of humid tropics versus winter temperate zone

Table 1 Crude risk of contagion in vehicles or buildings

	25%_G7 transmission risk contour line		40%_G7 transmission risk contour line		60%_G7 transmission risk contour line
	Per passenger or patron	Per vehicle or building	Per passenger or patron	Per vehicle or building	Per passenger or patron	Per vehicle or building
Luxury bus 4 hr	1/81	0.6	1/51	1	1/34	2
Luxury bus 8 hr	1/41	1	1/25	2	1/17	3
Luxury bus 12 hr	1/27	2	1/17	3	1/11	4
Taxi 20 min	1/989	1/495	1/618	1/309	1/412	1/206
Taxi 45 min	1/435	1/218	1/272	1/136	1/181	1/91
Tour car 2 hr	1/163	1/82	1/102	1/51	1/68	1/34
Tour car 5 hr	1/65	1/33	1/41	1/20	1/27	1/14
Bank branch 10 min	1/1959	1/109	1/1224	1/68	1/816	1/45

Crude risk of contagion for individuals in vehicle or building locations assuming presence of virus shedding. Estimates are calculated from equation 2 expressed as whole number fractions for selected transmission probability contours. For example, the per passenger risk on a 4 hour luxury bus ride is 0.0123, or 1/81. Since buses are assumed to have 50 passengers, for conditions at the 25%_G7 risk contour line, 50/81 = 0.6. So for every two bus rides of 4 hours under these conditions, approximately 1 aerosol infection would be expected. Similarly, in 10 minutes in a bank branch at the 25%_G7 risk contour, assuming 18 people in the branch, there is a roughly 1 in 1959 chance of infection in any 10 minute period. Assuming 300 patrons per day, there would be 1 infection per branch during a one week infectious cycle under standard conditions.
For quick, rough estimates in a public health setting, estimates such as these can be made by use of the table. These kinds of estimates were used to decide which was the reasonable aerosol transmission risk contour to use to demarcate the low risk boundary of the figure 1 graph. (See Discussion, 25%_G7transmission risk contour selection.) It should be noted that these numbers are useful as guidance rather than being completely predictive since other factors such as ventilation diluting virus aerosol, rate of exhalation of aerosol virus, direction of airflow, and wake effects will have major effects on actual infection numbers.

ISSN: 1743-422X