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

A model EP8706 digital psychrometer (Mannix Testing & Measurement, Lynbrook, New York) was used to take readings of temperature and humidity for most of the data. For a subset of the Singapore data, a Holmes analog humidity meter was used with a conventional analog dry bulb thermometer. These instruments were calibrated against each other, and the digital psychrometer was assumed to be the more accurate instrument. Correction was applied to the Holmes humidity data based on cross-calibration. Each reading is a one-time measurement; instruments came to equilibrium before a reading was recorded by hand with the time, date and location. Care was taken to ensure instrument temperature was at ambient, shielded from strong air currents and major infrared radiant sources to prevent condensation or improper evaporation from the probe.

During the months of July through September 2009, major cities in the countries of Costa Rica, El Salvador, Nicaragua, Panama, Peru, Thailand, Singapore and New Guinea were surveyed as representative of the humid tropical belt. Australia was chosen as the location of cities experiencing temperate winter conditions and a significant spike in H1N1 influenza. The cities (which included nearby suburban areas) were: San Jose, Costa Rica; Lima, Peru; Panama City, Panama; San Salvador, El Salvador; Managua, Nicaragua; Bangkok, Thailand; Singapore; Port Moresby, Papua New Guinea; Sydney and Melbourne, Australia. Some surveillance of outlying areas was also performed.

Travel locations typical of choices made by tourists and business travelers was performed, as well as a survey of shops, offices, malls, high end hotels, and dining establishments in each city. These locations were chosen because in the modern world, epidemics are spread rapidly by aircraft[1] and thus by extension, the places visited by those who travel on aircraft (tourists and businesspeople) are the logical contact points with the population of those nations.

Environment in buses, tour cars, and taxicabs was also monitored, and, where feasible, time courses were conducted on transportation. These data could be improved on by a more comprehensive data collection conducted in more countries over the course of several years at a larger number of locations. However, the data is sufficient to be useful, and care was taken to make choices as consistent with tourist and business behavior typical of air travelers as possible and to only observe, never direct or interfere with environmental controls or human behavior.

After review, aerosol transmission estimations were primarily drawn from Lowen et al. to interpolate contours of transmission of influenza in the humidity versus temperature phase space. A set of contours was generated that is believed to be mildly conservative (Figure 1 and Additional file 1 - Contagion contour estimation details). The emphasized 25% transmission line corresponds to a rough 25% transmission probability in the Lowen et al. guinea pig studies, which were done with continuous exposure for 7 days with animals housed near each other on a shelf (25%_G7). This line was used as the cutoff when counting locations inside and outside of optimum aerosol transmission conditions. Similarly, other contours can be referred to with the G7 subscript to clarify what is being discussed, (e.g. 10%_G7, 50%_G7, 100%_G7, etc.)

Equation 1 is a polynomial equation fitted for the 25%_G7 contour using Maple 10[20] (Maplesoft, Waterloo, Ontario, Canada) and entered into Excel 2003 (Microsoft, Redmond, Washington). This equation was used to create the distance values for other figures. Due to the length and technicality, equation 1 is only shown in the additional files. (Additional file 1- Contagion contour estimation details , Equation 1: 25% _G7 risk contour temperature.) A sample equation formatted for spreadsheet use (Additional file 2 - Empirical 25% line equation in text format for use in Excel) is supplied in text form along with the Maple 10 file used to generate it (Additional file 3 - Maple workbook for 25% line equation). Note the equation fit is meaningless below 20% RH or above 80% RH.

We recognize that accurate influenza morbidity and mortality data are notoriously difficult to acquire[21, 22]. Mortality and morbidity rates based on current surveillance data were collected both from official reports (WHO PAHO/SEARO, Departments of Public Health) and directly from responsible public health personnel in interviews. Care was taken to try to minimize artifact differences due to surveillance deficits. Alternate sources such as Flutracker[23] were consulted to vet surveillance data and we believe our data is as accurate as obtainable for the period.

Crude risk derivation is based on a histogram of the estimated time of contagion for the animals in Lowen et al., as presented in Figure 2. Calculated crude risk estimates are given in tables where time in location estimation allows it.

A polynomial equation (equation 2, Additional file 1- Contagion contour estimation details) was fitted to the histogram data using Maple 10[20] to estimate the risk of start of infection over the days in which secondary infections are estimated to have occurred. This 4^th degree equation from day 1 to 9 was selected to fit observations and knowledge that virus shedding from inoculated animals is expected to end by day 8. To express probability of infection, the histogram values were represented as fractions of the total number of infections, and the equation fitted so the area under the curve is equal to 1 to 3 decimal places. Tables 1 and 2 contain estimates generated from this equation. (See also: Additional file 4 - Maple workbook for empirical contagion probability equation, and Additional file 5 - Empirical contagion probability integral in text format for use in Excel.)

Equation 2: Contagion probability estimate by time expressed in days

Where t = time expressed in days, p is crude risk probability assuming 100% 7 day contagion (100% _G7 ) C(T, RH) is the estimated risk for a field reading from the contour map and p _c = probability of contagion. Result p when multiplied by contagion contour percentage at a temperature and RH provides the time scaled risk of infection in a location ( p _c ). See Additional file 1- Contagion contour estimation details for more discussion.

Luxury buses are used for long-distance trips of 4-12 hours, have air conditioning (AC) and windows operable only in emergency. A not uncommon complaint is that these buses are too cold. They are commonly used by traveling visitors, and also by middle class inhabitants of the region desiring more comfort in their travel. Luxury bus trips (N = 16) fell into the region of concern below the 25%_G7 transmission risk contour (Figure 6), and passengers shedding virus are a probable source of aerosol. Additionally, vendors often appear at stops selling merchandise. Some vendors get on at one stop and travel with the bus for 45 minutes or so, selling products and entertaining the passengers. Others get on the bus at stops and spend shorter periods of time on the order of 10 minutes, getting off one stop later. Some get on and off at the same stop in 2-4 minutes. Total estimated vendor time inside buses is on the order of 3-5 hours per day over as many as 20 buses, each bus containing potential influenza aerosol, raising their chances of both infection and transmission. Wake effects such as those proposed to explain SARS transmission in a modern jet aircraft[24] are generated by them, and they lean in close to many passengers. Additionally, they have physical contact through money and product exchange, which is usually food.

Inexpensive buses used by locals have no AC, using open windows for ventilation. Readings confirmed that their temperatures and RH are equal to or higher than ambient temperatures (data not shown). Vendors serving these inexpensive buses were fewer, and were only observed to board briefly at stops, departing the bus within 2-3 minutes, or to make sales directly through windows.

Trips in high end taxicabs and new minivan shuttles (N = 21) also showed good transmission conditions (Figure 6). Trips in old taxis (N = 11), even if closed and air-conditioned, and those not using recirculation, stayed out of the high risk region. The ability of a new automobile to rapidly lower humidity with the windows closed on recirculation setting to between 45% and 25% RH within 5 minutes or less is remarkable (Figure 7). New high end taxis and hotel cars were commonly occupied by travelers for 20-45 minutes (to/from airports, to/from holiday or business meetings).

Of high end tropical hotels studied (N = 22) approximately 50% had good conditions for aerosol transmission in common areas. However, all tropical hotels having good conditions were in the high RH region near 65% (data not shown).

Tropical locations to find good conditions for aerosol influenza transmission to the general public were bank branches, dining facilities, retail shops and offices (Figure 8). The overall impression was that business locations in the tropics needing to appear high-status set their AC systems to generate low humidity and temperature.

In the temperate Australian winter, 98% of buildings (N = 100) showed good aerosol transmission conditions (Figure 3B). Thus, no breakdown by type of facility is presented.

Dwellings were not surveyed in all nations and the sample was low; however, where surveyed, estimates were made of how typical RH and temperature were for a dwelling class. The majority of tropical apartment buildings (N = 10, data not shown) had open-air common areas and showed poor aerosol conditions. Surprisingly, temperature and RH conditions in dwellings surveyed were not optimum for transmission in Australia, although the sample was insufficiently large.

Using our transmission contours, conditions for influenza transmission exist during deplaning (Figure 9) for intervals of 7 minutes or less from the time passengers stood up until aircraft cleared (mean 3 min 55 sec, N = 12). During this time, ventilation was often turned to a low setting or off. The authors believe risk is probably low on a per passenger basis, because the period is limited to the short time in which passengers file out of the airplane and other factors may override T and RH. Aircraft data (Figure 10) is otherwise presented without risk interpretation (see discussion).

Literature shows opposing conditions for transmission of viruses in general; low relative humidity (RH) and high RH[5, 25, 26] with temperature a secondary factor. Theory predicts osmotic forces should affect enveloped viruses such as influenza, while icosahedral viruses (e.g. polio, norovirus) would not be so sensitive for structural reasons. Enveloped viruses generally have highest infective stability at RH somewhat below 50%[5], and non-enveloped icosahedral viruses usually show greatest infective stability in aerosol in high humidity conditions[25, 26].

Data of Lowen et al.[16, 17] at 20°C show optimal transmission of influenza by aerosol at a first RH range from 20-40%, and at a second from 60-70%. Lower temperatures improve transmission, with temperatures above 30°C reducing transmission to zero. These data correlate with other in-vitro studies[5, 25‐27].

Influenza is an enveloped virus. Enveloped viruses bud from the cell membrane, so the virus envelope is host cell (or golgi) membrane acquired in the budding process. Inside the envelope is RNA, a few enzymes and proteins, along with cell cytosol at physiological salt concentration. This matters because if one puts a cell in an environment containing lower salt concentration than in cytosol, the cell membrane acts as an osmosis membrane and eventually ruptures[28]. Enveloped viruses will have the same issue, although the smaller diameter should give greater stability to rupture per equation 3.

Equation 3:

Where F = membrane tensile force, P = pressure, r = radius

Infectious droplets from the lungs start out with physiological levels of salts. These salts could cause rupture of virion envelopes as droplets collect distilled water from humid air. Schaffer et al[27] studied stability of enveloped viruses from different cell lines (viz. kidney, chick embryo) and these cell lines buffer osmotic pressures at different rates[28]. Those results indicate that cells which are better at buffering themselves to osmotic pressure produce enveloped viruses that survive longer at higher RH. We are not aware of any direct study of osmotic destruction of enveloped virions, although it makes considerable sense.

At lower RH, enveloped viruses are quite stable and infectious; at high RH they are not. One possibility is the above-mentioned osmotic pressure issue. Another is the theory that droplet particles settle more quickly as they take on water[17] under high RH, which fits Stokes' law[2]. In addition, enlargement of the particle as a condensation nucleus will cause it not to penetrate as far into lungs as a result[2]. However, none of these hypotheses explain the viability trough at 50% RH nor the secondary peak at 65% RH, although the rapid decline toward 80% RH does fit. The primary variable between in-vitro studies and Lowen et al. appears to be differences between the synthetic droplet media of the in-vitro studies[25, 27] and the natural droplets from exhalation, which are likely to be glycoproteins, salts and other components of mucus[29].

A study by Harper[25] examined, in-vitro, survival of 4 cultured viruses in aerosol, at temperatures ranging from 21°C to 24°C. To the degree his results differ from Lowen in the 50% + humidity range, they might be explained by his higher temperature. If so, that would change the contours of influenza transmission risk (Figure 1) somewhat, although the current transmission risk contours would remain conservative. Alternatively, this difference may be from the droplet fluid carrying virus used by Harper, as mentioned above.

There is an argument that influenza strains might vary in stability from mutations sufficiently to affect the contours of transmission as taken from Lowen. However, evolutionary argument supports virus stability in aerosol as strongly conserved, since in humans, viruses with lesser aerosol stability will not propagate as well as those with greater stability (unless aerosol stability is compensated for by some other propagation enhancement), and viruses with optimum stability will be selected for during host to host transmission[30, 31]. Thus, literature results from human influenza virus strains would be expected to be from virus near the practical limit of aerosol stability. Further, osmotic pressure generates tensile force on the envelope, which will exhibit resistance to osmotic pressure not exceeding the weakest envelope bilayer hydrogen bonds.

Based on the considerations above, contours were generated based on linear interpolation of Lowen et al.[16, 17] cross-validated with others[5, 25, 27, 32]. These contours apply to RH conditions from 20% to 80%, although it is likely that contours above 80% RH have lower transmission risk than at 80%. Both the region from 0% to 20% RH and that above 80% RH are less clear and need investigation. The justification for using these risk contours in larger scale environments is based on data from studies that show long term persistence (hours) of viable aerosol virus[25, 27].

As presented by Lowen et al.[16, 17] in studies of aerosol transmission of influenza over 7 days, there are three temperature groups, 5°C, 20°C and 30°C at varying RH. For the 5°C temperature there are four RH categories, 35%, 50%, 65% and 80%. At 20°C and 30°C there is an additional fifth at 20% RH. At 30°C there is no transmission. At 5°C transmission varies from 100% to 50% and at 20°C from 100% to 0%. Thus, where statistical power is in question is between 5°C and 20°C. As discussed[17], the difference in transmissibility between 5°C and 20°C at 50% and 80% humidity is significant (p < 0.05). This leaves the 65% relative humidity results at 20°C to be examined.

To further evaluate the Lowen data, we considered it in the context of Harper[25] and Schaffer[27] data on time course viability of influenza virions at differing temperature and humidity, because it is axiomatic that the longer virions can remain viable in aerosol, the more likely they are to cause infection by this route. Harper shows support for the transmission decline of Lowen, as viability declines when RH increases toward 50%. Schaffer data for one hour survival at 21°C (see figure two of Schaffer et al.) also shows a viability trough at 50% RH rising at humidity above 50% followed by a decline[27]. These features of Harper and Schaffer further support the Lowen 20°C data for 50% RH, which was already of sufficient statistical significance. Additionally, Schaffer supports the 65% RH increase in transmission called out as statistically of insufficient power by Lowen et al. A further argument in favor of the 65% RH increase in transmission is care to present conservative contagion contours where there is a question; thus we retained the feature showing a rise in contagion at 65% RH.

For visual inspection purposes the 25%_G7 transmission estimate contour is emphasized and became the reference using the following rationale.

Since Lloyd-Smith et al. reported the SARS epidemic was primarily propagated by superspreaders infecting 4 or more people[33], we chose conditions that should limit spreading to infect 1 or 2 on average.

The guinea pig experiments of Lowen were performed for 7 days. In most public places such as banks and hotel lobbies with good conditions for transmission of influenza, the time people spend is on the order of 10 minutes. This corresponds by integration of equation 2 to a crude risk of 1/1959 for any one entry and exit at the 25%_G7 contour. Thus, assuming 300 patrons per day yields approximately 1 case every 6 days for a usual branch, assuming an infected individual is shedding virus continuously. Risk on a bus ride of 8 hours at the 25%_G7 contour yields a crude risk of 1/41, which roughly corresponds to 1 new infection per 8 hour bus ride assuming continuous virus shedding. Assuming an influenza case of 7 days virus shedding duration, we thought it improbable most locals would take more than two 8 hour luxury bus rides with 50 passengers per bus in that time span (vendors excluded) for a total of 2 new infections. (Table 1)

These crude risks represent the rationale of our risk cutoff in enclosed spaces. However, we do not think one contour cutoff is always appropriate.

A considerable amount of data was collected for aircraft, however, transmission risk on aircraft is complex. First, the influenza contagion space relative to temperature and RH on aircraft is mostly unknown since studies have not been done below 20% RH, and large portions of flights can occur with RH in the 3% to 15% range (Figure 10). How such extremely low RH affects transmission is unknown. Second, although influenza was communicated well to aircraft passengers circa 1979 during a ground delay (38 of 54 passengers in 4.5 hours, 1 index case) given lack of air circulation[34], HEPA filtration of recirculated cabin air on most aircraft today mitigates this hazard, together with outside air exchange in flight. Literature raises questions about efficacy of HEPA filters on aircraft[35]; however, the careful epidemiology of SARS on an aircraft[24] suggests HEPA filters and air exchange were fairly effective on that aircraft because of the apparent wake pattern of infection. That correlates with modeling of wake particles carried behind persons moving along the aisle[36]. SARS, like influenza, is an enveloped RNA virus of the same size, which likely has similar filtration characteristics. Third, on some aircraft, ozone is negligible due to catalytic units[37]. Ozone would be expected to deactivate virions significantly[38], but the extent this occurs at ozone levels of aircraft lacking catalytic units is unknown. One must also weigh ozone causing a possible increase in host susceptibility and worse course of disease[39], another unknown. Consequently, we believe risk, per our study criteria, to be low on aircraft outside deplaning, but worth continued attention.

Examining literature questioning whether influenza is transmitted by aerosol, it appears a major question is the distance at which aerosol transmission of influenza occurs, and we contend that such transmission varies greatly with conditions. There is also a proposal that vitamin D hormone is a regulator of seasonal influenza incidence in the context of questions raised about influenza's fundamental epidemiology[10].

Digital psychrometers cost on the order of $100 to $160 in single quantities (larger purchases may be possible at lower cost). Based on visualizing the data collected, steps can be taken to attempt to either move higher risk environments in the direction of lower aerosol transmission risk, or else direct the use of other measures in those environments. A chart (Figure 1) should be inexpensive to distribute.

Luxury buses deserve special attention, as they cross borders and travelers spend upwards of 4 hours in the environment. The close quarters of these vehicles' recirculating air are a good opportunity for aerosol transmission of influenza (and other respiratory diseases).

Vendors to luxury buses represent the potential to be superspreaders of influenza, visiting many buses, speaking and moving systematically through the bus for periods of time per bus from 2 to 45 minutes, spending totals of hours per day in buses in multiple visits. Vendors also have direct contacts with passengers, increasing their chances of acquiring and then transmitting infection. Superspreaders were important for the SARS epidemic, showing an unexpected distribution of infectors[33] and probably are for influenza. Bus companies distributing masks and hand sanitizer to vendors to protect them may yield benefits.

Public health can educate maintenance staff of luxury hotels, newer malls (small and large), offices, dining establishments, banks, and colleges about caring for their environmental settings during a flu season. For those locations that have a need to portray an image of higher status, and hence comfort, how to balance that is a question for HVAC engineers.

Based on our examination, we think that influenza transmission on aircraft is probably a not a serious risk most of the time, as discussed above, although the passenger numbers are quite large. Most of the risk appears to be off the aircraft, although wake effects can be troublesome, and T and RH may regulate whether wake effects can occur. Lacking viability data for the humidity range common on aircraft, how that works is clearly an open question. However, since the period starting when passengers stand up after landing to emptying the aircraft does fall within our parameters and is quite short, it may be an insignificant cost for airlines to flush cabin air from the end of the runway after landing until passengers leave the aircraft to further lower transmission risk on aircraft.

Many viruses and bacteria will display viability conditions opposite to influenza. Endemic disease threat such as M. tuberculosis should be weighed since TB is correlated with tropical climates[47], suggesting its aerosol transmission is optimum in high RH and warm temperature. TB is a hardy organism that forms culturable aerosol from coughing[48] but the aerobiology of transmission is not well explored[47]. Guinea pig model TB transmission studies in parallel with influenza exploring variations of temperature and RH relative to HEPA filtration and ultraviolet light as recommended by Nardell and Piessens[47] would be desirable. The TB concern indicates that in TB endemic regions humidity lower than 60% should be targeted on the transmission contour map (Figure 1). There are also commonalities between other measures that can minimize influenza aerosol contagion and measures against TB and other microbial aerosol (such as UV irradiation of upper air [49]).

Climate control for enclosed spaces should be added to public health to control influenza epidemics. The range between 20% and 80% RH covers most human habitation outside of aircraft, and the region above 80% RH appears to be a low transmission risk, although both these regions should be explored. In the tropics, getting an indoor facility out of the region of highest risk should be simple and low or no cost. In temperate regions, controlling AC to stay out of the optimum transmission region may be more challenging. At a minimum, the low or no cost step of changing climate control parameters should not raise the R ₀ (reproductive number) of an influenza epidemic and will lower it considerably if seasonal influenza transmission is any guide. The authors hope for further refinement; however, this is an inexpensive starting point with highly probable benefits, which should be a net savings for nations. For those who perform epidemiological studies, analyzing data in light of temperature and relative humidity will help our understanding of influenza epidemiology.