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JV - Influenza Seasonality: Underlying Causes and Modeling Theories

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  • JV - Influenza Seasonality: Underlying Causes and Modeling Theories

    Does anyone have access to this full article. I can get only certain Journal of Virology articles without a subscription. I do not intend to post it. My email is

    Influenza Seasonality: Underlying Causes and Modeling Theories
    E Lofgren, N Fefferman, Y N Naumov, J Gorski, and E N Naumova J. Virol. published 20 December 2006, JVI.01680-06v1



  • #2
    Jason got it before I did - here it is for reference

    JVI Accepts, published online ahead of print on 20 December 2006

    21 Influenza seasonality is one of the most obvious and basic characteristics of the disease
    22 process, yet remains the least understood. Most epidemiological studies take this annual
    23 oscillation as a basic assumption and build from it to examine other questions, while only a few
    24 papers have specifically focused on seasonal trends themselves. By isolating the particular
    25 cause(s) of seasonality in influenza, it may be possible to determine effective strategies for
    26 intervention in local epidemics, or even global pandemics. This paper provides a review of the
    27 diverse array of proposed hypotheses for the underlying cause of seasonality in influenza,
    28 including seasonal host health, social causes, and environmental factors such as climate and
    29 temperature. Additionally, we provide preliminary epidemiological and mathematical evidence
    30 supporting the need for a holistic perspective of all aspects of influenza dynamics within a
    31 population. We highlight gaps in these explanations and propose that seasonality may be an
    32 emerging property of the virus itself.
    Influenza seasonality: virological and epidemiological perspectives

    35 Influenza (or ?flu?) hospitalizes more than 200,000 people yearly and results in 36,000
    36 deaths from flu or flu-related complications in the United States (14), striking both elderly and
    37 infant populations particularly hard (22). Two members of the Orthomyxoviridae family,
    38 influenza A and B primarily cause this acute viral respiratory disease. Both viruses are
    39 characterized as enveloped viruses that contain 8 negative-stranded RNA segments that encode 9
    40 structural and 2 non-structural proteins (influenza virus A) or 10 structural and one non41
    structural proteins (influenza virus B). Because of the higher levels of morbidity and mortality
    associated with influenza A, in part due to the large reservoir 42 of the virus in aquatic birds, we
    43 will predominantly restrict ourselves to discussions of this virus.
    44 Each influenza A virus particle is surrounded by host cell membrane where two out of
    45 three surface proteins, hemagglutinin (HA) and neuraminidase (NA) are responsible for viral
    entry into host cell, and are the targets of B-cell immunity 46 (12). While 15 subtypes for HA and 9
    47 subtypes for NA exist in the wild, historically only HA subtypes 1, 2, and 3, and NA subtypes 1
    48 and 2 have been responsible for stable human infections (48). Human variants of HA 1-3 have
    49 high affinity to NeuA
    α2.6Gal-containg receptors on mucosal lining of human bronchi50
    pulmonary system, and are responsible for viral binding to the potential host cell. NA?s
    51 enzymatic cleaving of sialic acid appears to have functions both in reducing the number of
    52 ?decoy? receptors that may render viral attachment to epithelial cells of the bronchi-pulmonary
    53 system (55), and critically in allowing the release of viral particles from infected cells (32).
    54 Two processes allow the virus to quickly change in response to selection, and adeptly
    55 evade B-cell immunity through neutralizing antibodies (immunoglobulins). One is ?antigenic
    56 drift?, the remarkable ability to mutate rapidly, a function of the infidelity of RNA polymerases.
    57 This mutation alters the major antigenic proteins, hemagglutinin (HA) and neuraminidase (NA),
    58 which can maintain their function while still undergoing considerable amino acid substitution
    59 (11,29). The mutation effect is more restricted for other proteins involved in viral RNA
    60 replication and packaging of the viral genomes. The second process, ?antigenic shift? is the
    61 ability for the virus to undergo reassortment of its genome when more than one virus has infected
    62 a cell, a function of the segmented viral genome. The combination of these processes leads to
    63 viruses capable of evading host B-cell immune responses (18,82). The pathogen originates in
    64 avian host species and is traditionally thought to infect human populations only via intermediary

    hosts (e.g. pigs), although there is now evidence that direct 65 bird-human transmission is also
    66 possible (74).
    67 Novel influenza viral strains can be the source of infrequent but devastating pandemics,
    68 most famously the 1918 pandemic, which killed between 20 and 40 million people (42). These
    69 pandemics are associated with major shifts in the HA and NA proteins that define viral strains.
    70 More routinely, influenza also generates epidemics or large outbreaks. Epidemics can be traced
    71 to a drift in the HA and NA proteins that circumvents sufficient pre-existing B cell reactivity as
    72 to render the individual susceptible. Often influenza may produce sporadic localized outbreaks.
    73 Such cases have been not been the focus of research into influenza epidemiological virology but
    74 it is likely that they are also associated with a transient viral subpopulation with immuno-evasive
    75 properties, which has been borne out with mathematical models (29,68,69).
    76 In temperate climates, flu infections at whatever level of intensity are characterized by a
    77 flu season. In these areas, the disease is thought to exist at a low level throughout the year, but
    78 exhibits a marked seasonal increase, typically during the winter months. Influenza epidemics and
    79 outbreaks occur in tropical areas as well, although the timing and impact is not as well defined
    80 (15,71,81). Local epidemics begin suddenly, peak in 2 to 3 weeks and last for a total of 5 to 10
    81 weeks. It is believed that in most cases the seasonal outbreaks of influenza originate in China,
    82 spreading from there (17).
    83 Infectious disease dynamics offer a wide variety of intriguing and unexplained
    84 phenomena, yet none is as consistently observed while still remaining so poorly understood as
    85 seasonality in influenza. There is a gap in how the diverse studies encompassing immunology,
    86 mathematics, epidemiology and virology combine to form a complete picture of flu seasonality.
    87 This may be due to the daunting complexity of seasonality itself, which is likely to reflect the
    action of a vast multitude of variables. For whatever reason, only limited r 88 esearch has actually
    89 focused on supporting or rejecting each of the proposed underlying causes of seasonality in
    90 influenza and most of these, while thorough and well supported, are largely confined to a single
    91 discipline. This paper will provide a review of the proposed theories from existing literature and
    92 examine possible links between them that may suggest a more unified view of influenza
    93 seasonality, as well as call to attention gaps between the differing ways of understanding
    94 seasonality. Once actually understood, it may be that the mechanisms governing the seasonal
    95 pattern in the incidence of influenza can provide greater insight into all aspects of the dynamics
    96 of transmission and maintenance of this disease (59). The authors believe that the current
    97 research has reached the stage such that advances in our understanding of influenza seasonality
    98 will emerge from an enhanced conceptual understanding of the biological dynamics of infection,
    99 not a breakthrough in mathematical methodology. As such, the bulk of the review is devoted to
    100 the disease parameters being modeled, rather than modeling techniques, although these are
    101 briefly examined in their potential applications.
    Biological causes: seasonality as an emergent property of viral infection and host status

    Viral evolution and the immune response to virus

    106 As described above, influenza A virus is extremely adept both at evading the host
    107 immune system and at achieving heightened virulence. The HA and NA antigenic epitopes of
    influenza virus A have a very high rate of mutation (estimated at 6.7 x 10
    -3 108 nucleotide
    109 substitutions per site per year for HA). When properly positioned, these mutations can prevent
    110 the immunoglobulins raised against the HA and NA from previously encountered strains from

    binding to the mutant (31,44). This results in the rapid turnover 111 of viral strains, hampering
    112 (though not totally negating) the ability of previously generated B ?cell mediated immune
    113 response to guard against re-infection, even within the span of only relatively few viral
    114 generations. Small, subtle mutations in HA and NA are incapable of escaping the limited cross115
    protection from closely related viral strains that the proliferation of B cells provides, preventing
    116 the diversity of circulating strains from increasing explosively (29). This gives influenza its
    117 characteristic epidemiological profile of a frequently shifting dominant subtype, rather than a
    118 huge number of closely related subtypes which often appear in other rapidly mutating RNA
    viruses 119 (20). Models incorporating this limited cross-protection driven by viral adaptation and
    120 immune response have been shown to generate cyclical patterns closely resembling the seasonal
    121 patterns observed in influenza (29).
    Influenza virus B mutates at a much lower rate (3.2 x 10
    -3 122 nucleotide substitutions per site
    123 per year) (57), though still under similar selective pressure, and appears to rely less on the
    124 dramatic genetic reassortment from which much of influenza A virus?s variability is derived.
    125 While the complete mechanism of viral evolution in influenza B virus is not yet understood, this
    126 slower and more erratic viral evolution may be the driving force behind both the less frequent
    127 and less periodic emergence of Influenza B virus capable of infecting large numbers of people
    128 (57). Because influenza B virus is restricted to its human host, the lack of an external reservoir in
    129 which the virus can evolve independently may also play an important role in its decreased
    130 virulence, although there is some suggestion that the virus may have a reservoir in seals (62).
    131 While antibody mediated protection is generally considered the primary protection
    132 against infection, another source of protection is mediated by CD8 T cells that kill already
    133 infected host cells. This cellular immunity does not protect against infection but can rapidly

    ameliorate infection. The immunological studies conducted on hum 134 an populations revealed an
    135 interesting pattern where T-cell mediated immune response is targeted against most conserved
    136 influenza proteins, such as matrix M1 and NP, which are important in viral particle assembly and
    137 extrusion (budding). In fact, age-related studies concluded that be age 15 years children develop
    138 anti-influenza CD8 T cell memory comparable with adults (47), and the frequencies of memory
    139 cells might reach 0.11-0.56% of the total CD8 T cell pool (43). However, there is a report that
    140 memory T cells specific to influenza antigens being at high frequency among CD8 T cells in the
    141 peripheral blood might have diminished functional capacity such as interferon-g production and
    142 antigen-induced proliferation in elderly individuals, hampering their ability to control the
    143 magnitude of viral infections (21).
    144 The role of CD4 T cells in the immune response to influenza is well described (13,65,72).
    145 Mature B cell responses generally require T cell help, and thus a viral antigen must be able to
    146 both stimulate the B cell (hapten) and provide a T cell epitope (carrier). While it might be
    147 possible that very mature B memory cells may be able to dispense with T cell help, for most
    148 responses the absence of T cell help could lead to an absence of B cell effector function. CD4 T
    149 cells may also play a role in generating a strong CD8 memory response.
    150 Cyclical patterns in other viral diseases with different immunological and virological
    151 behavior can provide useful comparisons to influenza virus. Measles shows a clear cyclical
    152 epidemic pattern, although outbreaks are often separated by years, rather than seasons (30,54).
    153 However, in this disease, human immune memory provides a powerful, lifelong immunity
    154 subsequent to initial exposure. This limits the potential of a population to sustain epidemic levels
    155 of infection after an outbreak until the birth rate and immigration can once more provide a
    156 sufficiency of susceptible hosts. Influenza virus?s ability to evade protective immunity via the
    introduction of new strains replenishes that pool of available 157 susceptible hosts much more
    158 rapidly, shortening the expected length of the epidemic cycle (37). What is lacking is a linkage
    159 between viral evolution and the wide assortment of other potential factors in influenza
    160 seasonality. Theoretical modeling of viral evolution, or empirical studies, when combined with
    161 the examination of high level weather events, mass population mixing and other seasonality
    162 factors could establish whether viral evolution is a cause or an effect of differing pathogen-host
    163 relations and disease profiles, and how these interact on a microbiological level, where vaccines
    164 and prophylactics can address the disease directly.
    Seasonal host health

    167 Seasonal variations in the health and physiological status of animals are quite common. It
    168 is not surprising that the immune system may experience a pattern of ebb and flow that could
    169 leave a host animal vulnerable to infection. It has been suggested that the observed seasonality of
    170 influenza is the result not of sweeping waves of disease traveling across the globe, but rather of a
    171 constant level of infection mediated differently by the host immune system over time.
    172 Specifically it has been posited that light/dark cycles, manifesting as melatonin levels, may have
    173 an impact on the immune system, rendering the host more susceptible to infection at different
    174 times of the year from pathogens which are present year-round (23). While little direct evidence
    175 linking seasonality to susceptibility to influenza infection has yet been published, there is a
    176 strong case for the biological plausibility of such a relationship. For instance, photoperiod ? a
    177 useful measure of seasonal variations in light exposure ? has been shown to have an impact on
    178 the immunocompetence of Siberian hamsters and mice, raising certain immune responses and
    179 lowering others (83).
    Two possible intermediaries between photoperiod and i 180 mmunity are melatonin and
    181 vitamin-D (25-hydroxy-vitamin D). Melatonin appears to work partly by regulating host immune
    182 response via Interleukin-1
    β (IL-1β) levels, which rise when melatonin is present, and can exhibit
    183 a protective effect in some viral infections (9). Further studies in mice have shown that an IL-1

    184 deficiency results in a higher mortality upon influenza infection (46). Vitamin-D levels have a
    185 strong effect on immunity by promoting CD4 T cell and mucosal antibody responses (40) and
    186 vitamin D levels are directly related to the amount of sunlight. In both these cases photoperiod
    187 effects may be considered as altering normal levels of immunity. The relative impact of each ?
    188 and other factors ? is not yet quantified however, and provides ample opportunity for further
    189 examination. Dowell (2001) discusses several studies demonstrating that subjects exposed to
    190 influenza during the summer were less likely to develop the disease compared to those in the
    191 winter, suggesting a host-based defense mechanism active at high levels during the summer. Left
    192 unexplored was the possibility of seasonal fluctuations of the nature of the virus itself. A clear
    193 and definitive relationship between photoperiod, host immune response and influenza infection
    194 has yet to emerge, but the groundwork has been laid for continuing research.
    195 Host nutrient intake may also contribute to the seasonal patterns of flu. Low host levels of
    196 selenium (Se) lead to an increase in the rate of viral mutations, particularly in the coding of the
    197 viral M1 protein that has been shown to increase virulence in mice (8, 60). Additionally, mice
    198 fed a long-term diet rich in the antioxidant Vitamin E have been shown to have lower virus titers
    199 in the lungs after challenge with influenza as well as lessening the anorexic symptoms of
    200 influenza infection that lead to weight loss (39). As both vitamins are taken up in food, it has
    201 been proposed that seasonal fluctuations in diet (due to the availability of certain foods) lead to a
    202 decrease in antioxidant levels, an increase in oxidative stress, and a commensurate increase in
    viral mutation and, therefore, infection rates. The solid and well supported b 203 iological plausibility
    204 of the host?s physiological standing contrasts with a paucity of epidemiological evidence for the
    205 same. Questions of how largely local variations in immune response or nutrient intake give rise
    206 to global patterns of disease, and how changes in modern lifestyles with the widespread
    207 availability of nutritional supplementation, artificial lighting and the like, as well as other
    208 questions arise from our currently limited understanding of host-virus interaction. Further
    209 research into seasonal immunological phenomena on a population level, especially with regard to
    210 influenza, is needed to bridge the gap between disciplines and allow seasonal immunology to be
    211 applied directly to the study and prevention of infectious disease.
    Pathogen survival and transmission: social and environmental causes

    214 When influenza virus occasionally makes the transition from its natural reservoir of
    215 wildfowl and spread to humans, it is transmitted entirely through person-to-person contact. The
    216 viral particles replicate in respiratory epithelial cells and are subsequently excreted from the
    217 respiratory tract as small-particle aerosols (many less than 2
    μm in size) during coughing,
    218 sneezing or breathing. Incubation of the disease is very short, typically between 1 and 4 days.
    219 Spread from direct contact is also possible in some cases (82,34), although theories of
    220 seasonality based on direct contact or large droplet spread are not as well developed as those
    221 based on aerosol transmission. The classic SEIR (Susceptible-Exposed-Infected-Recovered)
    222 model of disease spread is extremely sensitive to the underlying population to which that model
    223 is being applied, as it provides both the pool of susceptible hosts who may come down with the
    224 disease, as well as the already infected individuals (spreaders) who will pass the virus on to
    225 them. A model of influenza must, therefore, examine the behavior of the infected population, and

    the possibility that the patterns of seasonal epidemics that characterize 226 flu are a result of the
    227 population itself.

    230 The person-to-person spread of virus-laden aerosol particles is greatly enhanced by
    231 having a dense population of susceptible individuals surrounding each infective subject, thereby
    232 maximizing the potential for the spread of infection. Crowding has therefore been implicated as a
    233 risk factor for a wide range of viral and bacterial diseases including influenza (71,64,10,5,54).
    234 The origin and spread of the 1918 influenza pandemic has attributed to the hyper-crowded
    235 conditions among military bases during the First World War, allowing several theories
    236 concerning the pandemic?s origin to emerge, one implicating the European Western Front (63),
    237 and another a U.S. Army base in otherwise isolated and sparsely populated Haskell County,
    238 Kansas (7). The crowded conditions of the base, as well as crude and under-supported medical
    239 facilities, would have given the otherwise isolated flu outbreak a massive pool of susceptible
    240 individuals, who were then shipped all over the country and abroad to fight in the Great War,
    241 carrying the disease with them.
    242 Given the profound effect crowding has on the spread of viral diseases and the emergence
    243 of epidemics, it is not surprising that it is often forwarded as a potential source of seasonality.
    244 Seasonal fluctuations in host-behavior might give influenza a greater opportunity to spread and
    245 maintain itself at epidemic levels during the winter (58). On a fundamental level, it is plausible
    246 that crowding and seasonal social/behavioral patterns are a source of seasonality, although no
    247 studies directly examining this plausibly causal relationship have been published. Much of the
    248 literature examining this theory treats crowding as a fundamental assumption from which to
    examine further, unrelated questions (28,45,66), and it is an especially 249 frequent explanation of
    250 seasonality in popular media. Only a few studies have directly examined this underlying
    251 assumption (e.g. Dowell, 2001) addresses the plausibility of the theory critically, asking why ? if
    252 crowding is the source of influenza seasonality ? are there not frequent epidemics at busy
    253 international summer conventions? The answer might lie in crowding not as the driving cause of
    254 seasonal incidence of influenza, but as a contributing factor, amplifying what would otherwise be
    255 a subtle and perhaps less pronounced change in virus biology, transmission or host response.
    Ambient temperature

    258 Decreased temperature is an environmental variable frequently found to be associated
    259 with high levels of seasonal influenza infection (18). Accepted as a basic assumption, this
    260 association has been used to explain the decreased effect of seasonality in the tropics (67), and is
    261 cited extensively? although not rigorously examined ? in articles examining the rate of acute
    262 respiratory infections (15,19,73,75). No direct biological justification for this effect has emerged
    263 and it is becoming an increasingly inadequate explanation as our view of seasonality is refined
    264 (24).
    265 It may be that ambient temperature is simply extremely strongly correlated to the actual
    266 mechanism responsible for driving seasonality. These actual causes could be among those
    267 already discussed, such as a decrease in temperature inducing behavioral changes such as
    268 increased crowding. Or it may be another cause, as yet unexplored. Perhaps decreased ambient
    269 temperature increases physiological stress and energy costs for thermoregulation. This could, in
    270 turn, weaken the immune system thereby increasing susceptibility to infection from an unaltered
    271 rate of exposure. Influenza in waterfowl is an enteric virus and has adapted to the higher
    temperature associated with fowl basal metabolism. T 272 he shift to a human host may have
    273 involved a cold adaptation step that is further enhanced at colder temperatures. Another possible
    274 temperature related factor is that viral particles are capable of prolonged persistence in colder
    275 environments. Given the unclear interactions among temperature, all of the myriad of other
    276 correlated mechanisms proposed, and the biology of influenza virus itself, further examination of
    277 this effect is clearly warranted.
    Indoor heating

    280 Paralleling the direct effects of temperature and harsh weather on the biology of either
    281 host or pathogen, it is possible that human defenses against declining temperature may
    282 themselves contribute to the seasonality of influenza. Indoor heating levels should increase as the
    283 temperature drops, resulting in a continuously recirculated body of air with very low humidity.
    284 These conditions are ideal for the persistence of viral particles in the environment, with the
    285 typical furnace filter incapable of effectively filtering the very small particles to remove them
    286 from the circulating air ? although this may be offset by inactivation of the virus at high
    287 temperatures. A lack of concurrent increase in influenza infection in summer, when the use of air
    288 conditioning systems is high, may be based largely on the mechanistic differences between the
    289 two systems. AC lowers the absolute humidity of the air via condensation (potentially trapping
    290 virus-bearing aerosols within the unit itself) while heaters lower only the relative humidity, and
    291 never exposing the air to a wet condensing surface. Large-scale heating, such as that in
    292 apartment buildings, offices and university dormitories would create a viral dispersion
    293 mechanism resembling the unintentional one potentially responsible for the Amoy Gardens
    294 SARS outbreak (49). Thus far, this theory, though consistent with other hypotheses governing
    seasonality, has not been discussed in the literature or examined e 295 mpirically in the case of
    296 influenza, although levels of indoor air pollution ? which would be continually recirculated in
    297 the winter ? have been shown to be risk factors for lower respiratory infection (70).
    298 Mathematical models have also been developed examining the risk of indoor, airborne infection
    299 risks. Small changes in ventilation accounted for dramatic changes in the R
    0 (reproductive ratio,
    300 i.e. the number of secondary infections each primary infection gives rise to in an entirely
    301 susceptible population) for influenza, although the authors examined risk of infections for single
    302 outbreaks, and did not discuss mass ventilation changes ? such as those that may accompany the
    303 onset of winter ? as a potential driver of influenza seasonality (50).
    Air travel

    306 The role of air travel in the modern epidemiology of influenza has been examined in a
    307 substantial body of work, and its impact on the spread of the disease can be subdivided into two
    308 categories. The first is the role of air travel on the geographic spread of the disease after an
    309 epidemic or pandemic strain has emerged. Using data from the 1968 Hong Kong pandemic,
    310 models based on air traffic originating in Hong Kong and carrying passengers with an emerging
    311 strain of pandemic influenza show rapid and wide dissemination of the virus across both
    312 hemispheres (35). Beyond the model?s implications for the spread of a major pandemic strain,
    313 the extremely rapid and universal spread of the hypothetical virus is also applicable to influenza
    314 seasonality. Flu season is characterized by near simultaneous appearance of influenza epidemics
    315 hemisphere wide. Rapid dissemination of a virus via air travel provides a possible alternative to
    316 the theory that exposure is the result of a continually seeded viral pool. Such an alternative
    317 would have a substantial effect not only on the spread of a particular strain of the virus, but in the
    global evolution of the disease and host?s immune responses. A 318 constantly exposed population
    319 that becomes vulnerable to infection triggered by a seasonal change in virus biology,
    320 immunocompetence or social habits is vastly different than a population who ? due to the rapid
    321 flow of people and diseases across the globe ? is periodically bombarded by a new viral strain in
    322 a short period of time over a wide geographic area. The air travel dissemination model poses
    323 intriguing possibilities on the theoretical level, with the potential to shed light on the
    324 fundamental issue of the virus?s passage through human populations. What is needed now is
    325 epidemiological and virological evidence to confirm (or fail to confirm) this particular
    326 mechanism of spread.
    327 The second category of air travel studies show that it is possible to forecast the severity of
    328 the influenza season based on air traffic patterns. Using a standard compartmental model of
    329 influenza transmission coupled with data from U.S. air travel statistics, Grais et. al. were capable
    330 of predicting the influenza season of major U.S. cities, and suggest that air travel has a role in the
    331 spread of influenza or in the creation of seasonal epidemics, as air travel has a large spike in the
    332 winter period, concurrent with influenza season. The model?s inability to accurately predict the
    333 incidence of influenza in several cities, and the absence of a peak in influenza during the summer
    334 ? a time of high air travel ? are detailed by the authors (36).
    Bulk aerosol transport

    337 Beyond the issues of crowding, the aerosolization of influenza virus particles from
    338 infectious individuals may itself be directly responsible for the disease?s seasonality. Coughing
    339 and sneezing (both symptoms of influenza) produce massive amounts of small-sized aerosol
    340 droplets with very high viral titers, which travel through the air at speeds of nearly 100ft/second
    (77). A single patient or population of patients could, over the course 341 of several days, represent a
    342 significant source of aerosolized viral particles that could disperse over a wide area. Modeling of
    343 the 2003 SARS outbreak at the Amoy Gardens housing complex in Hong Kong (which infected
    344 329 people, killing 42) has very thoroughly examined the role of so-called ?bioaerosols? in the
    345 transmission of disease (49). The non-uniform spatial pattern of the cases was found to be
    346 attributable to air currents created by the architecture and ventilation of the complex, which
    347 effectively circulated the virus particles produced by a single individual source to multiple
    348 apartments in the complex.
    349 On a population level, an entire city experiencing an epidemic could produce staggering
    350 amounts of virus aerosols, yielding something not unlike the medieval concept of infective
    351 miasma. It has been suggested that this mass of infective particles is responsible for the
    352 seasonality of influenza by using global convective currents in much the same way as the
    353 ventilation system of the Amoy Gardens housing complex. Originating in Asia during the winter,
    354 aerosol particles may be conveyed into the upper atmosphere by frequently forming cyclogenic
    355 systems. Here, the low temperature and relative humidity of the upper atmosphere may enhance
    356 long-term survival of viral particles, allowing them to be picked up by a westerly air current and
    357 transported to North America within the span of a few weeks. Once over the North American
    358 continent they are forced lower by frequent cold fronts. In the summer, the atmosphere over
    359 North America is less favorable to dispersion, and South Asian pressure systems are weaker and
    360 shifts directions, severely depleting the flow of particles and would therefore yield the observed
    361 seasonal patterns of infection (38).
    362 Although dispersed, the virus particles that survive this trip would find themselves in a
    363 favorable environment of cold temperatures, and dry air both inside and out ? both of which have
    been shown to be favorable for viral survival (41). Aerosolized t 364 ransmission of influenza has
    365 been shown to be very efficient, and so only a very small number of particles are needed to reach
    366 the lungs of a susceptible individual to initiate an epidemic (16). While the movement of small
    367 virus bioaerosols in the upper atmosphere is understandably difficult to measure, advances in the
    368 sampling of air for airborne viruses show promise in elucidating the quantity of influenza being
    369 carried on the wind (2,3,76). An expanded study of atmospheric patterns that would extend this
    370 mechanism to the rest of the world has yet to be brought forward and would have to emerge,
    371 accompanied by actual sampling data, to be useful in elucidating any possibility of seasonal
    372 fluctuations in the global spread of the virus. This notion of transoceanic viral movement in
    373 aerosolized form is extremely speculative and difficult to evaluate, however several reported
    374 examples of transoceanic dust movements exist. Beyond the issues of the physical movement of
    375 the viral particles involved, the added issues of viral survival for such a long period of time ? and
    376 under increased exposure to UV radiation ? seem to make likelihood of an infectious virus
    377 surviving a trans-Pacific journey somewhat unlikely.
    El Ni?o

    380 The El Ni?o Southern Oscillation (ENSO) is a semi-periodic, long term warming of the
    381 upper ocean in the tropical eastern Pacific Ocean, and represents the largest signal of
    382 atmospheric-oceanic variation and is capable of influencing climates across the globe (80). Links
    383 between El Ni?o episodes and infectious disease have been widely reported for a number of
    384 different diseases. Oscillations in climate due to ENSO events were associated with influenza
    385 morbidity and mortality in France between 1971 and 2002, with a rise in both during cold
    386 periods of the ENSO cycle (79). A second study of the hospitalization of women in California
    for viral pneumonia between 1983-1998 showed a similar association f 387 or the city of Sacramento,
    388 CA, although it?s results are much less clear, and no association was found for San Francisco or
    389 Los Angeles (26). No biological mechanism for this association has been put forward, although it
    390 has been suggested that atmospheric and climate variations caused by ENSO cycles may drive
    391 bulk aerosol transmission or increase crowding due to inclement weather.
    Mathematical modeling of influenza

    394 While many mathematical models have examined theoretical aspects of seasonality in
    395 host/pathogen systems, very few have tailored their investigations solely to the parameters and
    396 variables of influenza in humans. Although these more abstract models are crucial to the
    397 understanding of infectious disease dynamics a whole (including influenza), we will limit our
    398 discussion primarily to models which have been strictly focused to the seasonality of influenza.
    399 Within this scope, theoretical models that have incorporated an examination of seasonal trends in
    400 disease incidence have chosen one of two main perspectives: Seasonal Forcing or Emergent
    401 System Properties.
    402 Seasonal forcing, altering a model parameter at varying points through the year, is the
    403 mathematical method of expressing an underlying process that affects the etiological rates of the
    404 disease based on a seasonal pattern in the occurrence of the process (51). This underlying process
    405 can stem from any source and the motivations for including resulting changes in the parameters
    406 used to describe the disease dynamics can come either from a desire to incorporate
    407 mathematically some of the hypothesized theories governing seasonality already discussed (e.g.
    408 variations in host immunocompetence) or to prevent the absence of seasonal oscillation in the
    409 outcome from making it more difficult to validate model predictions using reported data.
    Mechanistically, seasonal forcing can be accomplished by altering 410 parameters governing
    411 environmental exposure, the reproductive rate of the disease (R
    0), or infectivity, or by
    412 incorporating a separate variable into the model equations.
    413 While seasonal forcing incorporates external, seasonally governed processes into
    414 mathematical models, it is possible to examine internal mathematical properties of disease
    415 dynamics in order to look for inherent patterns of oscillations in the processes themselves.
    416 Closely related to the idea of true seasonal forcing is the idea of dynamical resonance (25).
    417 While models of seasonal forcing are founded on the belief that annual oscillations are driven by
    418 large-scale, observable changes, this theory proposes that many minute changes in R
    0, so small
    419 as to be empirically undetectable, can act in concert with the duration of the infectious period
    420 and partial immunity, together generating regular seasonal oscillations. In general, these sorts of
    421 internally generated patterns are referred to as ?emergent system properties? and can also arise
    422 for a variety of different reasons. The most commonly studied is the phenomenon of bifurcation:
    423 the term used to describe systems that have a behavioral threshold beneath which they exhibit
    424 one set of behaviors and above which they exhibit another. It has been shown many times that,
    425 by examining multiple strains of influenza into a modeled system that incorporates imperfect
    426 cross-protective immunity among strains, bifurcations can arise yielding periodic oscillations
    427 (33,61). A number of cross-protective immunity models, while not specifically examining
    428 bifurcation, have also incorporated rates of influenza strain mutation in order to produce seasonal
    429 trends (4,68,69)
    430 Some models have tried to induce seasonality as an endogenous property of within431
    system dynamics without altering either external seasonal forces or internal strain mutations or
    432 multi-strain cross protection based solely on theories of stochastic processes, the influence of

    random noise in the system and cascading local effects (1). TRANSIMS 433
    (53) and EpiSims (27)
    434 are particularly ambitious in their attempt to capture all of the complexity of a functional society
    435 and experimentally examine the impact of direct intervention strategies on disease spread
    436 throughout a population. These models are now being adapted to directly examine the impact of
    437 pandemic influenza (6). One recent study (78) has examined the spatial movement of individuals
    438 on varying scales as a factor in determining the synchrony of disease incidence across large
    439 distances. This spatial synchrony could then yield annual oscillations. These models attempt,
    440 each in their own way, to incorporate much of the complexity of real-life population dynamics
    441 and produce patterns of influenza as emergent properties of populations themselves. Social
    442 interactions among different groups within a single population have also been shown to lead to
    443 oscillations in the incidence of influenza. By parameterizing the social interaction model
    444 presented in Fefferman & Naumova (in prep) using constant, age-specific values for the etiology
    445 of influenza taken from Longini et al. (2004), we were able to produce incredible variation in the
    446 resulting influenza incidence patterns by comparing different interaction rates among social
    447 groups (see Figure 1). Based on these results, it is apparent that social interactions by themselves
    448 can be responsible for periodic oscillations. This lends mathematical support to the theory that
    449 many different facets must contribute to the resulting observed seasonal patterns and that, to
    450 understand the whole, it is important to understand all components within a holistic context.
    Towards a holistic view of seasonality

    453 The myriad theories accounting for seasonality reviewed in this paper, as well as those
    454 that will hopefully emerge as influenza continues to rise in prominence, suggest that the elegant
    455 and predictable periodicity of non-pandemic influenza is caused by a less than straight-forward
    interaction of many different factors. The authors suggest that recognition of 456 this complexity,
    457 and the likelihood that seasonality arises from many different factors, is essential for the
    458 continued examination and elucidation of seasonality.
    459 Of particular note are the gaps between theories and between disciplines. While
    460 epidemiologists, virologists, immunologists and mathematicians have all developed laudable
    461 theoretical and empirical models to explain seasonality, none are so complete as fully and
    462 adequately explain the phenomenon. Our decoding of the dynamics of non-pandemic influenza
    463 must take place on biological, social and environmental levels, but more importantly must take
    464 place between disciplines. Complex networks of interactions between individual patients and
    465 their immune systems, society as a whole, global and local weather and the continual mixing and
    466 adaptation of viral antigens to form new strains are likely responsible for seasonal flu infections,
    467 and must be understood before steps in public health and medicine can be taken to positively
    468 affect health outcomes.
    469 Influenza has been a constant global health concern since the pandemic of 1918, if not
    470 before, and yet, the most obvious trends in its incidence remain unexplained. Epidemiological
    471 investigations have primarily incorporated seasonality as an underlying assumption, focusing
    472 instead on other aspects of flu transmission and exposure. As discussed, the proposed theories of
    473 causes of seasonal trends in flu incidence span many facets of epidemiology, ranging from
    474 specific genetic properties of the virus, to the build-up of pollution in indoor heating systems, to
    475 the paths of global wind streams. Alternatively, the answer may be as yet unconsidered. It is our
    476 intention that this paper should provide a framework from which further empirical and
    477 theoretical investigation specifically into the causes of seasonality in influenza may be
    478 undertaken. Studies seeking novel mechanism and those to provide both biological plausibility
    and epidemiological evidence for existing theories are needed. A 479 n understanding of what drives
    480 seasonal trends may allow better understanding of transmission dynamics leading to better
    481 methods of prevention of annual endemic outbreaks, of pandemics of already existing flu strains
    482 and of novel emerging influenzas.

    485 The authors wish to thank of the following funding agencies for their support: the National
    486 Institute of Allergy and Infectious Diseases (U19AI062627, HHSN266200500024C), and the
    487 National Institute of Environmental Health Sciences (R01ES013171) and the Tufts University
    488 Undergraduate Research Fund. Finally, the authors wish to gratefully acknowledge our two
    489 anonymous reviewers, whose thoughts and insight into earlier versions of this work were
    490 exceedingly helpful.

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    Figure Legends:

    Figure 1: The different patterns of influenza incidence in a total population caused by the

    different patterns of social interaction among 6 etiologically distinct age groups (children

    <5 years, children 6-20 years, adults with young children, adults with older children, adults

    with no children and elderly). All 12 modeled scenarios used the same population size and

    demography. The only differences between those modeled scenarios that yielded constant

    influenza incidence (Panel A), rapidly stabilizing oscillating influenza incidence (Panel B),

    or long-term periodic oscillating influenza incidence (Panel C) were in the social interaction

    rates among these etiological groups. All parameter values and interaction rates were held

    constant throughout each scenario.



    • #3
      Re: JV - Influenza Seasonality: Underlying Causes and Modeling Theories

      See, everyone should move to Florida - The Sunshine State - to avoid "bird flu". I am renting out rooms - cheap.

      For instance, photoperiod ? a
      177 useful measure of seasonal variations in light exposure ? has been shown to have an impact on
      178 the immunocompetence of Siberian hamsters and mice, raising certain immune responses and
      179 lowering others (83).

      Two possible intermediaries between photoperiod and i 180 mmunity are melatonin and
      181 vitamin-D (25-hydroxy-vitamin D). Melatonin appears to work partly by regulating host immune
      182 response via Interleukin-1
      β (IL-1β) levels, which rise when melatonin is present, and can exhibit
      183 a protective effect in some viral infections (9). Further studies in mice have shown that an IL-1

      184 deficiency results in a higher mortality upon influenza infection (46). Vitamin-D levels have a
      185 strong effect on immunity by promoting CD4 T cell and mucosal antibody responses (40) and
      186 vitamin D levels are directly related to the amount of sunlight. In both these cases photoperiod
      187 effects may be considered as altering normal levels of immunity. The relative impact of each ?
      188 and other factors ? is not yet quantified however, and provides ample opportunity for further
      189 examination. Dowell (2001) discusses several studies demonstrating that subjects exposed to
      190 influenza during the summer were less likely to develop the disease compared to those in the
      191 winter, suggesting a host-based defense mechanism active at high levels during the summer.