Vitamin D may be more effective than masks and distancing combined for COVID ?

Vitamin D may be more effective than masks and distancing combined for COVID ?

In patients older than 40 years they observed that those patients who were vitamin D sufficient were 51.5 percent less likely to die from the infection compared to patients who were vitamin D deficient or insufficient with a blood level of 25-hydroxyvitamin D less than 30 ng/mL.

Holick, who most recently published a study which found that a sufficient amount of vitamin D can reduce the risk of catching coronavirus by 54 percent, believes that being vitamin D sufficient helps to fight consequences from being infected not only with the corona virus but also other viruses causing upper respiratory tract illnesses including influenza. “There is great concern that the combination of an influenza infection and a coronal viral infection could substantially increase hospitalizations and death due to complications from these viral infections.”

#covid19 #sarscov2 #vitaminD

Kaufman HW, Niles JK, Kroll MH, Bi C, Holick MF (2020) SARS-CoV-2 positivity rates associated with circulating 25-hydroxyvitamin D levels. PLOS ONE 15(9): e0239252. https://doi.org/10.1371/journal.pone.0239252

COVID-19 Made worse By Social Distancing?

We are led to question whether the recommended social distancing measures to prevent SARS-CoV-2 transmission could increase the number of other serious instabilities. The breaking of the contagion pathways reduces the sharing of microorganisms between people, thus favoring dysbiosis, which, in turn, may increase the poor prognosis of the disease. #covid #microbiome #dysbiosis Célia P. F. Domingues, João S. Rebelo, Francisco Dionisio, Ana Botelho, Teresa Nogueira. The Social Distancing Imposed To Contain COVID-19 Can Affect Our Microbiome: a Double-Edged Sword in Human Health. mSphere, 2020; 5 (5) DOI: 10.1128/mSphere.00716-20 https://msphere.asm.org/content/5/5/e00716-20

COVID-19 Updated Nutritional Supplement Research

COVID-19 Updated Nutritional Supplement Research

Dietary supplements an important weapon for fighting off COVID-19

Optimal Nutritional Status for a Well-Functioning Immune System Is an Important Factor to Protect against Viral Infections Nutrients 2020, 12(4), 1181; https://doi.org/10.3390/nu12041181

https://www.mdpi.com/2072-6643/12/4/1181/htm

Ayurveda and yoga for COVID-19 prevention

Public Health Approach of Ayurveda and Yoga for COVID-19 Prophylaxis Published Online:20 Apr 2020https://doi.org/10.1089/acm.2020.0129

https://www.liebertpub.com/doi/10.1089/acm.2020.0129

#Ashwagandha, #Dietarysupplements, #sars-cov-2

Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area JAMA. Published online April 22, 2020. doi:10.1001/jama.2020.6775

https://jamanetwork.com/journals/jama/fullarticle/2765184?guestAccessKey=906e474e-0b94-4e0e-8eaa-606ddf0224f5

Eurekalet

https://www.eurekalert.org/

Science Daily

https://www.sciencedaily.com/

Annals of Internal Medicine

https://annals.org/

Ayurveda, sars-cov-2, yoga, dietary supplements, immune system, , dosage, zinc, omega-3, dha, epa, nutritional status, mechanical ventilator, mortality, vitamin e, SARS coronavirus 2, Prophylaxis, immune system; viral infection; influenza; COVID-19; micronutrients, vitamins, omega-3 fatty acids, minerals, vitamin C, vitamin D, influenza, flu, virus,

Recent Nutraceutical Research into RNA virus infections including influenza and coronavirus

Recent Nutraceutical Research into RNA virus infections including influenza and coronavirus

Recent Nutraceutical Research into RNA virus infections including influenza and coronavirus

“Therefore, it is clear that certain nutraceuticals have antiviral effects in both human and animal studies,” commented Dr. DiNicolantonio. “Considering that there is no treatment for COVID-19 and treatments for influenza are limited, we welcome further studies to test these nutraceuticals as a strategy to help provide relief in those infected with encapsulated RNA viruses.”

#covid19 #coronavirus #protocolresearched

M.F. McCarty and J.J. DiNicolantonio, Nutraceuticals have potential for boosting the type 1 interferon responseto RNA viruses including influenza and coronavirus Progress in Cardiovascular Diseases,https: //doi.org/10.1016/j.pcad.2020.02.007

#covid19 #coronavirus #protocol

https://www.sciencedirect.com/science/article/pii/S0033062020300372?via%3Dihub

Elderberry shown to fight Influenza at multiple stages

Elderberry shown to fight Influenza at multiple stages

Elderberry shown to fight Influenza at multiple stages

The phytochemicals from the elderberry juice were shown to be effective at stopping the virus infecting the cells, however to the surprise of the researchers they were even more effective at inhibiting viral propagation at later stages of the influenza cycle when the cells had already been infected with the virus.

#Elderberry #Influenza # anthocyanidin

Golnoosh Torabian, Peter Valtchev, Qayyum Adil, Fariba Dehghani. Anti-influenza activity of elderberry (Sambucus nigra). Journal of Functional Foods, 2019; 54: 353 DOI: 10.1016/j.jff.2019.01.031

https://www.sciencedirect.com/science/article/pii/S1756464619300313?via%3Dihub

Dramatic FLU virus mutations may be due to antiquated vaccine manufacturing

Dramatic FLU virus mutations may be due to antiquated vaccine manufacturing

Dramatic FLU virus mutations may be due to antiquated vaccine manufacturing

Researchers discovered that by manufacturing the vaccine through the use of chicken eggs it had the unintentional consequence of causing dramatic mutations in the H3N2 Virus. These mutations the researchers speculate resulted in a equally dramatic decline in vaccine effectiveness

Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US [version 1; referees: 1 approved]. F1000Research 2017, 6:2067 (doi: 10.12688/f1000research.13198.1)

A structural explanation for the low effectiveness of the seasonal influenza H3N2 vaccine  (doi: /10.1371/journal.ppat.1006682)

Change in Human Social Behavior in Response to a Common Vaccine ( Flu Vaccine )

 

– In the 2 days immediately after influenza immunization, study participants socially encountered almost twice as many other humans as they did in the 2 days before immunization


 

CHRIS REIBER, PHD, MPH, ERIC C. SHATTUCK, MS, SEAN FIORE, MS, PAULINE ALPERIN, MS, VANESSA DAVIS, MS, AND JANICE MOORE, PHD

PURPOSE: The purpose of this study was to test the hypothesis that exposure to a directly transmitted human pathogendflu virusdincreases human  social behavior  presymptomatically. This  hypothesis  is grounded in empirical evidence that animals infected with pathogens rarely behave like uninfected animals, and in evolutionary theory as applied to infectious disease. Such behavioral changes have the potential to increase parasite transmission and/or host solicitation of care. Continue reading “Change in Human Social Behavior in Response to a Common Vaccine ( Flu Vaccine )”

Scientists creating viruses deadlier to humans

Sunday, 22 December 2013

Some of the world’s most eminent scientists have severely criticised the arguments used by some influenza researchers who are trying to make the H5N1 bird-flu virus more dangerous to humans by repeatedly infecting laboratory ferrets.

More than 50 senior scientists from 14 countries, including three Nobel laureates and several fellows of the Royal Society, have written to the European Commission denouncing claims that the ferret experiments are necessary for the development of new flu vaccines and anti-viral drugs. Continue reading “Scientists creating viruses deadlier to humans”

Vaccine’s, the Lucky Rabbits Foot, and Shhh No questions allowed ( Part 1 )

Vaccines are just a form of medicine like everything else. Some of them good, and some of them not so good. In any case you have a right to know.

Just remember Scientific Method – Observation, Hypothesis, and Theory as well as Risk to Benefit Ratio ..> But don’t get me started on Epigenetics

We should all have the freedom to inoculate ourselves based upon fact… The first one However, I threw in for fun ; )

There are many more as this is just part 1 …. Just sticking with RECENT Peer Review. But let the first Salvo fly

Change in human social behavior in response to a common vaccine and Funvax Using Vaccines to Alter Human Behavior VMAT2 Gene 

Pneumococcal vaccination in adults does not appear to work

Live Vaccination against ( German Measles ) Rubella caused Signifigant Depression up to 10 weeks – Vaccines/ Bacteria Can Alter Mood and Behavior

No significant influenza (FLU) vaccine effectiveness could  be demonstrated for any season, age or setting after adjusting for county, sex, insurance, chronic conditions recommended for influenza vaccination and timing of influenza vaccination

The Hidden Threat That Could Prevent Polio’s Global Eradication – Vaccinated Children that Become  “chronic excreters”

U.S. Court Confrims M.M.R. Vaccine Caused Autism or Cumulative  (Verified through Multiple Sources) From DEC 2012 Judgment

Pig Virus DNA Found in Rotavirus Vaccine : Millions of children worldwide, including 1 million in the U.S. exposed

Seasonal flu vaccination increase the risk of infection with pandemic H1N1 flu by 68%

Flu vaccine may not protect seniors well / Vaccine was totally ineffective

OHSU research suggests America may over-vaccinate

Some children vaccinated against hepatitis B may have an increased risk of MS

Flu shot does not cut risk of death in elderly / no decrease in hospital admissions or all-cause mortality

Measles, Mumps, Rubella vaccine linked with 2-fold risk of seizures

‘MMR vaccine causes autism’ claim banned – Followed by 15 studies that link Strong Correlation, it May

Influenza Vaccine Failure among Highly Vaccinated Military Personal, No protection against Pandemic Strains.

Live virus used in polio vaccine can evolve and infect, warns TAU researcher

India: Paralysis cases soar after oral polio vaccine introduced

Flu Vaccine offers no Protection in seniors

Common cold virus can cause polio in mice when injected into muscles

Flu shot does not reduce risk of death

Swine flu vaccine linked to child narcolepsy: EU Confirmation

WHO and the pandemic flu “conspiracies” – FULL report from the BMJ and The Bureau of Investigative Journalism  2010

A vaccine-derived strain of poliovirus that has spread in recent years is serious but it can be tackled with an existing vaccine

Dosing schedule of pneumococcal vaccine linked with increased risk of getting multiresistant strain

Expert questions US public health agency advice on influenza vaccines

Whooping Cough Vaccine is obsolete ” Bulk of the cases were in fully vaccinated children ” few cases among unvaccinated children

Flu vaccine backfires in pigs / vaccinated against H1N2 influenza were more vulnerable to the rarer H1N1 strain

Higher anaphylaxis rates after HPV vaccination: CMAJ study / significantly higher – 5 to 20 fold – than that identified in comparable school-based vaccination programs

Allergic to Gummy Bears? Be Cautious Getting the Flu Shot

Vaccination campaign doubles HBV mutations

New wonder cure for killer flu originates from the humble turnip ( H1N1 & H7N9 )

A DRINK derived from a vegetable has been hailed as a breakthrough in the search for a cure for flu.

Published: Wed, November 6, 2013

Flu could soon be banished by a landmark scientific discovery
 

Flu could soon be banished by a landmark scientific discovery [GETTY: Pic posed by model]

 

  When a particular strain of Lactobacillus brevis is eaten by mice, it has protective effects against influenza

Naoko Waki

Scientists believe it could revolutionise the way the killer virus is tackled.

They discovered that a strain of bacteria in pickled turnip, a dish popular in Japan, boosts immunity to the virus. Experts are already ­carrying out human trials on a probiotic drink which contains the powerful new ingredient.

The development comes as a leading British expert has warned that the UK is facing one of the worst winter flu tolls for years, with up to 4,000 deaths.

If the research proves effective, a huge number of lives could be saved by people protecting themselves with a probiotic drink, similar to those drunk daily to boost good bacteria in the gut.

flu, flu cures, flu jabs, winter flu, summer flu, dieting to beat flu, easy flu cures, flu symptoms, turnip beats flu, japanese turnips, super foods,

 

Japanese turnips are hailed as the new health wonder [ALAMY]

Japanese scientists discovered that the bacteria Lactobacillus brevis found in a pickled turnip called suguki protected mice exposed to flu.

The bacteria increased the production of immune system moleclues, including flu-specific antibodies. In mice, the effects were powerful enough to prevent infection by the highly contagious H1N1 swine flu. Scientists at research company Kagome believe there could also be protection against the deadly H7N9 strain which has recently emerged in China.

While suguki fans hail its protective powers, it is unknown why the bacteria protects against flu, but it was found to be extremely tolerant to acidic stomach juices.

It is not known whether the same effects will be seen in humans but scientists are hopeful they have found the next superfood.

flu, flu cures, flu jabs, winter flu, summer flu, dieting to beat flu, easy flu cures, flu symptoms, turnip beats flu, japanese turnips, super foods,

Scientists warn this year could be the worst for flu deaths [GETTY: Pic posed by model]

Study author Naoko Waki, writing in science journal Letters of Applied Microbiology, said: “When a particular strain of Lactobacillus brevis is eaten by mice, it has protective effects against influenza.”

The research concluded: “Continual intake of (the drink) for 14 days prior to influenza virus infection alleviated symptoms such as loss of body weight and deterioration in observational physical conditions induced by the infection.”

Ms Waki said further studies are needed to confirm initial findings. Human trials are now under way.

Last month, virologist Professor John Oxford, of Queen Mary University in London, said Britain had “got away with it” recently and predicted this would be the worst winter for flu deaths for years.

He warned doctors and other health staff to improve the shockingly low levels of flu jabs among their ranks, which greatly increases the chances of flu spreading.

Many Britons have limited immunity after several years of relatively low-level outbreaks.

Analysts at Datamonitor Healthcare are forecasting that 10.5 million people will get flu this winter. The virus is most deadly to the elderly and very young.

 

http://www.express.co.uk/news/health/441374/New-wonder-cure-for-killer-flu-originates-from-the-humble-turnip

Video – Health Research Reports 9 SEP 2013

Topics:
Arginine performs as well as established drugs for Diabetes
* American Scientific journal Enocrinology Sep 2013
Nutritional Supplements reduce hospital stays by 21%
* American Journal of Managed Care Sep 2013
Sirtuin in the brain delays the process of aging
* Cell Metabolism Sep 2013
H1N2 influenza vaccine disables the bodies defense against H1N1 Swine flu
* Science Translational Medicine Aug 2013

Flu vaccine backfires in pigs / vaccinated against H1N2 influenza were more vulnerable to the rarer H1N1 strain

Antibodies against one strain increase risk of infection with another.

28 August 2013
Pigs vaccinated against H1N2 influenza were more vulnerable to the rarer H1N1 strain.

Andy Rouse/Photoshot

Preventing seasonal sniffles may be more complicated than researchers suspected. A vaccine that protects piglets from one common influenza virus also makes them more vulnerable to a rarer flu strain, researchers report today in Science Translational Medicine1.

The team gave piglets a vaccine against H1N2 influenza. The animals responded by making antibodies that blocked that virus — but aided infection with the swine flu H1N1, which caused a pandemic among humans in 2009. In the study, H1N1 infected more cells and caused more severe pneumonia in vaccinated piglets than unvaccinated ones.

The root of the different immune responses lies with the mushroom-shaped haemagglutinin protein found on the outside of influenza-virus particles, which helps them to attach onto cells in the airways. The protein occurs in all types of flu, but the make-up of its cap and stem vary between strains.

In the study, a vaccine for H1N2 spurred pigs to produce antibodies that bound the cap and the stem of that virus’s haemagglutinin. But some of those antibodies also targeted the stem of H1N1’s haemagglutinin protein, helping that virus fuse to cell membranes. That made H1N1 more efficient at infecting pigs and causing disease.

Stem vaccines

The finding may give some vaccine developers pause. Much of the work to develop a universal flu vaccine has targeted the stems of haemagglutinin proteins, because they are relatively consistent across many types of influenza viruses.

The new study suggests that such vaccines could also produce antibodies that enhance the ability of some viruses to infect new hosts, says James Crowe, an immunologist at Vanderbilt University in Nashville, Tennessee. But that does not mean that researchers should stop developing novel flu vaccines, including those that target haemagglutinin stems, he adds. “We should be very careful.”

Gary Nabel, a flu-vaccine researcher and chief scientific officer at the biotechnology firm Sanofi in Cambridge, Massachusetts, agrees. “It raises a warning flag, but at the same time it provides a tool to manage that risk,” he says of the new study’s results and methods.

Still, researchers have not yet tested whether human influenza vaccines can produce the same effect. And differences between pigs and humans make it difficult to interpret how relevant the findings are to the development of human vaccines, says Sarah Gilbert, a vaccine researcher at the University of Oxford, UK.

Lead author Hana Golding, a microbiologist at the US Food and Drug Administration in Bethesda, Maryland, agrees — and stresses that seasonal vaccines are still safe and effective. “This has no relevance to the regular vaccinations,” she says. “We think that people should definitely take them.”

Journal name:
Nature
DOI:
doi:10.1038/nature.2013.13621

References

  1. Khurana, S. et al. Sci. Transl. Med. 5, 200ra114 (2013).

 

http://www.nature.com/news/flu-vaccine-backfires-in-pigs-1.13621

Saliva proteins may protect older people from influenza

Contact: Michael Bernstein m_bernstein@acs.org 202-872-6042 American Chemical Society

Spit. Drool. Dribble. Saliva is not normally a topic of polite conversation, but it may be the key to explaining the age and sex bias exhibited by influenza and other diseases, according to a new study. Published in ACS’ Journal of Proteome Research, it provides new insights into why older people were better able to fight off the new strains of “bird” flu and “swine” flu than younger people.

Zheng Li and colleagues explain that saliva does more than start the process of digesting certain foods. Saliva also contains germ-fighting proteins that are a first-line defense against infections. Scientists already knew that levels of certain glycoproteins — proteins with a sugar coating that combat disease-causing microbes — differ with age. Li’s team took a closer look at how those differences affected vulnerability to influenza.

Their tests of 180 saliva samples from men and women of various ages suggested that seniors, who fought off the bird flu better than the younger groups, might thank their saliva. Glycoproteins in saliva of people age 65 and over were more efficient in binding to influenza than those in children and young adults. The research “may provide useful information to help understand some age-related diseases and physiological phenomenon specific to women or men, and inspire new ideas for prevention and diagnosis of the diseases by considering the individual conditions based primarily on the salivary analysis,” the scientists state.

###

The authors acknowledge funding from the National Science and Technology Major Project and the Foundation of Shaanxi Educational Committee.

The American Chemical Society is a nonprofit organization chartered by the U.S. Congress. With more than 163,000 members, ACS is the world’s largest scientific society and a global leader in providing access to chemistry-related research through its multiple databases, peer-reviewed journals and scientific conferences. Its main offices are in Washington, D.C., and Columbus, Ohio.

To automatically receive news releases from the American Chemical Society, contact newsroom@acs.org.

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Expert questions US public health agency advice on influenza vaccines: “All influenza is “flu,” but only one in six “flus” might be influenza”

Contact: Emma Dickinson edickinson@bmj.com 44-020-738-36529 BMJ-British Medical Journal

Marketing influenza vaccines involves marketing influenza as a threat of great proportions, argues Johns Hopkins fellow

Promotion of influenza vaccines is one of the most visible and aggressive public health policies today, writes Doshi. Today around 135 million doses of influenza vaccine annually enter the US market, with vaccinations administered in drug stores, supermarkets – even some drive-throughs.

This enormous growth has not been fuelled by popular demand but instead by a public health campaign that delivers a straightforward message: influenza is a serious disease, we are all at risk of complications from influenza, the flu shot is virtually risk free, and vaccination saves lives.

Yet, Doshi argues that the vaccine might be less beneficial and less safe than has been claimed, and the threat of influenza appears overstated.

To support its case, the CDC cites two studies of influenza vaccines, published in high-impact, peer-reviewed journals and carried out by academic and government researchers with non-commercial funding. Both found a large (up to 48%) relative reduction in the risk of death.

“If true, these statistics indicate that influenza vaccines can save more lives than any other single licensed medicine on the planet,” says Doshi. But he argues that these studies are “simply implausible” and likely the product of the ‘healthy-user effect’ (in this case, a propensity for healthier people to be more likely to get vaccinated than less healthy people).

In addition, he says, there is virtually no evidence that influenza vaccines reduce elderly deaths – the very reason the policy was originally created.

He points out that the agency itself acknowledges the evidence may be undermined by bias. Yet, he says “for most people, and possibly most doctors, officials need only claim that vaccines save lives, and it is assumed there must be solid research behind it.”

He also questions the CDC’s recommendation that beyond those for whom the vaccine is contraindicated, influenza vaccine can only do good, pointing to serious reactions to influenza vaccines in Australia (febrile convulsions in young children) and Sweden and Finland (a spike in cases of narcolepsy among adolescents).

Doshi suggests that influenza is yet one more case of “disease mongering” – medicalising ordinary life to expand markets for new products. But, he warns that unlike most stories of selling sickness, “here the salesmen are public health officials, worried little about which brand of vaccine you get so long as they can convince you to take influenza seriously.”

But perhaps the cleverest aspect of the influenza marketing strategy surrounds the claim that “flu” and “influenza” are the same, he concludes. “All influenza is “flu,” but only one in six “flus” might be influenza. It’s no wonder so many people feel that “flu shots” don’t work: for most flus, they can’t.”

Earlier this year, the BMJ launched a ‘Too Much Medicine’ campaign to help tackle the threat to health and the waste of money caused by unnecessary care. The journal will also partner at an international conference Preventing Overdiagnosis to be held in September in the USA

Paradox of Vaccination: Is Vaccination Really Effective against Avian Flu Epidemics?

Abstract

Background

Although vaccination can be a useful tool for control of avian influenza epidemics, it might engender emergence of a vaccine-resistant strain. Field and experimental studies show that some avian influenza strains acquire resistance ability against vaccination. We investigated, in the context of the emergence of a vaccine-resistant strain, whether a vaccination program can prevent the spread of infectious disease. We also investigated how losses from immunization by vaccination imposed by the resistant strain affect the spread of the disease.

Methods and Findings

We designed and analyzed a deterministic compartment model illustrating transmission of vaccine-sensitive and vaccine-resistant strains during a vaccination program. We investigated how the loss of protection effectiveness impacts the program. Results show that a vaccination to prevent the spread of disease can instead spread the disease when the resistant strain is less virulent than the sensitive strain. If the loss is high, the program does not prevent the spread of the resistant strain despite a large prevalence rate of the program. The epidemic’s final size can be larger than that before the vaccination program. We propose how to use poor vaccines, which have a large loss, to maximize program effects and describe various program risks, which can be estimated using available epidemiological data.

Conclusions

We presented clear and simple concepts to elucidate vaccination program guidelines to avoid negative program effects. Using our theory, monitoring the virulence of the resistant strain and investigating the loss caused by the resistant strain better development of vaccination strategies is possible.

Citation: Iwami S, Suzuki T, Takeuchi Y (2009) Paradox of Vaccination: Is Vaccination Really Effective against Avian Flu Epidemics? PLoS ONE 4(3):          e4915.            doi:10.1371/journal.pone.0004915

Editor: Carl Kingsford, University of Maryland, United States of America

Received: November 12, 2008; Accepted: November 26, 2008; Published: March 18, 2009

Copyright: © 2009 Iwami et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: Research Fellowships of the Japan Society for the Promotion of Science for Young Scientists. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

* E-mail: takeuchi@sys.eng.shizuoka.ac.jp

Introduction

Highly pathogenic H5N1 influenza A viruses have spread relentlessly across the globe since 2003. They are associated with widespread death of poultry, substantial economic loss to farmers, and reported infections of more than 300 people with a mortality rate of 60% [1]. Influenza prevention and containment strategies can be considered under the broad categories of antiviral, vaccine, and non-pharmaceutical measures [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13]. A major public health concern is the next influenza pandemic; yet it remains unclear how to control such a crisis.

Vaccination of domestic poultry against the H5N1 subtype of avian influenza has been used in several countries such as Pakistan, Hong Kong, Indonesia, China, and Vietnam [14], [15], [16]. Using vaccination to reduce the transmission rate might provide an alternative to mass culling, by reducing both the susceptibility of healthy birds and the infectiousness of infected birds [14], [17], [18]. However, incomplete protection at the bird level can cause the silent spread of the virus within and among birds [11]. Furthermore, vaccines might provide immunological pressure on the circulating strains, which might engender the emergence of drifted or shifted variants with enhanced potential for pathogenicity in humans [1]. Therefore, although vaccination programs have been recommended recently, some field evidence indicates that vaccination alone will not achieve eradication. Moreover, if not used appropriately, vaccination might result in the infection becoming endemic [11], [17].

An important issue related to influenza epidemics is the potential for the emergence of vaccine-resistant influenza viruses. The vaccine-resistant strain, in general, causes a loss of the protection effectiveness of vaccination [19], [20], [21], [22] (there is experimental evidence of the loss of the protection effectiveness for antiviral-resistant strains [23]). Consequently, a vaccination program that engenders the emergence of the resistant strain might promote the spread of the resistant strain and undermine the control of the infectious disease, even if the vaccination protects against the transmission of a vaccine-sensitive strain [20], [21], [22].

For example, in China, despite a compulsory program for the vaccination of all poultry commencing in September 2005, the H5N1 influenza virus has caused outbreaks in poultry in 12 provinces from October 2005 to August 2006 [14], [15], [22]. Genetic analysis revealed that an H5N1 influenza variant (Fujian-like, FJ like), which is a previously uncharacterized H5N1 virus sublineage, had emerged and subsequently became the prevalent variant in each of the provinces, replacing those previously established multiple sublineages in different regions of southern China. Some data suggest that the poultry vaccine currently used in China might only generate very low neutralizing antibodies to FJ-like viruses (seroconversion rates remain low and vaccinated birds are poorly immunized against FJ-like viruses) in comparison to other previously cocirculating H5N1 sublineages [20], [22]. That evidence implies the possibility that the emergence and replacement of FJ-like virus was preceded by and facilitated by the vaccination program, although the mechanism remains unknown epidemiologically and virologically (some researchers consider that the emergence and replacement of FJ-like virus are questionable [24], [25]).

Furthermore, the H5N2 vaccines have been used in Mexico since 1995 [17], [19], [21]. Phylogenetic analysis suggests the presence of (previously uncharacterized) multiple sublineages of Mexican lineage isolates which emerged after the introduction of the vaccine. Vaccine protection studies further confirmed in vitro serologic results indicating that commercial vaccine was not able to prevent virus shedding when chickens were challenged with the multiple sublineage isolates [19], [21]. Therefore, the vaccine protective efficacy would be impaired and the use of this specific vaccine would eventually become obsolete. That fact also implies that the vaccine promotes the selection of mutation in the circulating virus.

The emergence of a vaccine-resistant strain presents the risk of generating a new pandemic virus that is dangerous for humans through an avian-human link because of the spread of vaccine-resistant strain. The dynamics of competition between vaccine-sensitive and vaccine-resistant strains is, in general, complex [8], [9]. Actually, outcomes of the dynamics might be influenced by several factors, including a loss of protection effectiveness, a competitive advantage of vaccine-resistant strain, and a prevalence rate of vaccination. Understanding the dynamics of a spread of vaccine-resistant is therefore crucial for implementation of effective mitigation strategies.

Several theoretical studies have investigated the impact of an emergence of a resistant strain of antiviral drug such as M2 inhibitors and NA inhibitors during an influenza pandemic among humans [2], [3], [8], [9], [10], [12], [26]. However, to our knowledge, no study has used a mathematical model to investigate the application of vaccination program among poultry in the context of an emergence of a vaccine-resistant strain. It remains unclear whether a vaccination program can prevent the spread of infectious disease when the vaccine-resistant strain emerges and how a loss of immunization by vaccination within birds infected with the vaccine-resistant strain affects the spread of infectious disease among birds. Nobody can give a simple and clear explanation to capture the problems described above in a theoretical framework (using numerical simulations, many qualitative and quantitative but sometimes very complex studies have investigated effects of antiviral drugs [3], [8], [9], [10], [12], [26]). Furthermore, we remain skeptical that a vaccination program can reduce the number of total infectious individuals even if the vaccination protects against transmission of a vaccine-sensitive strain. We developed a simple mathematical model to evaluate the effectiveness, as a strategy to control influenza epidemic, of a vaccination program among poultry which can engender the emergence of a vaccine-resistant strain.

Methods

Herein, we describe a homogeneous population model of infectious disease and its control using a vaccination program in the presence of a vaccine-resistant strain (Fig. 1).

thumbnail

Figure 1. Model structure for the emergence of vaccine-resistant strain during a vaccination program: Susceptible birds (X) become infected with vaccine-sensitive (Y) and vaccine-resistant (Z) strains at rates in direct relation to the number of respective infectious birds.

We assume that vaccinated birds (V) can be protected completely from the vaccine-sensitive strain, but are partially protected from vaccine-resistant strains with a loss of protection effectiveness of the vaccination (σ). See the Mathematical model section for corresponding equations.

doi:10.1371/journal.pone.0004915.g001

All birds in the effective population are divided into several compartments, respectively including susceptible birds (X), vaccinated birds (V), birds infected with vaccine-sensitive strain (Y), and birds infected with vaccine-resistant strain (Z). We assume that susceptible birds are born or restocked at a rate of c per day and that all birds are naturally dead or removed from the effective population at a rate of b per day.

In the absence of vaccination, transmission occurs at a rate that is directly related to the number of infectious birds, with respective transmission rate constants ω and φ from infected birds with the vaccine-sensitive strain and with the vaccine-resistant strain. The infectiousness of vaccine-sensitive and vaccine-resistant strain are assumed to be exponentially distributed, respectively, with mean durations of 1/(b+my) and 1/(b+mz) days. Actually, my and mz respectively signify virulence of vaccine-sensitive and vaccine-resistant strains.

At the beginning of the vaccination program, X moves directly to V by the vaccination. However, after some period after the initial vaccination, the direct movement might vanish because almost all birds are vaccinated. Therefore, we can assume that vaccination is only administered to the newly hatched birds. The newly hatched birds are vaccinated at the rate 0≤p≤1 (more appropriately, p is proportional). Actually, p represents the prevalence rate of the vaccination program.

To simplify the theoretical treatment, as described in [11], we assume that the vaccinated birds can be protected completely from the vaccine-sensitive strain (note that the assumption is not necessary for our results: see Supplementary Information: Text S1, Fig. S10, S11). Actually, in laboratory experience, many avian influenza vaccines confer a very high level of protection against clinical signs and mortality (90–100% protected birds) [21]. However, many factors determine whether a vaccinated bird becomes infected, including age, species, challenge dose, health, antibody titre, infections of immunosuppressive diseases, and cross-reactivity of other avian influenza serotypes [11], [27], [28], [29]. On the other hand, we assume that the vaccinated birds are partially protected from the vaccine-resistant strain at the rate (proportion) 0≤1−σ≤1 because of cross-reactivity of immune systems [19], [20], [22], [23], [29] (e.g., σ = 0 represents complete cross immunity against vaccine-resistant strains). Actually, σ represents a loss of protection effectiveness of the vaccination caused by a vaccine-resistant strain.

Mathematical model

We extended the standard susceptible–infective model [30] including the effect of a vaccination program that can engender the emergence of a vaccine-resistant strain. Our mathematical model is given by the following equations: (1) Model (1) is a simplified one that is used in [31]. We considered a mechanism for the emergence and replacement of the FJ-like virus over a large geographical region in China using a more complex patch-structured model in the heterogeneous area [31]. Here we investigate the impact of the vaccination program in a homogeneous area and specifically examine the role of epidemiological parameters such as the prevalence rate of the vaccination program (p) and the loss of protection effectiveness of the vaccination (σ) in the spread of the disease.

Estimation of epidemiological parameters

Baseline values of model parameters and their respective ranges used for simulations are presented in Table 1 and 2. These parameters are based on avian influenza epidemics among poultry in The Netherlands in 2003 [32], [33], [34].

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Table 1. Description of physical characteristics, transmission, infectious, and vaccination parameters of the model with their baseline values and ranges used for simulations.

doi:10.1371/journal.pone.0004915.t001

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Table 2. Basic reproductive numbers and invasion reproductive numbers before the vaccination program.

doi:10.1371/journal.pone.0004915.t002

The initial population size was c/b = 984 birds at the 2003 epidemic [34]. Usually, the mean lifespan of poultry is about 2 years. However, we assume that the mean duration of a bird being in effective population is about 1/b = 100 days because of migration and marketing. Therefore, the birth or restocking rate of birds is c = 9.84 birds per day. Estimated infectious period and transmission parameters are 1/(b+my) = 13.8 days and ω = 4.78×10−4 day−1 individual−1, respectively, [34]. These physical characteristics, in addition to infectious and transmission parameters, are used in our model as parameters of the vaccine-sensitive strain.

The epidemiological and biological feature of antiviral drug-resistance is well reported in [23]. The transmissibility and virulence of drug-resistant strains are usually lower than those of the wild strain because of its mutation cost [8], [10], [23], [35]. Actually, antiviral drugs are also used for prophylaxis drug intervention as vaccination [8], [10], [12]. Herein, we use some reduced value of transmissibility (φ/ω = 0.58) and the increased value of infectious period of the vaccine-sensitive strain ((b+my)/(b+mz) = 1.32) for parameters of vaccine-resistant strain (sensitivity analyses are given in Supplementary Information: Text S1, Fig. S6, S7, S8, S9).

Reproductive numbers

A measure of transmissibility and of the stringency of control policies necessary to stop an epidemic is the basic reproductive number, which is the number of secondary cases produced by each primary case [30]. We obtain basic reproductive quantities of vaccine-sensitive strain and vaccine-resistant strain before vaccination program (superscript n means no vaccination). In fact, during the vaccination program, the basic reproductive numbers depend on the rate of prevalence of the vaccination program. We derived these basic reproductive numbers depending on the prevalence rate in Supplementary Information: Text S1. With the estimated parameters in Table 1 the basic reproductive number of vaccine-sensitive and vaccine-resistant strain are and , respectively (note that corresponds to an estimated value in [34]).

Furthermore, to clarify the concept of competition among strains simply, we introduce the invasion reproductive number for the vaccine-resistant strain before the vaccination program , which signifies an expected number of new infectious cases with the vaccine-resistant strain after a spread of a vaccine-sensitive strain among birds. The invasion reproductive number is considered as a competitive condition (relative fitness), which represents some advantage measure of the vaccine-resistant strain against the vaccine-sensitive strain. The estimated invasion reproductive number of the vaccine-resistant strain is . During the vaccination program, the invasion reproductive number also depends on the prevalence rate of the vaccination program (see Supplementary Information: Text S1).

Results

We consider a scenario in which a vaccine-resistant strain can emerge (i.e., be eventually selected) during a vaccination program designed to be effective against the spread of a vaccine-sensitive strain. This implies that : otherwise the vaccine-resistant strain can not emerge at all (see Supplementary Information: Text S1, Fig. S1, S2, S3). Acquisition of resistance ability usually engenders a strain which, in the absence of a pharmaceutical intervention, is less fit than the sensitive strain [8], [9], [12], [35]. Therefore, . We generally assume the following conditions for reproductive numbers before the vaccination program (our baseline parameter values are satisfied with these assumptions):

The assumption precludes the possibility that a pre-existing vaccine-resistant strain beats the vaccine-sensitive strain before the vaccination program because .

Evaluation of the effect of a vaccination program

Although vaccination is an important tool to control epidemics, the use of vaccination might engender a spread of a vaccine-resistant strain. To demonstrate the interplay between these opposing effects, we simulated our model to determine the final size of an epidemic (total infected individuals Y+Z at equilibrium level) over vaccination prevalence (0≤p≤1) in Fig. 2 (we use our baseline parameter values except for mz). We assume that the loss of the protection effectiveness is 35% (σ = 0.35: this value can be chosen arbitrarily with little effect on the meaning of the results). The estimated infectious period of the vaccine-sensitive strain is 13.8 days [34] (see Table 1). Therefore, the virulence of vaccine-sensitive strain is my = 0.062 day−1. Results show that the patterns of the final size can be divided into two cases, which depend strongly on the virulence of the vaccine-resistant strain. If the virulence of the vaccine-resistant strain is lower than that of vaccine-sensitive strain (e.g., we choose mz = 0.045), then increasing the prevalence rate of vaccination from 13.5% to 30.3% can increase the final size (green line at top figure in Fig. 2). On the other hand, if the virulence is higher (mz = 0.065), increasing the prevalence always decreases the final size (bottom figure in Fig. 2). These two patterns are qualitatively preserved for different virulence of the vaccine-resistant strain.

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Figure 2. Final size of epidemics related with the prevalence rate of the vaccination: The top figure represents that the vaccination is not always effective in the case of lower virulence of vaccine-resistant strain.

The bottom figure represents that the vaccination is always effective in the case of higher virulence of the vaccine-resistant strain. We assume that σ = 0.35, mz = 0.045 (top) and mz = 0.065 (bottom). These values of σ and mz are not so influential on the result. The blue, green, and red lines respectively signify situations in which only the vaccine-sensitive strain exists, both the vaccine-sensitive and the vaccine-resistant strains exist, and only the vaccine-resistant strain exists.

doi:10.1371/journal.pone.0004915.g002

In [8], [9], although they consider the emergence of an antiviral drug-resistant virus, a similar tendency (increasing the treatment level increases the final size of the epidemic) was obtained through complex models that are difficult to treat mathematically. The mathematical model presented herein demonstrates that the patterns of final size over vaccination prevalence only depend on the virulence of the vaccine-resistant strain as follows (see Supplementary Information: Text S1). Increasing the prevalence rate increases the final size when only both strains co-exist if the virulence of vaccine-resistant strain is lower than that of vaccine-sensitive strain (my>mz). That is to say, the vaccination is effective when either a vaccine-sensitive or a vaccine-resistant strain exists. On the other hand, if the virulence of vaccine-resistant strain is higher than that of vaccine-sensitive strain (my<mz), the final size always decreases as the prevalence rate increases. The other parameters can not change these patterns. In fact, many studies have ignored the impact of the virulence of the vaccine-resistant strain. In [7], we also found that the virulence of mutant strain determines a choice of the optimal prevention policy for avian influenza epidemic. Therefore, we suggest that, to monitor and investigate the virulence evolution between the vaccine-sensitive and vaccine-resistant strain is important to develop avian flu epidemic plans. In fact, if the vaccine-resistant strain has higher virulence than the vaccine-sensitive strain, the vaccination program is always effective, even though the program engenders the emergence of a vaccine-resistant strain. On the other hand, if the vaccine-resistant strain has lower virulence, we must carefully manage vaccination to prevent the spread of a vaccine-resistant strain.

Impact of loss of protection effectiveness of vaccination

To ensure an effective vaccination program, the vaccine must protect vaccinated animals against clinical signs of the disease and prevent mortality [21]. However, the vaccine-resistant strain causes a loss of the protection effectiveness of the vaccination [19], [20], [21], [22], [37]. We investigate an impact of the loss of the protection on change of final size of the epidemic over the vaccination prevalence. Assume, hereafter, that the virulence of vaccine-resistant strain is lower than that of vaccine-sensitive strain (my>mz): otherwise, the vaccination is always effective (our baseline parameter values are satisfied with my>mz). Actually, a resistant strain seems to have reduced virulence in general [8], [10], [23], [35].

We conduct a simulation using our model to elucidate the change of the final size with the loss of the protection effectiveness 5%, 15%, and 80% over vaccination prevalence in Fig. 3. Results showed that the patterns of the change are divisible into three cases. In theory, we can estimate the threshold values of the loss of the protection which determines the patterns (see Supplementary Information: Text S1, Fig. S4):

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Figure 3. Impact of the loss of the protection effectiveness of the vaccination on the change of the final size of the epidemic: The losses of the protection in the top, middle, and bottom figure are σ = 0.05, 0.15, and 0.8, respectively.

The top (0≤σσ*) and middle () figures portray the possibility of eradication of the infectious disease through the vaccination program. However, in the bottom figure (), the vaccination engenders a failure to prevent the spread of the disease. The patterns of the change are divisible into these three cases, depending on the loss of the protection. The blue, green, and red lines respectively correspond to the situation in which only the vaccine-sensitive strain exists, both the vaccine-sensitive and the vaccine-resistant strains exist, and only the vaccine-resistant strain exists.

doi:10.1371/journal.pone.0004915.g003

In fact, σ* = 0.056 and in our simulation from Table 1. When the loss of the protection is between 0% and σ* = 5.6% (5%: the top figure in Fig. 3), the vaccination can control the epidemic with the prevalence rate of 84.7% without the emergence of a resistant strain (a vaccine-resistant strain never emerges in the population). Therefore, increasing the prevalence rate of vaccination always decreases the final size of the epidemic. For the loss of the protection is between σ* = 5.6% and (15%: the middle figure in Fig. 3), the vaccination eventually prevents the spread of the disease with 94.1% of vaccination prevalence in spite of the emergence of the resistant strain. Increasing the prevalence rate from 31.5% to 44.1% increases the final size. Therefore, the vaccination is not always effective. However, when the loss of the protection is between and 100% (80%: the bottom figure in Fig. 3), the vaccination no longer controls the disease (even if the prevalence rate is 100%) and the vaccine-resistant strain spreads widely through the population instead of the vaccine-sensitive strain. In this case, the vaccination only slightly provides beneficial effects for preventing the spread of the disease. Therefore, the loss of the protection effectiveness of vaccination plays an important role in preventing the spread of the disease.

Vaccination can facilitate spread of disease

Sometimes a considerable spread of the resistant strain partially compromises the benefits of a vaccination program [19], [20], [22], [37]. For example, even if we can completely execute the vaccination program (p = 1), the final size of the epidemic can become larger than that before the vaccination program (p = 0) by the emergence of vaccine-resistant strain (bottom figure in Fig. 3). This implies that the vaccination, which is expected to prevent the spread of the disease, can instead help the spread of the disease. If the loss of the protection effectiveness of vaccination is high (σ*σ≤1), the vaccination might increase the final size over vaccination prevalence compared with that before the vaccination program (vaccination always decreases the final size if 0≤σσ* (top figure in Fig. 3)). Here we can also calculate such a risk of help, which depends on the loss of the protection (see Supplementary Information: Text S1). Let

Actually, σc = 0.236 in our simulation is from Table 1. When the loss of the protection is between 23.6% and 100%, we found that the vaccination program is attended by the risk that the final size becomes larger than that before the vaccination program (see Supplementary Information: Text S1).

Difficulty of prediction of a prevalent strain

Vaccination is well known to engender “silent carriers or excretors” if the vaccine can not completely protect the vaccinated animals against clinical signs of the disease [16], [21]. The existence of silent carriers or excretors is dangerous because they become a virus reservoir and shed the virus into their environment, causing potential outbreaks among their own and other species. Furthermore, even if a vaccination is effective in a bird (individual level), an incomplete vaccination program for all birds (population level) can engender the “silent spread” of an infectious disease [1], [11]. Additionally, we found that it is difficult for us to predict a prevalent strain even if we can completely estimate the basic reproductive number of vaccine-sensitive and vaccine-resistant strains during the vaccination program (although estimations, usually, are almost impossible). Even when the basic reproductive number of the vaccine-resistant strain is less than that of the vaccine-sensitive strain (), the vaccine-resistant strain can beat the vaccine-sensitive strain and spread widely through the population (see Supplementary Information: Text S1, Fig. S5). Therefore, a non-ideal vaccination program might make a prediction of prevalent strain difficult.

Optimal prevalence rate of vaccination program

In the absence of a vaccine-resistant strain, a goal of vaccination program is to reduce the basic reproductive number of vaccine-sensitive strain to be less than 1. We assume that . Therefore, the vaccination can eradicate the vaccine-sensitive strain if at least 84.7% of the birds in poultry are vaccinated effectively based on the fraction of [30]. However, in the presence of the resistant strain, the simple theory is inapplicable to an optimal prevalence rate of vaccination program. Here we define the optimal prevalence rate of a vaccination program which minimizes both the final size of the epidemic and the prevalence rate (see Supplementary Information: Text S1).

We calculate the optimal prevalence rate, which depends on the loss of the protection effectiveness of the vaccination in Fig. 4 (sensitivity analyses are given in Supplementary Information: Text S1, Fig. S6). At the point where the loss of the protection effectiveness is greater than some threshold value σo, the optimal prevalence rate changes catastrophically from high prevalence rate to a low prevalence rate. Here

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Figure 4. Optimal prevalence rate of vaccination program: Increasing of the loss of the protection effectiveness engenders a catastrophic change in the optimal prevalence rate.

The optimal rate increases as the loss increases if the loss of the protection effectiveness is small (0≤σσo). This implies that a small loss of the protection effectiveness can be compensated by a high optimal prevalence rate of the vaccination program. On the other hand, if the loss is large (σoσ≤1), the optimal rate decreases as the loss of the protection effectiveness increases. This eventuality implies that a large loss of the protection effectiveness is no longer compensated by the high optimal prevalence rate of the vaccination program. Therefore, a low prevalence rate, which does not engender the emergence of a vaccine-resistant strain becomes optimal because the poor vaccine engenders the increase of final size of the epidemic because of the spread of the resistant strain.

doi:10.1371/journal.pone.0004915.g004

Actually, σo = 0.461 in our simulation from Table 1. The optimal prevalence rate is 84.6% when the loss of the protection effectiveness is between 0% and 5.6%. In addition, if the loss rate is between 5.6% and 20.1%, then the optimal prevalence rate increases from 84.6% to 100%. Furthermore, if the loss rate is between 20.1% and 46.1%, then the optimal prevalence rate must always be 100%. Consequently, as long as the loss of the protection effectiveness is small (0%–46.1%), the loss can be compensated by a high optimal prevalence rate of the vaccination program. However, if the loss rate is greater than 46.1%, the loss is no longer compensated by the high prevalence rate of the vaccination program. The optimal prevalence rate changes catastrophically from 100% to 10.2%. Afterward, as the loss rate increases from 46.1% to 100%, the optimal prevalence rate decreases from 10.2% to 4.72% (the low prevalence rate becomes optimal). This is true because the poor vaccine (with a large loss of the protection) engenders the emergence of the vaccine-resistant strain for the high prevalence rate; in addition, the spread of the resistant strain increases the final size of the epidemic. Therefore, the loss of the protection effectiveness strongly impacts also on the optimal prevalence rate.

Variation of final size of epidemic according to the vaccination program

In countries where poultry are mainly backyard scavengers, optimum vaccination coverage might be difficult to achieve [21]. The final size of the epidemic might be increased and the program might fail if the optimal prevalence rate of the vaccination program can not be achieved. However, if we can achieve optimum vaccination coverage, the final size is greatly reduced. The final size of the epidemics can be variable depending on the prevalence rate. Here we calculate the optimal (smallest) and worst (largest) final size of the epidemic over the vaccination prevalence (see Supplementary Information: Text S1) in Fig. 5 (black and yellow bars respectively represent the optimal and worst final size). The variation of the final size is between black and yellow bars shown in Fig. 5 (sensitivity analyses are given in Supplementary Information: Text S1, Fig. S7).

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Figure 5. Variation of the final size of the epidemic over the vaccination prevalence: The black bar represents the optimal (smallest) final size of the epidemic.

The yellow bar represents the worst (largest) final size of the epidemic over the vaccination prevalence. The variation of the final size depending on the prevalence rate is between black and yellow bars. If the loss of protection effectiveness is small, then the variation is very large. On the other hand, if the loss becomes large, then the variation decreases. Therefore, the final size of the epidemic is strongly affected by the vaccination coverage and the loss of protection effectiveness: a bad vaccination program (far from the optimal prevalence rate) increases the final size and prevents eradication of the disease.

doi:10.1371/journal.pone.0004915.g005

If the loss of protection effectiveness is small, then the variation is very large. The vaccination program can eradicate the disease or reduce the final size of the epidemic to a very small size if we can execute the vaccination program near the optimal prevalence rate. The variation is sensitive for the prevalence rate. Therefore, we must carefully manage the vaccination program to control the disease when the loss is small. However, as the loss of protection effectiveness increases, the variation decreases. In particular, when the loss is medium, the reduction of the variation is remarkable. In addition, the reduction of the variation remains almost unchanged when the loss is large. This implies that the variation becomes insensitive if the loss is high. In this case, even if we can execute the vaccination program near the optimal prevalence rate, the effect of the program is not large. Therefore, although the final size is strongly affected by the vaccination coverage and a non-optimal vaccination program (far from the optimal prevalence rate) increases the final size, in general, good vaccine treatment with small loss of protection effectiveness has a great possibility for disease control. Demonstrably, poor vaccine application has little or no benefit.

Effects of non-pharmaceutical intervention

Avian influenza vaccination need not be used alone to eradicate the disease: additional non-pharmaceutical intervention is beneficial. Additional interventions must include culling infected animals, strict quarantine, movement controls and increased biosecurity, extensive surveillance [11], [16], [21], [34], [37]. We investigate the effects of some additional non-pharmaceutical intervention measures on the vaccination program. The effects are considered by changing model parameters (1).

In the European Union (EU), regulations for the control of avian influenza strains are imposed by EU council directive 92/40/EEC [34]. Virus output is reduced by the killing and removal of infected poultry flocks (culling). During the H7N7 epidemic in The Netherlands in 2003, this and other approaches were executed. To investigate the effectiveness of the control measures, A. Stegeman et al. quantified the transmission characteristics of the H7N7 strain before and after detection of the first outbreak of avian influenza in The Netherlands in 2003 [34]. In Table 1, we present the chosen epidemiological parameters, which are estimated on the H7N7 epidemic before notification of the circulation of the avian influenza (these parameters are not affected by the additional control measures). Here we choose other epidemiological parameters for vaccine-sensitive strain which are estimated by the H7N7 epidemic after the notification in [34] (these parameters are affected by the additional control measures) to evaluate an effect of the non-pharmaceutical intervention on the vaccination program. The estimate of the transmission parameter ω decreases considerably from 4.78×10−4 day−1 individual−1 to 1.70×10−4 day−1 individual−1 by the control measures. Furthermore, the estimate of the infectious period 1/(b+my) is also reduced from 13.8 days to 7.3 days. Therefore, control measures can reduce the basic reproductive number from 6.53 to 1.22 [34]. In addition, we assume, for example, that the relative transmissibility of vaccine-resistant strains is φ/ω = 0.7 and that the relative infectious period of vaccine-resistant strain is (b+my)/(b+mz) = 1.32 (these values are not strongly influential on our results).

We calculated the threshold values of the loss of protection effectiveness of the vaccination and present them in Table 3 when the vaccination program accompanies non-pharmaceutical intervention. Results show that the non-pharmaceutical intervention markedly reduces the risk of the emergence of the vaccine-resistant strain because σ* changes from 5.6% to 37.2%. In addition, the possibility that the vaccination program eventually eradicates the spread of the disease increases because changes from 20.1% to 88.6%. Furthermore, because σc changes from 23.6% to 100%, the vaccination program always decreases the final size of the epidemic compared with that before the vaccination program, even if the size increases when both strains co-exist. When the vaccination program accompanies non-pharmaceutical intervention, even if the loss of protection effectiveness is increased considerably by the vaccine-resistant strain, the loss can almost be compensated by the high optimal prevalence rate of the vaccination program: σo changes from 46.1% to 96.8%.

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Table 3. Threshold values of the loss of protection effectiveness of the vaccination.

doi:10.1371/journal.pone.0004915.t003

Figure 6 portrays the optimal prevalence rate of a vaccination program (top figure) and the optimal final size of the epidemic (bottom figure) with (pink curve and bar) or without (black curve and bar) the non-pharmaceutical intervention. The non-pharmaceutical intervention makes it easy to achieve an optimal prevalence rate and to prevent the spread of the disease. Moreover, catastrophic change does not occur until the loss of protection effectiveness becomes very high (top figure in Fig. 6). Furthermore, the optimal final size is also dramatically reduced by the additional intervention (bottom figure in Fig. 6). Even if vaccination without the additional intervention can not prevent the spread of the disease, the vaccination with the intervention can eradicate the disease (for example σ = 60%). Therefore, non-pharmaceutical intervention improves weak points of vaccination programs such as the difficult control of optimal vaccination coverage, the small applicability of the program with respect to the loss of protection effectiveness caused by the vaccine-resistant strain, and so on.

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Figure 6. Effects of non-pharmaceutical intervention: The top figure shows the optimal prevalence rate of the vaccination program with (pink curve) or without (black curve) non-pharmaceutical intervention.

The non-pharmaceutical intervention readily achieves the optimal prevalence rate and hinders the catastrophic change. The bottom figure shows the optimal final size of the epidemic with (pink bar) or without (black bar) the non-pharmaceutical intervention. The intervention also dramatically reduces the final size of the epidemic.

doi:10.1371/journal.pone.0004915.g006

Time-course of the spread of the disease

Finally, we investigate the time-course of spread of the disease according to vaccination and non-pharmaceutical interventions for 500 days in the presence of a vaccine-resistant strain. The results are presented in Fig. 7. We consider that the vaccination program and non-pharmaceutical interventions are executed after the vaccine-sensitive strain spreads and becomes endemic (around 200 days). Furthermore, the vaccine-resistant strain is assumed to occur in a few individuals after the start of the vaccination program (around 260 days). We assume that the prevalence rate of the vaccination program is p = 50%, the loss of protection effectiveness is σ = 80%; the other parameters are the same as those used in the descriptions above. These values of p and σ are not influential on our results (sensitivity analyses are shown in Supplementary Information: Text S1, Fig. S8, S9).

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Figure 7. Time-course of the spread of the disease with vaccination and non-pharmaceutical interventions: We calculate epidemic curves with a vaccination program for 500 days.

The vaccination program and non-pharmaceutical intervention are started after the vaccine-sensitive strain becomes endemic (around 200 days). We assume that the vaccine-resistant strain occurs after the start of vaccination (around 260 days). The top, middle, and bottom figures respectively depict time courses of infection without the vaccination program, with only the vaccination program, and with both the vaccination program and the non-pharmaceutical intervention. The blue and red curves respectively represent the number of infected individuals with vaccine-sensitive and vaccine-resistant strains. We assume that the prevalence rate of vaccination program is p = 0.5, the loss of protection effectiveness is σ = 0.8.

doi:10.1371/journal.pone.0004915.g007

The top figure in Fig. 7 depicts the epidemic curve without the vaccination program. It is apparent that the vaccine-sensitive strain (the blue curve) becomes endemic at around 200 days after a pandemic phase of the disease if we execute no intervention policy. The middle figure portrays the time-course of spread of the disease, assuming the vaccination program alone. A vaccine-resistant strain (the red curve) emerges and spreads widely through the population by replacing the vaccine-sensitive strain. It becomes endemic at around 450 days. This result shows the possibility that the emergence and replacement of the resistant strain can be facilitated by the vaccination program, as in some vaccination programs [19], [21], [22]. We can observe that it takes about several months for the resistant strain to beat the sensitive strain (see the middle figure in Fig. 7). Actually, the replacement time of the resistant strain was reported as several months in the China and Mexico epidemics [19], [21], [22]. The final size of the simulated epidemic is larger than that before (without) the vaccination program because the loss of protection effectiveness σ = 80% is greater than (see Fig. 3). In this case, the vaccination program negatively affects the control of infectious disease. The bottom figure presents the time-course of the spread of the disease with both the vaccination program and non-pharmaceutical interventions. The vaccine-sensitive strain is dramatically reduced and the vaccine-resistant strain hardly spreads in the population; therefore, both strains are eventually controlled at a low level by the interventions. Thus, non-pharmaceutical interventions can help the vaccination program and control the resistant strain to spread in the population.

Discussion

A serious problem of vaccination strategy is the emergence of vaccine-resistant strains [19], [20], [21], [22]. Even if a resistant strain emerges, a vaccination program must be managed to control the spread of the disease. In the absence of the resistant strain, our mathematical model certainly shows that a large prevalence of the vaccination program might markedly reduce an epidemic curve and the final size of the epidemic. Therefore, we can control infectious diseases as in previous models [30]. However, in the presence of the emergence of a vaccine-resistant strain, the vaccination program can not simply control the spread of the disease. The control of the infectious disease through vaccination becomes more difficult.

The paradoxical result obtained here is that if the virulence of vaccine-resistant strain is less than that of vaccine-sensitive strain, the final size of the epidemic might increase as the prevalence rate of the vaccination program increases (see Fig. 2). A vaccination that is expected to prevent the spread of the disease can instead foster the spread of the disease. Although qualitatively similar results were obtained through more complex models [8], [9], which can be treated analytically only to a slight degree, one of our important results is the clear and simple concept illustrating the value and pitfalls of vaccination programs; the concept can help farmers and administrators to avoid negative effects from paradoxical phenomena.

We investigated how the loss of protection effectiveness impacts a vaccination program’s results in the lower virulence case. If the loss of protection effectiveness is between 0 and , the vaccination program can eventually eradicate the disease, even if a vaccine-resistant strain emerges (see Fig. 3). In particular, if the loss is between 0 and σ*, the program prevents even the emergence of the resistant strain. However, when the loss is greater than , the program no longer prevents the wide spread of the resistant strain in spite of the large prevalence rate of the program. Furthermore, if the loss is between σc and 1, the program presents the risk that the final size will become larger than that without the vaccination program. Therefore, in the context of the emergence of the resistant strain, we must carefully execute the program to exercise a positive effect of the vaccine effectively. Additionally, we investigated the optimal prevalence rate of the vaccination program, its final size, and the worst-case final size (see Fig. 4, 5 and Supplementary Information: Text S1). The catastrophic change of the optimal prevalence rate and the variation of the final size depending on the loss of protection effectiveness were confirmed.

From our theoretical analysis, we propose that monitoring the virulence of the resistant strain and investigating the loss resulting from a resistant strain can have important consequences for developing a vaccination strategy. In particular, all thresholds derived herein are only constructed using basic reproductive numbers and transmissibilities that prevail before the vaccination program, which can be estimated using epidemiological data (it is usually almost impossible to estimate basic and invasion reproductive numbers during vaccination programs). Therefore, using our theory, we were able to calculate various risks in the vaccination program using the available data (Table 3) and propose how we might use a poor vaccine, which has a large loss of protection effectiveness, against the resistant strain to maximize the effects of the program (Fig. 4, 5, and 6). For the results reported here, we assumed that the vaccinated birds can perfectly protect the infection from the vaccine-sensitive strain. Although that assumption is not unreasonable [21], in Supplementary Information: Text S1, Fig. S10, S11, we present an investigation of the effect of the loss of protection effectiveness against the vaccine-sensitive strain. Qualitatively similar results were obtained using numerical simulations.

Vaccination is now being used extensively to aid the prevention of emergence or to control the spread of avian influenza [14]. However, if the vaccinations are not used appropriately, prevention and control will be negatively affected by the vaccination program [1], [11], [19], [21], [22]. Actually, when the vaccine-resistant strain emerges, our model predicts various risks in the program. Therefore, to eradicate the infectious disease effectively by vaccination, early detection of the resistant strain, monitoring of its virulence and loss of protection effectiveness of vaccination caused by the resistant strain, and attendance of non-pharmaceutical interventions, in addition to collaboration among farmers, industry, public health authorities, and the government are all required.

Supporting Information

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Text S1.

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Author Contributions

Analyzed the data: SI TS YT. Contributed reagents/materials/analysis tools: SI TS YT. Wrote the paper: SI.

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Flu shot does not reduce risk of death

2008 Study posted for filing

Contact: Keely Savoie
ksavoie@thoracic.org
212-315-8620
American Thoracic Society

The widely-held perception that the influenza vaccination reduces overall mortality risk in the elderly does not withstand careful scrutiny, according to researchers in Alberta. The vaccine does confer protection against specific strains of influenza, but its overall benefit appears to have been exaggerated by a number of observational studies that found a very large reduction in all-cause mortality among elderly patients who had been vaccinated.

The results will appear in the first issue for September of the American Journal of Respiratory and Critical Care Medicine, a publication of the American Thoracic Society.

The study included more than 700 matched elderly subjects, half of whom had taken the vaccine and half of whom had not. After controlling for a wealth of variables that were largely not considered or simply not available in previous studies that reported the mortality benefit, the researchers concluded that any such benefit “if present at all, was very small and statistically non-significant and may simply be a healthy-user artifact that they were unable to identify.”

“While such a reduction in all-cause mortality would have been impressive, these mortality benefits are likely implausible. Previous studies were likely measuring a benefit not directly attributable to the vaccine itself, but something specific to the individuals who were vaccinated—a healthy-user benefit or frailty bias,” said Dean T. Eurich,Ph.D. clinical epidemiologist and assistant professor at the School of Public Health at the University of Alberta. “Over the last two decades in the United Sates, even while vaccination rates among the elderly have increased from 15 to 65 percent, there has been no commensurate decrease in hospital admissions or all-cause mortality. Further, only about 10 percent of winter-time deaths in the United States are attributable to influenza, thus to suggest that the vaccine can reduce 50 percent of deaths from all causes is implausible in our opinion.”

Dr. Eurich and colleagues hypothesized that if the healthy-user effect was responsible for the mortality benefit associated with influenza vaccination seen in observational studies, there should also be a significant mortality benefit present during the “off-season”.

To determine whether the observed mortality benefits were actually an effect of the flu vaccine, therefore, they analyzed clinical data from records of all six hospitals in the Capital Health region in Alberta. In total, they analyzed data from 704 patients 65 years of age and older who were admitted to the hospital for community-acquired pneumonia during non-flu season, half of whom had been vaccinated, and half of whom had not. Each vaccinated patient was matched to a non-vaccinated patient with similar demographics, medical conditions, functional status, smoking status and current prescription medications.

In examining in-hospital mortality, they found that 12 percent of the patients died overall, with a median length of stay of approximately eight days. While analysis with a model similar to that employed by past observational studies indeed showed that patients who were vaccinated were about half as likely to die as unvaccinated patients, a finding consistent with other studies, they found a striking difference after adjusting for detailed clinical information, such as the need for an advanced directive, pneumococcal immunizations, socioeconomic status, as well as sex, smoking, functional status and severity of disease. Controlling for those variables reduced the relative risk of death to a statistically non-significant 19 percent.

Further analyses that included more than 3,400 patients from the same cohort did not significantly alter the relative risk. The researchers concluded that there was a difficult to capture healthy-user effect among vaccinated patients.

“The healthy-user effect is seen in what doctors often refer to as their ‘good’ patients— patients who are well-informed about their health, who exercise regularly, do not smoke or have quit, drink only in moderation, watch what they eat, come in regularly for health maintenance visits and disease screenings, take their medications exactly as prescribed— and quite religiously get vaccinated each year so as to stay healthy. Such attributes are almost impossible to capture in large scale studies using administrative databases,” said principal investigator Sumit Majumdar, M.D., M.P.H., associate professor in the Faculty of Medicine & Dentistry at the University of Alberta.

The finding has broad implications:

 

  • For patients: People with chronic diseases such as chronic respiratory diseases such as chronic obstructive pulmonary disease, immuno-compromised patients, healthcare workers, family members or friends who take care of elderly patients and others with greater exposure or susceptibility to the influenza virus should still be vaccinated. “But you also need to take care of yourself. Everyone can reduce their risk by taking simple precautions,” says Dr. Majumdar. “Wash your hands, avoid sick kids and hospitals during flu season, consider antiviral agents for prophylaxis and tell your doctor as soon as you feel unwell because there is still a chance to decrease symptoms and prevent hospitalization if you get sick— because flu vaccine is not as effective as people have been thinking it is.”
  • For vaccine developers: Previously reported mortality reductions are clearly inflated and erroneous–this may have stifled efforts at developing newer and better vaccines especially for use in the elderly.
  • For policy makers: Efforts directed at “improving quality of care” are better directed at where the evidence is, such as hand-washing, vaccinating children and vaccinating healthcare workers. 

Finally, Dr. Majumder said, the findings are a reminder to researchers that “the healthy-user effect is everywhere you don’t want it to be.”

 

 

 

###

Flu Vaccine offers no Protection in seniors

Respost 2008

Contact: Rebecca Hughes hughes.r@ghc.org 206-287-2055 Group Health Research Institute

Flu vaccine may not protect seniors well

Group Health study in Lancet finds no less risk of pneumonia with vaccine

SEATTLE—A Group Health study in the August 2 issue of The Lancet adds fuel to the growing controversy over how well the flu vaccine protects the elderly.

The study of more than 3,500 Group Health patients age 65󈟊 found no link between flu vaccination and risk of pneumonia during three flu seasons. “This suggests that the flu vaccine doesn’t protect seniors as much as has been thought,” said Michael L. Jackson, PhD, MPH, a postdoctoral fellow at the Group Health Center for Health Studies.

“Ours is by far the largest case-control study of flu vaccine in the elderly,” Jackson added. This kind of study compares “cases” with “controls.” The cases were patients with “community-acquired” pneumonia treated in a hospital or elsewhere. The controls were people matched to cases by sex and age, but with no pneumonia. Both groups were found to have similar rates of flu vaccination. All had intact immune systems and none lived in a nursing home.

Jackson and his colleagues carefully reviewed medical records to reveal details of seniors’ health and ability to do daily activities. “We tried to overcome the limits of previous studies done by others,” he explained. “Those studies may have overestimated the benefits of the flu vaccine in the elderly for various reasons.” For instance, those studies looked only at pneumonia cases treated in a hospital. They also included seniors who had immune problems, which limit potential benefit from vaccination. And they didn’t review medical records to get information on chronic diseases, such as heart or lung disease, which raise the risk of pneumonia.

Most importantly, those previous studies also failed to account for differences between healthier seniors and those who were “frail,” Jackson said. Frail seniors are older and have chronic  diseases and difficulty walking. “They are less likely than younger, healthier seniors to go out and get vaccinated—and more apt to develop pneumonia,” he said.

Pneumonia is a common and potentially life-threatening complication of the flu, Jackson said. But pneumonia can happen without the flu. “That’s why our study used a control time period, after flu vaccine became available but before each flu season actually started,” he said. During those pre-flu-season periods, people who had been vaccinated were much less likely to get pneumonia. Why? “Because those who got the vaccine happened to be healthier—not because the flu vaccine was protecting them from pneumonia caused by the flu, since it wasn’t present yet,” he explained.

“Despite our findings, and even though immune responses are known to decline with age, I still want my grandmother to keep getting the flu vaccine,” said Jackson. “The flu vaccine is safe. So it seems worth getting, even if it might lower the risk of pneumonia and death only slightly.”

His co-author Lisa A. Jackson, MD, MPH (no relation), a senior investigator at the Group Health Center for Health Studies, agreed. “People age 65 and older should still get yearly flu vaccines as usual,” she advised. But she said that researchers should work to understand better how well the current flu vaccines work in seniors—and to explore other options for controlling flu in the “old old.” Examples include bigger doses or stronger types of vaccines, and conducting randomized controlled trials comparing them.

###

Other co-authors are Group Health’s Jennifer C. Nelson, PhD and William Barlow, PhD; Noel S. Weiss, MD, DrPH of the Fred Hutchinson Cancer Research Center and University of Washington; and Kathleen M. Neuzil, MD, MPH, of the Program for Appropriate Technology in Health (PATH), University of Washington, and the Group Health Center for Health Studies, all in Seattle.

A fellowship grant from the Group Health Foundation and internal funds from the Group Health Center for Health Studies funded this study.

Group Health Center for Health Studies

Founded in 1947, Group Health Cooperative is a Seattle-based, consumer-governed, nonprofit health care system that coordinates care and coverage. For 25 years, the Group Health Center for Health Studies has conducted research on preventing, diagnosing, and treating major health problems. Government and private research grants provide its main funding.

Please visit the virtual newsroom on our Web site, www.ghc.org under “Newsroom.”

Detailed How To: The Potential for Respiratory Droplet–Transmissible A/H5N1 Influenza Virus to Evolve in a Mammalian Host

* This is information has been made public, I am leaving the figures out...

Science 22 June 2012:
Vol. 336 no. 6088 pp. 1541-1547
DOI: 10.1126/science.1222526

Abstract

Avian A/H5N1 influenza viruses pose a pandemic threat. As few as five amino acid substitutions, or four with reassortment, might be sufficient for mammal-to-mammal transmission through respiratory droplets. From surveillance data, we found that two of these substitutions are common in A/H5N1 viruses, and thus, some viruses might require only three additional substitutions to become transmissible via respiratory droplets between mammals. We used a mathematical model of within-host virus evolution to study factors that could increase and decrease the probability of the remaining substitutions evolving after the virus has infected a mammalian host. These factors, combined with the presence of some of these substitutions in circulating strains, make a virus evolving in nature a potentially serious threat. These results highlight critical areas in which more data are needed for assessing, and potentially averting, this threat.

Recent studies have shown that the A/Indonesia/5/2005 avian A/H5N1 influenza virus may require as few as five amino acid substitutions (1), and the A/Vietnam/1203/2004 A/H5N1 influenza virus requires four substitutions and reassortment (2), to become transmissible between ferrets via respiratory droplets. Here, we assess the likelihood that these substitutions could arise in nature. We first analyzed A/H5N1 sequence surveillance data to identify whether any of these substitutions are already circulating. We then explored the probability of the virus evolving the remaining substitutions after a spillover event of an avian virus into a single mammalian host and in a short chain of transmission between mammalian hosts.

The minimal set of substitutions identified by (1) (the Herfst et al. set) contains two receptor-binding amino acid substitutions, Q222L and G224S (H5 numbering used throughout) in the hemagglutinin (HA), known to change the virus from the more avian-like alpha-2-3–linked sialic acid specificity to the more humanlike alpha-2-6–linked sialic acid (3, 4). The remaining three substitutions in the set are T156A in HA, which disrupts the N-linked glycosylation sequon spanning positions 154 to 156; H103Y in the HA trimer-interface; and E627K in the PB2, which is a common mammalian polymerase adaptation (5). (Numbers refer to amino acid positions in the mature H5 proteins; for example, Q222L indicates that glutamine at position 222 was replaced by leucine. Single-letter abbreviations for the amino acid residues are as follows: A, Ala; C, Cys; D, Asp; E, Glu; F, Phe; G, Gly; H, His; I, Ile; K, Lys; L, Leu; M, Met; N, Asn; P, Pro; Q, Gln; R, Arg; S, Ser; T, Thr; V, Val; W, Trp; and Y, Tyr.)

The four amino acid substitutions in HA identified by (2) (the Imai et al. set) also contain two receptor-binding amino acid substitutions, N220K and Q222L, one of which is in common with the Herfst et al. set and which together are known to change the sialic acid linkage preference to the more human-like alpha-2-6 linkage (2). The remaining two substitutions are N154D, which disrupts the same N-linked glycosylation sequon as the T156A substitution in the Herfst et al. set, and T315I in the stalk region.

Of the three receptor-binding substitutions in the two sets, only N220K in the Imai et al. set has been detected by means of surveillance in consensus sequencing of the HA of A/H5N1 viruses, and only in 2 of 3392 sequences [both avian viruses, one from 2007 in Vietnam, one from Egypt in 2010 (Fig. 1, B and F, black arrows)]. The T315I stalk substitution and H103Y trimer interface substitution have each been detected once in two viruses from China in 2002 (Fig. 1, A and B, orange arrows). T315I has been detected in two pre-1997 H5N1 viruses, four H5N2 viruses, two H5N3 viruses, and two H5N9 viruses. H103Y has been detected in five H5N2 viruses and one H5N3 virus. The remaining substitutions, N154D and T156A in the HA glycosylation sequon and E627K in PB2, however, are common and occur in 942 of 3392, 1803 of 3392, and 432 of 1612 sequences, respectively. A summary of the substitutions detected in surveillance is shown in fig. S1 and table S1. For viruses in which both HA and PB2 have been sequenced, 338 of 1533 have lost the 154-to-156 glycosylation sequon and have E627K in PB2. These viruses have been collected in at least 28 countries in Europe, the Middle East, Africa, and Asia.

Fig. 1

(A to L) Phylogenetic trees of the A/H5N1 HA1 nucleotide sequences. The sequences are split into three trees: 2022 avian H5 sequences from East and Southeast (E and SE) Asia (top row); 1097 avian H5 sequences from Europe, the Middle East, and Africa (middle row); and 385 human H5 sequences (bottom row). Each sequence is color coded by the minimum number of nucleotide mutations required to obtain the four amino acid substitutions in HA in the Herfst et al. set (column 1), to obtain the four amino acid substitutions in the Imai et al. set (column 2), to disrupt the N-linked glycosylation sequon spanning positions 154 to 156 in HA (column 3), and to obtain E627K in the PB2 segment of the corresponding virus in these HA trees (column 4). In columns 1 and 2, blue indicates five nucleotide changes, green indicates four, and orange indicates three. In columns 3 and 4, yellow viruses require one mutation, and pink require zero mutations. Gray indicates PB2 not sequenced. Clades as defined by (35) are marked to the right of the branches; the red portion of the vertical clade-identification lines indicates strains sampled in 2010 or 2011. The viruses indicated by black arrows are two nucleotides from the Imai et al. set. The virus indicated in (A) by the orange arrow has the H103Y substitution, and the virus indicated in (B) by the orange arrow has the T315I substitution. The blue circle indicates A/Indonesia/5/2005, and the red circle indicates A/Vietnam/1203/2004, the starting viruses used by (1) and (2), respectively. The initial trees were constructed with PhyML version 3.0 (36), with A/Chicken/Scotland/1959 as the root, using GTR+I+Γ4 [determined by ModelTest (37)] as the evolutionary model. GARLI version 0.96 (38) was run on the best tree from PhyML for 1 million generations to optimize tree topology and branch lengths. “Zoom-able” versions of these trees are shown in fig. S1 to show detail

The HA glycosylation sequon substitutions, N154D and T156A, have drifted in and out of the avian virus population over time, suggesting that they may be under little selective pressure in birds. The other substitutions—which are rare in birds, particularly those that change the sialic acid linkage preference—are likely to be negatively selected in birds.

Phylogenetic trees of the A/H5N1 HA are shown in Fig. 1, color-coded by the number of nucleotide mutations required to obtain the five Herfst et al. set (column 1) and four Imai et al. set (column 2) of substitutions in HA. Obtaining these mutations does not necessarily mean the virus will be transmissible through respiratory droplets between ferrets because the genetic background of each strain is different from the strain used by Herfst et al. (Fig. 1A, blue circle) and the strain used by Imai et al. (Fig. 1J, red circle). Other than for clade 2.3.2.1, the variation in color in Fig. 1, columns 1 and 2, is due to the presence (mostly in East and Southeast Asia) or absence (mostly outside of East and Southeast Asia) of the glycosylation sequon at positions 154 to 156.

The sequenced viruses that are closest to the Herfst et al. set are in clade 2.3.2.1 (Fig. 1A and fig. S1A). These HAs have acquired a silent nucleotide mutation that makes the amino acid substitution G224S require only one nucleotide mutation instead of the two mutations for other strains. It is the requirement of these two nucleotide mutations that makes viruses usually farther from the Herfst et al. set than the Imai et al. set. The viruses in clade 2.3.2.1 have been sampled in Nepal, Mongolia, Japan, and Korea from 2009 to 2011. Seventeen out of 94 of these viruses have been sequenced in PB2 (Fig. 1D), and none have the E627K substitution. Thus, the closest known viruses to the Herfst et al. set by consensus sequencing are four nucleotide substitutions away.

The majority of H5 viruses in clade 2.2 (and its subclades) are three nucleotide mutations from the Imai et al. set in HA (Fig. 1, F and J). These viruses have been sampled in Europe from 2005 to 2007, in the Middle East (including Egypt) from 2005 to 2011, and in Africa from 2005 to 2007. Viruses sampled in 2010 and 2011 are indicated by the red portion of the vertical line delimiting the clade (Fig. 1 and by the time series in fig. S1, F and J). If it is the loss of glycosylation that is important, rather than any other effect of N154D, then as shown in Fig. 1, column 3, almost all the non-Asian viruses have lost the glycosylation sequon, and thus all these viruses would potentially be functionally three nucleotides from the Imai et al. set in HA.

The viruses indicated by the black arrows in Fig. 1, B and F (one from Vietnam in 2007 and one from Egypt in 2010), have the N220K receptor-binding substitution and have lost the glycosylation sequon at positions 154 to 156. Thus, these two viruses are two nucleotide substitutions from the Imai et al. set in HA, and are the viruses closest to having the full Imai et al. set in HA detected to date by means of consensus sequencing.

Surveillance has detected humans with A/H5N1 viruses four nucleotide mutations from the full Herfst et al. set and three from the Imai et al. set in HA. Viruses isolated from human A/H5N1 infections (Fig. 1, bottom row) are generally the same number of mutations in HA away from the Herfst et al. and Imai et al. sets, by means of consensus sequencing, as their most closely related avian viruses. The within-host evolution modeling below indicates that any host adaptation substitutions would only reach a small proportion of the total virus population in the first spillover host and, although potentially critical in the host-adaptation process, would not be detected with consensus sequencing. Thus, the absence of evidence of host-adaption through consensus sequencing is not evidence for the absence of potentially critical adaptation to the mammalian host. See (6) for details of human strains and their most genetically similar avian A/H5N1 viruses.

To explore the probability of accumulating the remaining nucleotide mutations after the avian virus has been transmitted to a human (or other mammalian host), we constructed a mathematical model (7–10) of the within-host evolutionary dynamics of the virus. In the model, errors made by the virus polymerase are the source of mutation (10−5 mutations per site, per genome replication), the initial virus population expands exponentially [each infected cell produces 104 virions (11, 12), and 1010 cells can be infected (13)] until it reaches 1014 virions, after which the virus population size stays roughly constant, and selection is modeled by use of differences in expected numbers of progeny (fig. S2 and table S2) (6). The results of the model are largely insensitive to the number of cells that can be infected, maximum virus population size, and whether the virus population remains roughly constant or declines (figs. S3 to S5). Typical infections were simulated out to 5 days corresponding to the approximate time of peak viral load, and long-duration infections to 14 days (14).

It is not possible to calculate the level of risk precisely because of uncertainties in some aspects of the biology. We used the model to compare the relative effects of factors that could increase or decrease the probability of accumulating mutations and to identify areas for further investigation that are critical for more accurate risk assessment. We compare and contrast the effects of factors that can increase the probability of accumulating mutations and thus evolving a respiratory droplet–transmissible A/H5N1 influenza virus in a mammalian host, and factors that could decrease the probability of evolving a such a virus. The factors we considered that can increase the probability are random mutation, positive selection, long infection, alternate functionally equivalent substitutions, and transmission of partially adapted viruses as a proportion of the within-host diversity both in the avian-mammal and the mammal-mammal transmission events (10, 14–18). The factors we considered that can decrease the probability are an effective immune response, deleterious substitutions, and order-dependence in the acquisition of substitutions. We considered these factors for starting viruses differing in the number of mutations that separates them from a respiratory droplet–transmissible A/H5N1 virus—viruses that require five, four, three, two, or one mutations at specific positions in the virus HA, reflecting that zero, one, two, three, or four of the mutations are already present in the avian population and thus are present at the start of the infection in mammals. We treat each amino acid substitution as if it can be acquired by a single-nucleotide mutation, as is the case for the circulating viruses closest to acquiring the Herfst et al. or Imai et al. sets [see (6) for the general case].

First, we considered random mutation. Even without any positive selection pressure, the random process of mutations introduced by the virus polymerase in the expanding population of viruses will on average produce viruses that contain the required single, double, or triple mutations and even some quadruple mutants. These mutants will arise after a few days of an infection in a host in which the virus replicates efficiently (Fig. 2A) and would be delayed if replication is impaired (fig. S5). However, the existence of a virus within-host does not mean that it will transmit because it might exist only as a small proportion of the total virus population and thus have little chance of being excreted (Fig. 2B). The minimum proportion of mutant virus required to make transmission likely is not known, but increased proportion translates into increased probability of transmission; thus, we focused on proportion of mutant virus in the total virus population. These proportions (equivalent to the probability of a single virion to be a mutant), both here and below, cannot yet be precisely determined—they are sensitive to some biological parameters that are not yet known accurately and some that are specific to a particular virus or mutant. For such parameters, we tested a range of the current best estimates and focused on the relative, rather than the absolute, effects (6).

Fig. 2

Fig. 2

Expected absolute numbers and proportions of respiratory droplet–transmissible A/H5N1 virions within a host initially infected by strains that require five (blue), four (green), three (orange), two (red), or one (purple) mutation (or mutations) to become respiratory droplet–transmissible, calculated from the deterministic model. (A) The absolute number of respiratory droplet–transmissible A/H5N1 viruses in a host. The intersections with the gray line indicate the point when at least one virus in each host is expected to have the required mutations. The change in slope is due to the transition in the virus population from exponential expansion to constant size. (B) Expected proportion of respiratory droplet–transmissible A/H5N1 viruses in the total virus population over time in the random mutation case (when all mutations are fitness-neutral).

Second, we considered positive selection. Some of the substitutions identified by (1) and (2) have been shown to increase within-host virus fitness—specifically, the loss of glycosylation at positions 154 and 156 and E627K in PB2. However, given the absence of specific information on the within-host selective advantage or disadvantage conferred by each substitution, or combination of substitutions, we considered two cases of positive selection: one in which each individual substitution confers an additive advantage (hill-climb) and one in which only viruses that have acquired all substitutions have an advantage (all-or-nothing). We considered a total advantage of 1.1-, 2-, or 10-fold in each genome replication step for the full set of respiratory droplet transmission–enabling substitutions (table S2 and fig. S6) (A twofold advantage at each genome replication step translates into an approximately 100-fold increase in mutant virus titer after 36 hours.) In the all-or-nothing scenario, a strong increase in proportion occurs for viruses that have acquired all mutations because of its substantial fitness advantage over the rest of the population. The rate at which all-or-nothing selection increases the proportion of respiratory droplet–transmissible A/H5N1 viruses, as compared with the neutral case, is mostly independent of the number of mutations required (Fig. 3A). In contrast, for hill-climb selection the rate of increase above the neutral case decreases when fewer mutations are required (Fig. 3A). This difference between the all-or-nothing and hill-climb is because the fitness differential from the starting virus to the respiratory droplet–transmissible A/H5N1 virus decreases as the number of needed mutations decreases (if some of the mutations are already present in the avian host) (table S2) (6). We consider this hill-climb case to be the most likely situation during the host-adaptation we modeled (in the absence of deleterious substitutions). However, we have also compared the two selection scenarios when the starting fitnesses of all-or-nothing and hill-climb are the same independent of the starting number of necessary mutations, and discuss the subtle tradeoff between the fitness advantage of, and clonal-interference among, intermediate mutants (figs. S7 and S8) (6, 19).

Fig. 3

Factors that increase or decrease the proportion of respiratory droplet–transmissible A/H5N1 virus based on starting viruses that require five (blue), four (green), three (orange), two (red), or one (purple) mutation (or mutations) to become respiratory droplet–transmissible. (A) The effect of hill-climb and all-or-nothing positive selection compared with random mutation alone. (B) The effect of avian–mammal transmission of partially adapted virus as a result of intra-host diversity (100 viruses start the infection, one of which has a mutation) and the effect of alternate substitutions with 10 functionally equivalent sites for a virus requiring five mutations (blue), nine sites for a virus requiring four mutations (green), eight sites for a virus requiring three mutations (orange), seven sites for a virus requiring two mutations (red), and six sites for a virus requiring one mutation (purple), both with hill-climb selection, compared with hill-climb selection alone. (C) The effect of two of the required substitutions being individually deleterious (for these two specific substitutions, either substitution alone reduces the replicative fitness of the virus to zero) and the effect of complete order dependence of acquiring substitutions, both with hill-climb selection as compared with hill-climb selection alone.

Third, we considered long infection. Because both random mutation and positive selection increase the expected proportion of mutated virions with every viral generation, the longer a host is infected, the greater the proportion of a particular mutant (Fig. 4) (15). Human A/H5N1 infections lasting 14 days or longer have been reported especially in children, the elderly, and the immunocompromised (14) and have been associated with the evolution of oseltamivir resistance (20). It might be that only immunocompromised individuals can typically transmit the virus late in a long infection. The increasing proportion of mutant virus is only dependent on continued virus production and is independent of whether the virus load stays constant or declines (fig. S4) (21). The variance in the proportion of mutant virus (Fig. 4, pale regions) increases with each additional mutation required because of the increased number of combinatorial options and the greater selective advantage of mutant viruses as compared with wild-type viruses in the hill-climb scenario. The pale regions only reflect the within-model variance in results, as indicated by the different runs of the stochastic model, and not uncertainty as a result of other factors; sensitivity of the outcomes for model parameters such as the error rate and the number of virions produced by each infected cell are explored in (6) (fig. S5).

Fig. 4

Proportion of respiratory droplet–transmissible A/H5N1 virus in a long infection with virus replication for 14 days in the presence of hill-climb selection. Bold lines show results from a probability-based deterministic model of virus mutation, the pale region (composed of lines) shows 10,000 stochastic model simulations for each starting virus. Starting viruses require either five (blue), four (green), or three (orange) mutations to become respiratory droplet–transmissible. For the stochastic simulations, the lines start when the first virion that has the required mutations appears.

Fourth, we considered functionally equivalent substitutions. The sets of substitutions required for a respiratory droplet–transmissible A/H5N1 virus identified by (1) and (2) are unlikely to be the only combinations of substitutions capable of producing a respiratory droplet–transmissible A/H5N1 virus. If particular biological traits could be achieved by other substitutions, this would increase the expected proportions of respiratory droplet–transmissible A/H5N1 viruses. This is likely to be the case, given that there are multiple substitutions that can cause changes in receptor-binding specificity and two sites where substitutions will result in loss of glycosylation: positions 154 and 156 (table S3). If five substitutions could be from any 10 specific positions in the virus genome (or if two already existed in nature, three from any eight), then there would be 252 (or 56) combinations, and this would raise the proportion of respiratory droplet–transmissible A/H5N1 virus within a host by ~102.5 (or ~101.5) above the case of positive selection alone after 5 days (Fig. 3B, figs. S9 and S10, and table S4).

Fifth, we considered the avian-to-mammal transmission of partially adapted mutants. We specifically considered the case in which one of the required mutations exists as a small proportion of the avian within-host viral population, or in the viral populations from the >20 mammalian hosts in which A/H5N1 infections have been observed (22–25), so that they would not be detected by the usual consensus sequencing techniques. If the mutant is one of the 100 virions that seed an infection (16, 17), then with positive selection the probability of acquiring the remaining mutations increases by 103 after 5 days of infection above the case of positive selection alone (Fig. 3B). If the proportion of mutants in the seeding population is 10−4 however, the increase in proportion of respiratory droplet transmissible A/H5N1 virions in the mammalian host is small (fig. S11).

Sixth, we considered mammal-to-mammal transmission of partially adapted viruses. Transmission of viruses between mammals that have some but not all of the substitutions necessary for respiratory droplet transmission potentially increases the risk of evolving a respiratory droplet–transmissible A/H5N1 virus, but this increase is modulated by the difficulty of transmitting partially adapted strains and the loss of diversity at transmission. Two primary factors strongly modulate the effect of transmission on the accumulation of mutations. First, transmission could decrease the accumulation of mutations by the loss of low-proportion mutants because only a limited portion of the virus population will be transmitted. Second, transmission could increase the accumulation of respiratory droplet transmission–enabling substitutions by concentrating a transmissible virus during excretion from or seeding into a host—for example, if the adapted virus has increased tropism for the mammalian upper respiratory tract and therefore concentrated in the nose and throat. Thus, the effect of transmission can range from negligible, if mutants are culled by the loss of diversity at transmission, to substantial, if selection favors mutants at transmission (table S5). Given that A/H5N1 virus infections have been observed in >20 mammalian species, there is a potentially large pool of nonhuman hosts in which short chains of transmission could play a role in the emergence of respiratory droplet–transmissible A/H5N1 viruses.

In contrast to these factors that could increase the rate of accumulating substitutions, we next discuss factors that could decrease this rate.

First, we considered an effective immune response. An immune response that substantially shortened an infection would decrease the probability of the accumulation of mutations; however, there are many reported cases of infections up to and beyond 5 days (14, 21). Variation in the number of virions produced by each infected cell does not affect the deterministic calculations of the proportion of mutants. However, if this number is substantially lower for the stochastic simulations—for example, 25 (6) as compared with 10,000 (used for most of the figures)—the slower growth and lower total number of viruses could substantially delay the appearance of mutants within a host. As the number of required mutations increases, stochastic effects caused by the slower growth decrease the proportion of these mutants (fig. S5) (6).

Second, we considered deleterious intermediate substitutions. The receptor binding and trimer-interface or stalk substitutions required by (1) and (2) are, as we have seen, either rare or absent in influenza viruses isolated to date. The receptor-binding substitutions, although deleterious in birds, would be expected to be advantageous in humans. However, the details of this host-adaptation are not yet elucidated, and so we also consider the possibility that there are deleterious intermediate substitutions and explore a variety of scenarios (figs. S12 and S13). When two of the required substitutions are individually deleterious (for these two specific substitutions, either substitution alone reduces the replicative fitness of the virus to zero), this slows the rate of accumulation of mutations for the three-mutation case by less than the amount that hill-climb positive selection increases the rate above the neutral case (Fig. 3C). When three substitutions are required (all single and double substitutions reduce the replicative fitness of a mutant virus to zero), this can lower the accumulation rate ~102 below the neutral case (fig. S12). Deleterious (or advantageous) substitutions other than the respiratory droplet–transmissible A/H5N1 substitutions can, to a first approximation, be ignored in calculating proportions because such substitutions would on average affect all viruses equally and thus would not specifically affect the accumulation of respiratory droplet–transmissible A/H5N1 mutations (6).

Third, we considered order dependence in the acquisition of substitutions. It is not currently known whether the acquisition of some or all of the respiratory droplet transmission–enabling substitutions is dependent on the order in which viruses accumulate those substitutions. For example, the gain of 2-6-receptor binding might be required before loss of 2-3-receptor binding. If there were any order dependence, it would slow down the rate of accumulation of mutations. However, even in the most extreme scenario in which there is a single specific order in which the substitutions must be acquired, and any other order results in a virion with a replicative fitness of zero, if fewer than four mutations are required, the effect on the rate of accumulation of mutations is less than that of the deleterious scenario described above (Fig. 3C and figs. S14 and S15).

In addition to the substitutions in HA, the Imai et al. virus was a reassortment with an A/H1pdm09 virus. The probability of a reassortment event is difficult to determine given current knowledge. In one study (26), it has been estimated to be more likely than the likelihood of acquiring a single mutation as calculated here.

Highly pathogenic avian A/H5N1 viruses have been infecting humans for over a decade, with ~600 reported cases to date (and possibly many more that have not been reported), but there have yet to be known cases of efficient human-to-human transmission (27, 28). One hypothesis for the lack of sustained transmission is that it is not possible for A/H5N1 viruses to become respiratory droplet–transmissible in mammals; (1) and (2) have shown that this may not be the case in ferrets. Another hypothesis is that the number of mutations necessary for respiratory droplet–transmissibility might be so great that such a virus would be unlikely to evolve. We show here that in biologically plausible scenarios, respiratory droplet–transmissible A/H5N1 viruses can evolve during a mammalian infection. Given that respiratory droplet transmission between mammals is possible and that respiratory droplet–transmissible A/H5N1 mutants are likely to evolve in infected individuals, the primary impediment to transmission could be whether the respiratory droplet–transmissible A/H5N1 viruses comprise a sufficient proportion of the within-host viral population to actually transmit.

The minimum proportion of virus required for transmission is not known, but increased proportion likely translates into increased probability of transmission. There cannot be respiratory droplet transmission if there are no viruses in the air. Given a peak excretion rate of ~107 viruses per day (29, 30), a proportion of which are likely to become aerosolized (31), mutants at proportions near or above 10−7 might thus be among the particles excreted. Each of the factors analyzed above has a potentially substantial effect on the rate of accumulating mutations (Fig. 3), and the effects of each can be additive. With plausible combinations of these factors, a virus that requires three mutations reaches proportions at which a few respiratory droplet–transmissible A/H5N1 viruses are likely to be among the particles excreted. For a virus that requires five mutations, it may only reach such proportions with more extreme combinations of factors or if an event occurs that is not encompassed by the model (32). However, it is known that influenza viruses are capable of respiratory droplet transmission in animal models at low infectious doses (33), and that transmission routes other than in respiratory droplets could be important; thus despite the three key current unknowns about transmission (6), even low numbers of excreted respiratory droplet–transmissible A/H5N1 virus may be relevant for emergence. In addition, the probability of emergence increases when more mammals are infected when this also corresponds with a rise in potential transmission events. The output of the model is a guide to understand the approximate effects of different factors and should not be interpreted as actual proportions of virus and probabilities of transmission, given the uncertainty inherent in parameter estimates and model structure, and the inherent unpredictability of rare events (34).

These results highlight four areas of investigation that are critical to more accurately assess and monitor the risk of a respiratory droplet–transmissible A/H5N1 virus emerging and to increase our understanding of virus emergence in general. Some of this work is already ongoing, planned, or suggested. The work of Herfst et al. (1) and Imai et al. (2) and the analyses here help to prioritize particular areas.

First, additional surveillance in higher-risk regions where viruses require fewer nucleotide mutations to acquire respiratory droplet transmission–enabling substitutions (Fig. 1 and fig. S1) (and in regions connected by travel, trade, and migratory flyways) is key for monitoring the emergence of a respiratory droplet–transmissible A/H5N1 virus. Surveillance of nonhuman mammalian hosts, especially any that harbor long infections or live in large groups, is important for the early identification of mammalian adaptation. Additionally, studies are needed on the accumulation of mutations within-host and in short chains of transmission in mammals (22–25), even when endemic circulation has not been observed.

Second, deep sequencing of avian and other nonhuman virus samples is necessary to accurately estimate the prevalence of the respiratory droplet transmission–enabling amino acid substitutions in nature. Deep sequencing of human samples, particularly at multiple time points from individuals with long infections, would be useful for evaluating within-host evolution, for estimating selective advantage of substitutions, and for testing the underlying dynamics and assumptions of the model (15). Respiratory droplet–transmissible A/H5N1 mutations present in a proportion higher than the polymerase error rate—exceeding approximately 10−5, but far below the threshold for detection with consensus sequencing and thus not detectable with current surveillance practices—would increase the risk of a respiratory droplet–transmissible A/H5N1 evolving. Thus, sequencing deeper than that currently routinely achieved for RNA viruses (ideally detecting mutations at 0.1% frequency and lower for detailed studies) is necessary to more accurately assess the risk posed by intra-host variability (15).

Third, experiments are needed to determine which substitutions, besides the already identified receptor-binding substitutions by (1) and (2), are capable of producing respiratory droplet–transmissible A/H5N1 viruses, including the important case of functionally equivalent substitutions or alternative sets of substitutions that would require fewer nucleotide mutations than those of the Herfst et al. or Imai et al. sets. This work will be important for calculating risk and for monitoring in surveillance.

Fourth, further studies are needed to elucidate the changes in within-host fitness and between-host transmissibility associated with each respiratory droplet transmission–enabling substitution and combination of substitutions. These studies are necessary for determining the dynamics of within-host selection [including data on, and modeling of, the effects of glycan heterogeneity between the upper and lower respiratory tract (6)] and the potential for transmission of partially adapted viruses. It is important to determine the strength of selection at transmission because it can increase the proportion of respiratory droplet transmission–enabling substitutions. Further work is needed to refine the estimate for virus excretion and the minimum human infectious dose (29).

Numerous avian A/H5N1 viruses have been sampled in the past 2 years that are four nucleotide mutations from acquiring the Herfst et al. set of HA and PB2 substitutions and three nucleotide mutations from acquiring the Imai et al. set in HA (the Imai et al. set also requires a reassortment event). Precise estimates of the probability of evolving the remaining mutations for the virus to become a respiratory droplet–transmissible A/H5N1 virus cannot be accurately calculated at this time because of gaps in knowledge of the factors described above. However, the analyses here, using current best estimates, indicate that the remaining mutations could evolve within a single mammalian host, making the possibility of a respiratory droplet–transmissible A/H5N1 virus evolving in nature a

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Acknowledgments: C.A.R. was supported by a University Research Fellowship from the Royal Society. The authors acknowledge an Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) VICI grant, European Union (EU) FP7 programs EMPERIE (223498) and ANTIGONE (278976), Human Frontier Science Program (HFSP) program grant P0050/2008, Wellcome 087982AIA, the Bill and Melinda Gates Foundation (OPPGH5383), and NIH Director’s Pioneer Award DP1-OD000490-01. R.A.M.F was supported by National Institute of Allergy and Infectious Diseases (NIAID)–NIH contract HHSN266200700010C. A.E.X.B. was supported by a long-term fellowship from the HFSP. E.M., G.N., and Y.K. are supported by the Bill and Melinda Gates Foundation (OPPGH5383) and NIAID-NIH grant R01 AI 069274; in addition, Y.K. was supported by a Grant-in-Aid for Specially Promoted Research from the Ministry of Education, Culture, Sports, Science, and Technology of Japan and by ERATO. Y.K. and G.N. have a financial interest as founders of FluGen and hold a patent on influenza virus reverse genetics. Y.K and G.N. have a paid consulting relationship with Theraclone; Y.K. also has a paid consulting relationship with Crucell. Y.K. has received speaker’s honoraria from Chugai Pharmaceuticals, Novartis, Daiichi-Sankyo Pharmaceutical, Toyama Chemical, Wyeth, GlaxoSmithKline, and Astellas and grant support from Chugai Pharmaceuticals, Daiichi Sankyo Pharmaceutical, Toyama Chemical, Otsuka Pharmaceutical Company A.D.M.E.O. (on behalf of Viroclinics Biosciences BV) has advisory affiliations with GlaxoSmithKline, Novartis, and Roche. A.D.M.E.O. is Chief Scientific Officer of Viroclinics Biosciences BV. We thank S. Cornell, E. Ghedin, R. Johnstone, L. Reperant, and D. M. Smith for helpful discussions and the reviewers for their detailed and thoughtful comments.