Time lines of infection and disease in human influenza: a review of volunteer challenge studies

Changes in severity of 2009 pandemic A/H1N1 influenza in England: a Bayesian evidence synthesis

OBJECTIVE

To assess the impact of the 2009 A/H1N1 influenza pandemic in England during the two waves of activity up to end of February 2010 by estimating the probabilities of cases leading to severe events and the proportion of the population infected.

DESIGN

A Bayesian evidence synthesis of all available relevant surveillance data in England to estimate severity of the pandemic.

DATA SOURCES

All available surveillance systems relevant to the pandemic 2009 A/H1N1 influenza outbreak in England from June 2009 to February 2010. Pre-existing influenza surveillance systems, including estimated numbers of symptomatic cases based on consultations to the health service for influenza-like illness and cross sectional population serological surveys, as well as systems set up in response to the pandemic, including follow-up of laboratory confirmed cases up to end of June 2009 (FF100 and Fluzone databases), retrospective and prospective follow-up of confirmed hospitalised cases, and reported deaths associated with pandemic 2009 A/H1N1 influenza. Main outcome measures Age specific and wave specific probabilities of infection and symptomatic infection resulting in hospitalisation, intensive care admission, and death, as well as infection attack rates (both symptomatic and total). The probabilities of intensive care admission and death given hospitalisation over time are also estimated to evaluate potential changes in severity across waves.

RESULTS

In the summer wave of A/H1N1 influenza, 0.54% (95% credible interval 0.33% to 0.82%) of the estimated 606,100 (419,300 to 886,300) symptomatic cases were hospitalised, 0.05% (0.03% to 0.08%) entered intensive care, and 0.015% (0.010% to 0.022%) died. These correspond to 3200 (2300 to 4700) hospital admissions, 310 (200 to 480) intensive care admissions, and 90 (80 to 110) deaths in the summer wave. In the second wave, 0.55% (0.28% to 0.89%) of the 1,352,000 (829,900 to 2,806,000) estimated symptomatic cases were hospitalised, 0.10% (0.05% to 0.16%) were admitted to intensive care, and 0.025% (0.013% to 0.040%) died. These correspond to 7500 (5900 to 9700) hospitalisations, 1340 (1030 to 1790) admissions to intensive care, and 240 (310 to 380) deaths. Just over a third (35% (26% to 45%)) of infections were estimated to be symptomatic. The estimated probabilities of infections resulting in severe events were therefore 0.19% (0.12% to 0.29%), 0.02% (0.01% to 0.03%), and 0.005% (0.004% to 0.008%) in the summer wave for hospitalisation, intensive care admission, and death respectively. The corresponding second wave probabilities are 0.19% (0.10% to 0.32%), 0.03% (0.02% to 0.06%), and 0.009% (0.004% to 0.014%). An estimated 30% (20% to 43%) of hospitalisations were detected in surveillance systems in the summer, compared with 20% (15% to 25%) in the second wave. Across the two waves, a mid-estimate of 11.2% (7.4% to 18.9%) of the population of England were infected, rising to 29.5% (16.9% to 64.1%) in 5-14 year olds. Sensitivity analyses to the evidence included suggest this infection attack rate could be as low as 5.9% (4.2% to 8.7%) or as high as 28.4% (26.0% to 30.8%). In terms of the probability that an infection leads to death in the second wave, these correspond, respectively, to a high estimate of 0.017% (0.011% to 0.024%) and a low estimate of 0.0027% (0.0024% to 0.0031%).

CONCLUSIONS

This study suggests a mild pandemic, characterised by case and infection severity ratios increasing between waves. Results suggest low ascertainment rates, highlighting the importance of systems enabling early robust estimation of severity, to inform optimal public health responses, particularly in light of the apparent resurgence of the 2009 A/H1N1 strain in the 2010-11 influenza season.

Influenza transmission in households during the 1918 pandemic

Analysis of historical data has strongly shaped our understanding of the epidemiology of pandemic influenza and informs analysis of current and future epidemics. Here, the authors analyzed previously unpublished documents from a large household survey of the "Spanish" H1N1 influenza pandemic, conducted in 1918, for the first time quantifying influenza transmissibility at the person-to-person level during that most lethal of pandemics. The authors estimated a low probability of person-to-person transmission relative to comparable estimates from seasonal influenza and other directly transmitted infections but similar to recent estimates from the 2009 H1N1 pandemic. The authors estimated a very low probability of asymptomatic infection, a previously unknown parameter for this pandemic, consistent with an unusually virulent virus. The authors estimated a high frequency of prior immunity that they attributed to a largely unreported influenza epidemic in the spring of 1918 (or perhaps to cross-reactive immunity). Extrapolating from this finding, the authors hypothesize that prior immunity partially protected some populations from the worst of the fall pandemic and helps explain differences in attack rates between populations. Together, these analyses demonstrate that the 1918 influenza virus, though highly virulent, was only moderately transmissible and thus in a modern context would be considered controllable.

H1N1 hemagglutinin-inhibition seroprevalence in Emergency Department Health Care workers after the first wave of the 2009 influenza pandemic

STUDY OBJECTIVE

The 2009 H1N1 pandemic (H1N1pdm) virus has been associated with high rates of asymptomatic infections. Existing influenza infection control policies do not address potential transmission through exposure to asymptomatic infected individuals in health care settings. We conducted a seroprevalence study of H1N1pdm infection to determine whether health care workers (HCWs) in the emergency department showed increased evidence of infection during the first wave of the pandemic than that previously reported in adults in the community.

METHODS

Blood samples and demographic and clinical data were collected from eligible emergency department HCWs. Subjects' sera were tested for presence of antibodies specific for seasonal H1N1 and H1N1pdm viruses by hemagglutination-inhibition assay.

RESULTS

One hundred eight subjects were enrolled, of which 20 (18.5%) were seropositive for H1N1pdm and 52 (48%) for seasonal H1N1. The median age of H1N1pdm-seropositive subjects was 32 years (range, 24-59 years). Of H1N1pdm-seropositive subjects, 35% were asymptomatic. Rates of H1N1pdm detection in HCWs (18.5%) were significantly higher than those observed previously in an identical age cohort in the community (2.6%, n = 262).

CONCLUSIONS

The higher serodetection rates in adults observed in the current study suggest potentially significantly more frequent infections in HCWs than in the general population. Further investigations are needed to ascertain the relative incidence of influenza infections in HCWs and non-HCWs, to study influenza transmission by asymptomatic infected subjects and ascertain the burden of such transmission in health care settings.

Temporal dynamics of host molecular responses differentiate symptomatic and asymptomatic influenza a infection

Exposure to influenza viruses is necessary, but not sufficient, for healthy human hosts to develop symptomatic illness. The host response is an important determinant of disease progression. In order to delineate host molecular responses that differentiate symptomatic and asymptomatic Influenza A infection, we inoculated 17 healthy adults with live influenza (H3N2/Wisconsin) and examined changes in host peripheral blood gene expression at 16 timepoints over 132 hours. Here we present distinct transcriptional dynamics of host responses unique to asymptomatic and symptomatic infections. We show that symptomatic hosts invoke, simultaneously, multiple pattern recognition receptors-mediated antiviral and inflammatory responses that may relate to virus-induced oxidative stress. In contrast, asymptomatic subjects tightly regulate these responses and exhibit elevated expression of genes that function in antioxidant responses and cell-mediated responses. We reveal an ab initio molecular signature that strongly correlates to symptomatic clinical disease and biomarkers whose expression patterns best discriminate early from late phases of infection. Our results establish a temporal pattern of host molecular responses that differentiates symptomatic from asymptomatic infections and reveals an asymptomatic host-unique non-passive response signature, suggesting novel putative molecular targets for both prognostic assessment and ameliorative therapeutic intervention in seasonal and pandemic influenza.

The transcriptional responses of human hosts towards influenza viral pathogens are important for understanding virus-mediated immunopathology. Despite great advances gained through studies using model organisms, the complete temporal host transcriptional responses in a natural human system are poorly understood. In a human challenge study using live influenza (H3N2/Wisconsin) viruses, we conducted a clinically uninformed (unsupervised) factor analysis on gene expression profiles and established an ab initio molecular signature that strongly correlates to symptomatic clinical disease. This is followed by the identification of 42 biomarkers whose expression patterns best differentiate early from late phases of infection. In parallel, a clinically informed (supervised) analysis revealed over-stimulation of multiple viral sensing pathways in symptomatic hosts and linked their temporal trajectory with development of diverse clinical signs and symptoms. The resultant inflammatory cytokine profiles were shown to contribute to the pathogenesis because their significant increase preceded disease manifestation by 36 hours. In subclinical asymptomatic hosts, we discovered strong transcriptional regulation of genes involved in inflammasome activation, genes encoding virus interacting proteins, and evidence of active anti-oxidant and cell-mediated innate immune response. Taken together, our findings offer insights into influenza virus-induced pathogenesis and provide a valuable tool for disease monitoring and management in natural environments.

Estimating incidence curves of several infections using symptom surveillance data

We introduce a method for estimating incidence curves of several co-circulating infectious pathogens, where each infection has its own probabilities of particular symptom profiles. Our deconvolution method utilizes weekly surveillance data on symptoms from a defined population as well as additional data on symptoms from a sample of virologically confirmed infectious episodes. We illustrate this method by numerical simulations and by using data from a survey conducted on the University of Michigan campus. Last, we describe the data needs to make such estimates accurate.

Antiviral treatment for pandemic influenza: assessing potential repercussions using a seasonally forced SIR model

When resources are limited, measures to control an incipient influenza pandemic must be carefully considered. Because several months are needed to mass-produce vaccines once a new pandemic strain has been identified, antiviral drugs are often considered the first line of defense in a pandemic situation. Here we use an SIR disease model with periodic transmission rate to assess the efficacy of control strategies via antiviral drug treatment during an outbreak of pandemic influenza. We show that in some situations, and independent of drug-resistance effects, antiviral treatment can have a detrimental impact on the final size of the pandemic. Antiviral treatment also has the potential to increase the size of the major peak of the pandemic, and cause it to occur earlier than it would have if treatment were not used. Our studies suggest that when a disease exhibits periodic patterns in transmission, decisions of public health policy will be particularly important as to how control measures such as drug treatment should be implemented, and to what end (i.e.; towards immediate control of a current epidemic peak, or towards potential delay and/or reduction of an anticipated autumn peak).

E-mail-based symptomatic surveillance combined with self-collection of nasal swabs: a new tool for acute respiratory infection epidemiology

Objective

We examined the feasibility of combining communication by e-mail and self-collection of nasal swabs for the prospective detection of acute respiratory infections in a non-medical setting.

Methods

The study was conducted among a convenience sample of employees (n = 53) at a research institution (December 2009–April 2010). Real-time data on the occurrence of acute respiratory symptoms and a nasal self-swab were collected prospectively, with automated weekly e-mails as a reminder mechanism. Reverse transcription polymerase chain reaction (RT-PCR) was used to detect respiratory viral pathogens in the swabs.

Results

Fifty-one out of 53 participants completed the study. The study design was well accepted. Thirty (∼57%) participants reported at least one episode of acute respiratory infection and returned the nasal swab during the study period (eight participants reported two episodes). The majority had no difficulties taking the self-swab and preferred this to swabbing by study personnel. Most participants obtained and returned the swabs within the recommended time. Viral respiratory pathogens were detected in 19 of 38 swabs (50%), with coronaviruses 229E/NL63 and OC43 and rhinoviruses A and B constituting 17 positive swabs (89%).

Conclusions

Combining e-mail-based symptomatic surveillance with nasal self-swabbing promises to be a powerful tool for the real-time identification of incident cases of acute respiratory infections and the associated pathogens in population-based studies.

The age-specific cumulative incidence of infection with pandemic influenza H1N1 2009 was similar in various countries prior to vaccination

BACKGROUND

During the influenza pandemic of 2009 estimates of symptomatic and asymptomatic infection were needed to guide vaccination policies and inform other control measures. Serological studies are the most reliable way to measure influenza infection independent of symptoms. We reviewed all published serological studies that estimated the cumulative incidence of infection with pandemic influenza H1N1 2009 prior to the initiation of population-based vaccination against the pandemic strain.

METHODOLOGY AND PRINCIPAL FINDINGS

We searched for studies that estimated the cumulative incidence of pandemic influenza infection in the wider community. We excluded studies that did not include both pre- and post-pandemic serological sampling and studies that included response to vaccination. We identified 47 potentially eligible studies and included 12 of them in the review. Where there had been a significant first wave, the cumulative incidence of pandemic influenza infection was reported in the range 16%-28% in pre-school aged children, 34%-43% in school aged children and 12%-15% in young adults. Only 2%-3% of older adults were infected. The proportion of the entire population infected ranged from 11%-18%. We re-estimated the cumulative incidence to account for the small proportion of infections that may not have been detected by serology, and performed direct age-standardisation to the study population. For those countries where it could be calculated, this suggested a population cumulative incidence in the range 11%-21%.

CONCLUSIONS AND SIGNIFICANCE

Around the world, the cumulative incidence of infection (which is higher than the cumulative incidence of clinical disease) was below that anticipated prior to the pandemic. Serological studies need to be routine in order to be sufficiently timely to provide support for decisions about vaccination.

Mitigating effects of vaccination on influenza outbreaks given constraints in stockpile size and daily administration capacity

Background

Influenza viruses are a major cause of morbidity and mortality worldwide. Vaccination remains a powerful tool for preventing or mitigating influenza outbreaks. Yet, vaccine supplies and daily administration capacities are limited, even in developed countries. Understanding how such constraints can alter the mitigating effects of vaccination is a crucial part of influenza preparedness plans. Mathematical models provide tools for government and medical officials to assess the impact of different vaccination strategies and plan accordingly. However, many existing models of vaccination employ several questionable assumptions, including a rate of vaccination proportional to the population at each point in time.

Methods

We present a SIR-like model that explicitly takes into account vaccine supply and the number of vaccines administered per day and places data-informed limits on these parameters. We refer to this as the non-proportional model of vaccination and compare it to the proportional scheme typically found in the literature.

Results

The proportional and non-proportional models behave similarly for a few different vaccination scenarios. However, there are parameter regimes involving the vaccination campaign duration and daily supply limit for which the non-proportional model predicts smaller epidemics that peak later, but may last longer, than those of the proportional model. We also use the non-proportional model to predict the mitigating effects of variably timed vaccination campaigns for different levels of vaccination coverage, using specific constraints on daily administration capacity.

Conclusions

The non-proportional model of vaccination is a theoretical improvement that provides more accurate predictions of the mitigating effects of vaccination on influenza outbreaks than the proportional model. In addition, parameters such as vaccine supply and daily administration limit can be easily adjusted to simulate conditions in developed and developing nations with a wide variety of financial and medical resources. Finally, the model can be used by government and medical officials to create customized pandemic preparedness plans based on the supply and administration constraints of specific communities.

Tuberculosis vaccines: progress and challenges

An effective tuberculosis (TB) vaccine could have a significant impact on the current TB pandemic. The past decade has seen sustained global investment into reaching this goal; currently there are several promising vaccines in clinical trials. Current strategies include the development of an improved bacille Calmette-Guerin (BCG) vaccine to be given at birth and a booster vaccine to be administered after BCG. Here, we describe the current vaccination strategy and review the main issues in novel TB vaccine development. Potential vaccine candidates are evaluated in pre-clinical animal models, and the most promising go into clinical testing; a vaccine candidate is evaluated in at least one model before progressing to early clinical trials. The main challenge in early trials is the lack of a defined correlate of vaccine-induced immune protection. Following this, large efficacy trials are undertaken, which face the daunting challenges of cost, logistics and trial site capacity.

Potential role of human challenge studies for investigation of influenza transmission

The importance of different routes of influenza transmission (including the role of bioaerosols) and the ability of masks and hand hygiene to prevent transmission remain poorly understood. Interest in transmission of influenza has grown as the effectiveness of prevention measures implemented during the 2009 H1N1 pandemic are questioned and as plans to better prepare for the next pandemic are debated. Recent studies of naturally infected patients have encountered difficulties and have fallen short of providing definitive answers. Human challenge studies with influenza virus date back to the 1918 pandemic. In more recent decades they have been undertaken to investigate the efficacy of antiviral agents and vaccines. Could experimental challenge studies, in which volunteers are deliberately infected with influenza virus, provide an alternative approach to the study of transmission? Here, we review the latest intervention studies and discuss the potential of challenge studies to address the remaining gaps in our knowledge.

Strength and tempo of selection revealed in viral gene genealogies

Background

RNA viruses evolve extremely quickly, allowing them to rapidly adapt to new environmental conditions. Viral pathogens, such as influenza virus, exploit this capacity for evolutionary change to persist within the human population despite substantial immune pressure. Understanding the process of adaptation in these viral systems is essential to our efforts to combat infectious disease.

Results

Through analysis of simulated populations and sequence data from influenza A (H3N2) and measles virus, we show how phylogenetic and population genetic techniques can be used to assess the strength and temporal pattern of adaptive evolution. The action of natural selection affects the shape of the genealogical tree connecting members of an evolving population, causing deviations from the neutral expectation. The magnitude and distribution of these deviations lends insight into the historical pattern of evolution and adaptation in the viral population. We quantify the degree of ongoing adaptation in influenza and measles virus through comparison of census population size and effective population size inferred from genealogical patterns, finding a 60-fold greater deviation in influenza than in measles. We also examine the tempo of adaptation in influenza, finding evidence for both continuous and episodic change.

Conclusions

Our results have important consequences for understanding the epidemiological and evolutionary dynamics of the influenza virus. Additionally, these general techniques may prove useful to assess the strength and pattern of adaptive evolution in a variety of evolving systems. They are especially powerful when assessing selection in fast-evolving populations, where temporal patterns become highly visible.

Factors associated with infection by 2009 pandemic H1N1 influenza virus during different phases of the epidemic

OBJECTIVE

The focus of this study was to ascertain the factors associated with 2009 pandemic influenza H1N1 (pH1N1) infection during different phases of the epidemic.

METHODS

In central Taiwan, 306 persons from households with schoolchildren were followed sequentially and serum samples were taken at three sampling time-points starting in the fall of 2008, shortly after influenza vaccination. Participants who seroconverted between two consecutive blood samplings were considered as having serological evidence of infection. A generalized estimation equation (GEE) with a logistic link to account for household correlations was applied to identify factors associated with pH1N1 infections during the pre-epidemic (April-June) and epidemic (September-October) periods.

RESULTS

The results showed that receiving an inactivated seasonal influenza vaccine (ISIV) and having a hemagglutination inhibition assay (HI) titer of 40 or higher resulted in a significantly lower likelihood of pH1N1 infection during the pre-epidemic period only, for both children and adults (adjusted odds ratio (OR) 0.3, 95% confidence interval (CI) 0.12-0.9). Having a previous infection by pH1N1 with a baseline titer of 20 or higher resulted in a significantly lower likelihood of infection by pH1N1 during the epidemic period (adjusted OR 0.06, 95% CI 0.02-0.16).

CONCLUSIONS

Our results provide the first serological evidence to suggest a protection effect from receiving an ISIV against pH1N1 infection only when the HI titer reaches 40 or higher during the pre-epidemic period. This study gives an important insight into the control and intervention measures required for preventing infections during future influenza epidemics.

Influenza vaccine effectiveness in Wisconsin during the 2007-08 season: comparison of interim and final results

BACKGROUND

During the 2007-08 influenza season, we reported an interim vaccine effectiveness (VE) estimate of 44% for preventing medically attended influenza. In this analysis we report results for the entire season and compare them with the interim estimate.

METHODS

Patients with feverishness, chills, or cough <8 days duration were prospectively recruited over 10 weeks and tested for influenza by real-time reverse transcriptase PCR (rRT-PCR). Case-control analyses were performed using data from patients with rRT-PCR confirmed influenza (cases) and ill patients without influenza (test-negative controls). VE was estimated as 100×(1-adjusted odds ratio) in a logistic regression model adjusting for age, week, and high risk medical condition. A sample of influenza isolates was antigenically characterized.

RESULTS

Influenza was detected by rRT-PCR in 865 (44%) of 1972 patients; 73% were type A and 27% were type B. VE was 37% (95% CI, 22-49%) overall and 44% (95% CI, 27-58%) among participants tested 0-3 days after illness onset. VE was 39% (95% CI, 2-62%) in children 6-59 months old and 37% (95% CI, -2% to 61%) in adults ≥50 years old. VE was 41% (95% CI, 24-53%) for influenza A and 31% (95% CI, 3-51%) for influenza B. All 24 characterized influenza A viruses were antigenically matched to the H3N2 vaccine strain, although 14 viruses exhibited mild antigenic drift. There was a lineage mismatch with the vaccine strain for all 39 characterized influenza B viruses.

CONCLUSIONS

The 2007-08 influenza vaccine provided modest protection against medically attended influenza in this population. The interim estimate of VE after 17 days closely approximated the final season VE, supporting the potential use of interim VE estimates while influenza seasons are still in progress.

Analysis of CDC social control measures using an agent-based simulation of an influenza epidemic in a city

Background

The transmission of infectious disease amongst the human population is a complex process which requires advanced, often individual-based, models to capture the space-time details observed in reality.

Methods

An Individual Space-Time Activity-based Model (ISTAM) was applied to simulate the effectiveness of non-pharmaceutical control measures including: (1) refraining from social activities, (2) school closure and (3) household quarantine, for a hypothetical influenza outbreak in an urban area.

Results

Amongst the set of control measures tested, refraining from social activities with various compliance levels was relatively ineffective. Household quarantine was very effective, especially for the peak number of cases and total number of cases, with large differences between compliance levels. Household quarantine resulted in a decrease in the peak number of cases from more than 300 to around 158 for a 100% compliance level, a decrease of about 48.7%. The delay in the outbreak peak was about 3 to 17 days. The total number of cases decreased to a range of 3635-5403, that is, 63.7%-94.7% of the baseline value.

When coupling control measures, household quarantine together with school closure was the most effective strategy. The resulting space-time distribution of infection in different classes of activity bundles (AB) suggests that the epidemic outbreak is strengthened amongst children and then spread to adults. By sensitivity analysis, this study demonstrated that earlier implementation of control measures leads to greater efficacy. Also, for infectious diseases with larger basic reproduction number, the effectiveness of non-pharmaceutical measures was shown to be limited.

Conclusions

Simulated results showed that household quarantine was the most effective control measure, while school closure and household quarantine implemented together achieved the greatest benefit. Agent-based models should be applied in the future to evaluate the efficacy of control measures for a range of disease outbreaks in a range of settings given sufficient information about the given case and knowledge about the transmission processes at a fine scale.

Influenza in travelers: epidemiology, risk, prevention, and control issues

Influenza is the most frequent travel related infection preventable by universally available vaccines, but preventive measures were neglected until recently. Since the spread of pandemic (H1N1) 2009, various public health measures have been promoted first to contain, then to mitigate, the pandemic. Some of these measures contradicted recommendations issued by the World Health Organization and were of questionable efficacy. However, travelers may benefit from targeted recommendations on influenza risk reduction (eg, by social distancing or immunization). These recommendations are particularly indicated for those with an increased personal risk profile and for those likely to be exposed to influenza patients.

An insight into the swine-influenza A (H1N1) virus infection in humans

WHO declares on June 11, 2009, that H1N1 (Swine-influenza A) is pandemic. There have been nearly 30,000 confirmed H1N1 cases across 74 countries. The reports have shown sharp increase in the number of infections reported in recent days from Chile, Japan, and the UK, and other parts of the world, with the most dramatic increase recorded in Australia where more than 1200 cases were reported in a very short duration. As per the latest report of the Ministry of Health and Family Welfare, death from swine flu has reached to 1235. Around 12,3397 people have been tested in India as on February 1, 2010. In India, 23.3% of people who have tested for swine flu are found suffering from swine flu. Also around 4% of people who have tested positive for swine flu have died and could not be saved in India. The New York Times has reported that this is the first flu for being pandemic in the last 41 years. This article enlightens the brief review about the swine influenza virus, its modes of spread, and prevention measures. The aim of this article is to bring awareness in general and know the consequences of the infection.

Viral shedding in children infected by pandemic A/H1N1/2009 influenza virus

BACKGROUND

The aim of this study was to investigate viral shedding in otherwise healthy children with pandemic A/H1N1/2009 influenza in order to define how long children with pandemic A/H1N1/2009 influenza shed the virus, and also plan adequate measures to control the spread of the disease within households.

FINDINGS

In 74 otherwise healthy children with pandemic A/H1N1/2009 influenza, nasopharyngeal swabs were taken for virus detection upon hospital admission and every two days until negative. The nasopharyngeal swabs of all of the children were positive for pandemic A/H1N1/2009 influenza virus in the first three days after the onset of infection, and only 21.6% and 13.5% remained positive after respectively 11 and 15 days. No child was positive after more than 15 days. Viral load also decreased over time, and was not associated with patient age or the risk of pneumonia. Those who shed the virus for ≥ 9 days were not at any increased risk of suffering from more severe disease in comparison with those who shed the virus for a shorter time, but their households experienced a significantly higher number of influenza-like illness during the two weeks after the onset of the initial disease (72.3% vs 41.4%; p < 0.05).

CONCLUSIONS

Regardless of their age, healthy children can shed pandemic A/H1N1/2009 influenza virus for up to two weeks after illness onset, and the households of the children who shed the virus for ≥ 9 days suffered a higher number of influenza-like illness in the two weeks following the onset of the first disease. This could suggest that when a completely unknown influenza virus is circulating, isolation period of infected children has to be longer than the 7 days recommended for the infections due to seasonal influenza viruses.

Findings from a household randomized controlled trial of hand washing and face masks to reduce influenza transmission in Bangkok, Thailand

BACKGROUND

Evidence is needed on the effectiveness of non-pharmaceutical interventions (NPIs) to reduce influenza transmission.

METHODOLOGY

We studied NPIs in households with a febrile, influenza-positive child. Households were randomized to control, hand washing (HW), or hand washing plus paper surgical face masks (HW + FM) arms. Study nurses conducted home visits within 24 hours of enrollment and on days 3, 7, and 21. Respiratory swabs and serum were collected from all household members and tested for influenza by RT-PCR or serology.

PRINCIPAL FINDINGS

Between April 2008 and August 2009, 991 (16·5%) of 5995 pediatric influenza-like illness patients tested influenza positive. Four hundred and forty-two index children with 1147 household members were enrolled, and 221 (50·0%) were aged <6 years. Three hundred and ninety-seven (89·8%) households reported that the index patient slept in the parents' bedroom. The secondary attack rate was 21·5%, and 56/345 (16·3%; 95% CI 12·4-20·2%) secondary cases were asymptomatic. Hand-washing subjects reported 4·7 washing episodes/day, compared to 4·9 times/day in the HW + FM arm and 3·9 times/day in controls (P = 0·001). The odds ratios (ORs) for secondary influenza infection were not significantly different in the HW arm (OR = 1·20; 95% CI 0·76-1·88; P-0.442), or the HW + FM arm (OR = 1·16; 95% CI .0·74-1·82; P = 0.525).

CONCLUSIONS

Influenza transmission was not reduced by interventions to promote hand washing and face mask use. This may be attributable to transmission that occurred before the intervention, poor facemask compliance, little difference in hand-washing frequency between study groups, and shared sleeping arrangements. A prospective study design and a careful analysis of sociocultural factors could improve future NPI studies.

Influenza in immunocompromised patients: considerations for therapy

Influenza infection results in substantial morbidity and mortality in immunocompromised patients, and the risks for influenza and its related complications depend on the degree of immunosuppression. In addition to influenza vaccination and infection control precautions, two classes of antiviral drugs are currently approved for treatment and prophylaxis in uncomplicated infected patients. However, there are no randomized controlled trials assessing the efficacy and safety of licensed antivirals for influenza management in immunocompromised patients. The purpose of this article is to highlight some considerations for therapy in immunocompromised patients, the usefulness of vaccination for the prevention of influenza and the clinical interest in surveillance of antiviral resistance.

Essential epidemiological mechanisms underpinning the transmission dynamics of seasonal influenza

Seasonal influenza has considerable impact around the world, both economically and in mortality among risk groups, but there is considerable uncertainty as to the essential mechanisms and their parametrization. In this paper, we identify a number of characteristic features of influenza incidence time series in temperate regions, including ranges of annual attack rates and outbreak durations. By constraining the output of simple models to match these characteristic features, we investigate the role played by population heterogeneity, multiple strains, cross-immunity and the rate of strain evolution in the generation of incidence time series. Results indicate that an age-structured model with non-random mixing and co-circulating strains are both required to match observed time-series data. Our work gives estimates of the seasonal peak basic reproduction number, R₀, in the range 1.6–3. Estimates of R₀ are strongly correlated with the timescale for waning of immunity to current circulating seasonal influenza strain, which we estimate is between 3 and 8 years. Seasonal variation in transmissibility is largely confined to 15–30% of its mean value. While population heterogeneity and cross-immunity are required mechanisms, the degree of heterogeneity and cross-immunity is not tightly constrained. We discuss our findings in the context of other work fitting to seasonal influenza data.

The correlation between infectivity and incubation period of measles, estimated from households with two cases

The generation time of an infectious disease is the time between infection of a primary case and infection of a secondary case by the primary case. Its distribution plays a key role in understanding the dynamics of infectious diseases in populations, e.g. in estimating the basic reproduction number. Moreover, the generation time and incubation period distributions together characterize the effectiveness of control by isolation and quarantine. In modelling studies, a relation between the two is often not made specific, but a correlation is biologically plausible. However, it is difficult to establish such correlation, because of the unobservable nature of infection events. We have quantified a joint distribution of generation time and incubation period by a novel estimation method for household data with two susceptible individuals, consisting of time intervals between disease onsets of two measles cases. We used two such datasets, and a separate incubation period dataset. Results indicate that the mean incubation period and the generation time of measles are positively correlated, and that both lie in the range of 11-12 days, suggesting that infectiousness of measles cases increases significantly around the time of symptom onset. The correlation between times from infection to secondary transmission and to symptom onset could critically affect the predicted effectiveness of isolation and quarantine.

Estimation of type- and subtype-specific influenza vaccine effectiveness in Victoria, Australia using a test negative case control method, 2007-2008

BACKGROUND

Antigenic variation of influenza virus necessitates annual reformulation of seasonal influenza vaccines, which contain two type A strains (H1N1 and H3N2) and one type B strain. We used a test negative case control design to estimate influenza vaccine effectiveness (VE) against influenza by type and subtype over two consecutive seasons in Victoria, Australia.

METHODS

Patients presenting with influenza-like illness to general practitioners (GPs) in a sentinel surveillance network during 2007 and 2008 were tested for influenza. Cases tested positive for influenza by polymerase chain reaction and controls tested negative for influenza. Vaccination status was recorded by sentinel GPs. Vaccine effectiveness was calculated as [(1--adjusted odds ratio) × 100%].

RESULTS

There were 386 eligible study participants in 2007 of whom 50% were influenza positive and 19% were vaccinated. In 2008 there were 330 eligible study participants of whom 32% were influenza positive and 17% were vaccinated. Adjusted VE against A/H3N2 influenza in 2007 was 68% (95% CI, 32 to 85%) but VE against A/H1N1 (27%; 95% CI, -92 to 72%) and B (84%; 95% CI, -2 to 98%) were not statistically significant. In 2008, the adjusted VE estimate was positive against type B influenza (49%) but negative for A/H1N1 (-88%) and A/H3N2 (-66%); none was statistically significant.

CONCLUSIONS

Type- and subtype-specific assessment of influenza VE is needed to identify variations that cannot be differentiated from a measure of VE against all influenza. Type- and subtype-specific influenza VE estimates in Victoria in 2007 and 2008 were generally consistent with strain circulation data.

Estimating the disease burden of 2009 pandemic influenza A(H1N1) from surveillance and household surveys in Greece

BACKGROUND

The aim of this study was to assess the disease burden of the 2009 pandemic influenza A(H1N1) in Greece.

METHODOLOGY/PRINCIPAL FINDINGS

Data on influenza-like illness (ILI), collected through cross-sectional nationwide telephone surveys of 1,000 households in Greece repeated for 25 consecutive weeks, were combined with data from H1N1 virologic surveillance to estimate the incidence and the clinical attack rate (CAR) of influenza A(H1N1). Alternative definitions of ILI (cough or sore throat and fever>38°C [ILI-38] or fever 37.1-38°C [ILI-37]) were used to estimate the number of symptomatic infections. The infection attack rate (IAR) was approximated using estimates from published studies on the frequency of fever in infected individuals. Data on H1N1 morbidity and mortality were used to estimate ICU admission and case fatality (CFR) rates. The epidemic peaked on week 48/2009 with approximately 750-1,500 new cases/100,000 population per week, depending on ILI-38 or ILI-37 case definition, respectively. By week 6/2010, 7.1%-15.6% of the population in Greece was estimated to be symptomatically infected with H1N1. Children 5-19 years represented the most affected population group (CAR:27%-54%), whereas individuals older than 64 years were the least affected (CAR:0.6%-2.2%). The IAR (95% CI) of influenza A(H1N1) was estimated to be 19.7% (13.3%, 26.1%). Per 1,000 symptomatic cases, based on ILI-38 case definition, 416 attended health services, 108 visited hospital emergency departments and 15 were admitted to hospitals. ICU admission rate and CFR were 37 and 17.5 per 100,000 symptomatic cases or 13.4 and 6.3 per 100,000 infections, respectively.

CONCLUSIONS/SIGNIFICANCE

Influenza A(H1N1) infected one fifth and caused symptomatic infection in up to 15% of the Greek population. Although individuals older than 65 years were the least affected age group in terms of attack rate, they had 55 and 185 times higher risk of ICU admission and CFR, respectively.

Modelling the initial phase of an epidemic using incidence and infection network data: 2009 H1N1 pandemic in Israel as a case study

This paper presents new computational and modelling tools for studying the dynamics of an epidemic in its initial stages that use both available incidence time series and data describing the population's infection network structure. The work is motivated by data collected at the beginning of the H1N1 pandemic outbreak in Israel in the summer of 2009. We formulated a new discrete-time stochastic epidemic SIR (susceptible-infected-recovered) model that explicitly takes into account the disease's specific generation-time distribution and the intrinsic demographic stochasticity inherent to the infection process. Moreover, in contrast with many other modelling approaches, the model allows direct analytical derivation of estimates for the effective reproductive number (R(e)) and of their credible intervals, by maximum likelihood and Bayesian methods. The basic model can be extended to include age-class structure, and a maximum likelihood methodology allows us to estimate the model's next-generation matrix by combining two types of data: (i) the incidence series of each age group, and (ii) infection network data that provide partial information of 'who-infected-who'. Unlike other approaches for estimating the next-generation matrix, the method developed here does not require making a priori assumptions about the structure of the next-generation matrix. We show, using a simulation study, that even a relatively small amount of information about the infection network greatly improves the accuracy of estimation of the next-generation matrix. The method is applied in practice to estimate the next-generation matrix from the Israeli H1N1 pandemic data. The tools developed here should be of practical importance for future investigations of epidemics during their initial stages. However, they require the availability of data which represent a random sample of the real epidemic process. We discuss the conditions under which reporting rates may or may not influence our estimated quantities and the effects of bias.

Improving the evidence base for decision making during a pandemic: the example of 2009 influenza A/H1N1

This article synthesizes and extends discussions held during an international meeting on "Surveillance for Decision Making: The Example of 2009 Pandemic Influenza A/H1N1," held at the Center for Communicable Disease Dynamics (CCDD), Harvard School of Public Health, on June 14 and 15, 2010. The meeting involved local, national, and global health authorities and academics representing 7 countries on 4 continents. We define the needs for surveillance in terms of the key decisions that must be made in response to a pandemic: how large a response to mount and which control measures to implement, for whom, and when. In doing so, we specify the quantitative evidence required to make informed decisions. We then describe the sources of surveillance and other population-based data that can presently--or in the future--form the basis for such evidence, and the interpretive tools needed to process raw surveillance data. We describe other inputs to decision making besides epidemiologic and surveillance data, and we conclude with key lessons of the 2009 pandemic for designing and planning surveillance in the future.

Preclinical development of an in vivo BCG challenge model for testing candidate TB vaccine efficacy

There is an urgent need for an immunological correlate of protection against tuberculosis (TB) with which to evaluate candidate TB vaccines in clinical trials. Development of a human challenge model of Mycobacterium tuberculosis (M.tb) could facilitate the detection of such correlate(s). Here we propose a novel in vivo Bacille Calmette-Guérin (BCG) challenge model using BCG immunization as a surrogate for M.tb infection. Culture and quantitative PCR methods have been developed to quantify BCG in the skin, using the mouse ear as a surrogate for human skin. Candidate TB vaccines have been evaluated for their ability to protect against a BCG skin challenge, using this model, and the results indicate that protection against a BCG skin challenge is predictive of BCG vaccine efficacy against aerosol M.tb challenge. Translation of these findings to a human BCG challenge model could enable more rapid assessment and down selection of candidate TB vaccines and ultimately the identification of an immune correlate of protection.

Planning for the control of pandemic influenza A (H1N1) in Los Angeles County and the United States

Mathematical and computer models can provide guidance to public health officials by projecting the course of an epidemic and evaluating control measures. The authors built upon an existing collaboration between an academic research group and the Los Angeles County, California, Department of Public Health to plan for and respond to the first and subsequent years of pandemic influenza A (H1N1) circulation. The use of models allowed the authors to 1) project the timing and magnitude of the epidemic in Los Angeles County and the continental United States; 2) predict the effect of the influenza mass vaccination campaign that began in October 2009 on the spread of pandemic H1N1 in Los Angeles County and the continental United States; and 3) predict that a third wave of pandemic influenza in the winter or spring of 2010 was unlikely to occur. The close collaboration between modelers and public health officials during pandemic H1N1 spread in the fall of 2009 helped Los Angeles County officials develop a measured and appropriate response to the unfolding pandemic and establish reasonable goals for mitigation of pandemic H1N1.

Virus detection and duration of illness among patients with 2009 pandemic influenza A (H1N1) virus infection in Texas

Knowledge from early outbreaks is limited regarding the virus detection and illness duration of the 2009 pandemic influenza A (H1N1) infections. During the period from April to May 2009 in Texas, we collected serial nasopharyngeal (NP) and stool specimens from 35 participants, testing by real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) and culture. The participants were aged 2 months to 71 years; 25 (71%) were under 18. The median duration of measured fever was 3.0 days and of virus detection in NP specimens was 4.2 days; however, few specimens were collected between days 5-9. The duration of virus detection (4.2 days) was similar to the duration of fever (3.5 days) (RR, 1.14; 95% CI, .66-1.95; P = .8), but was shorter than the duration of cough (11.0 days) (RR, .41; 95% CI, .24-.68; P < .001). We detected viral RNA in two participants' stools. All cultures were negative. This investigation suggests that the duration of virus detection was likely similar to the seasonal influenza virus.

Serial intervals and the temporal distribution of secondary infections within households of 2009 pandemic influenza A (H1N1): implications for influenza control recommendations

A critical issue during the 2009 influenza A (H1N1) pandemic was determining the appropriate duration of time individuals with influenza-like illness (ILI) should remain isolated to reduce onward transmission while limiting societal disruption. Ideally this is based on knowledge of the relative infectiousness of ill individuals at each point during the course of the infection. Data on 261 clinically apparent pH1N1 infector-infectee pairs in households, from 7 epidemiological studies conducted in the United States early in 2009, were analyzed to estimate the distribution of times from symptom onset in an infector to symptom onset in the household contacts they infect (mean, 2.9 days, not correcting for tertiary transmission). Only 5% of transmission events were estimated to take place >3 days after the onset of clinical symptoms among those ill with pH1N1 virus. These results will inform future recommendations on duration of isolation of individuals with ILI.

Viral shedding duration of pandemic influenza A H1N1 virus during an elementary school outbreak--Pennsylvania, May-June 2009

We report shedding duration of 2009 pandemic influenza A (pH1N1) virus from a school-associated outbreak in Pennsylvania during May through June 2009. Outbreak-associated students or household contacts with influenza-like illness (ILI) onset within 7 days of interview were recruited. Nasopharyngeal specimens, collected every 48 hours until 2 consecutive nonpositive tests, underwent real-time reverse transcriptase polymerase chain reaction (rRT-PCR) and culture for pH1N1 virus. Culture-positive specimens underwent virus titrations. Twenty-six (median age, 8 years) rRT-PCR-positive persons, for pH1N1 virus, were included in analysis. Median shedding duration from fever onset by rRT-PCR was 6 days (range, 1-13) and 5 days (range, 1-7) by culture. Following fever resolution virus was isolated for a median of 2 days (range, 0-5). Highest and lowest virus titers detected, 2 and 5 days following fever onset, were 3.2 and 1.2 log(10) TCID(50)/mL respectively. Overall, shedding duration in children and adults were similar to seasonal influenza viruses.

Comparing three basic models for seasonal influenza

In this paper we report the use of the open source Spatiotemporal Epidemiological Modeler (STEM, www.eclipse.org/stem) to compare three basic models for seasonal influenza transmission. The models are designed to test for possible differences between the seasonal transmission of influenza A and B. Model 1 assumes that the seasonality and magnitude of transmission do not vary between influenza A and B. Model 2 assumes that the magnitude of seasonal forcing (i.e., the maximum transmissibility), but not the background transmission or flu season length, differs between influenza A and B. Model 3 assumes that the magnitude of seasonal forcing, the background transmission, and flu season length all differ between strains. The models are all optimized using 10 years of surveillance data from 49 of 50 administrative divisions in Israel. Using a cross-validation technique, we compare the relative accuracy of the models and discuss the potential for prediction. We find that accounting for variation in transmission amplitude increases the predictive ability compared to the base. However, little improvement is obtained by allowing for further variation in the shape of the seasonal forcing function.

Reconstructing the 2003/2004 H3N2 influenza epidemic in Switzerland with a spatially explicit, individual-based model

world has not faced a severe pandemic for decades, except the rather mild H1N1 one in 2009, pandemic influenza models are inherently hypothetical and validation is, thus, difficult. We aim at reconstructing a recent seasonal influenza epidemic that occurred in Switzerland and deem this to be a promising validation strategy for models of influenza spread.

METHODS

We present a spatially explicit, individual-based simulation model of influenza spread. The simulation model bases upon (i) simulated human travel data, (ii) data on human contact patterns and (iii) empirical knowledge on the epidemiology of influenza. For model validation we compare the simulation outcomes with empirical knowledge regarding (i) the shape of the epidemic curve, overall infection rate and reproduction number, (ii) age-dependent infection rates and time of infection, (iii) spatial patterns.

RESULTS

The simulation model is capable of reproducing the shape of the 2003/2004 H3N2 epidemic curve of Switzerland and generates an overall infection rate (14.9 percent) and reproduction numbers (between 1.2 and 1.3), which are realistic for seasonal influenza epidemics. Age and spatial patterns observed in empirical data are also reflected by the model: Highest infection rates are in children between 5 and 14 and the disease spreads along the main transport axes from west to east.

CONCLUSIONS

We show that finding evidence for the validity of simulation models of influenza spread by challenging them with seasonal influenza outbreak data is possible and promising. Simulation models for pandemic spread gain more credibility if they are able to reproduce seasonal influenza outbreaks. For more robust modelling of seasonal influenza, serological data complementing sentinel information would be beneficial.

Has estimation of numbers of cases of pandemic influenza H1N1 in England in 2009 provided a useful measure of the occurrence of disease?

BACKGROUND

Surveillance indicators of influenza activity have generally provided robust comparative trend data for England. These indicators became less reliable, however, for monitoring trends in activity, or comparisons with previous years, during the influenza pandemic in 2009 because of changes in the perception of risk and changes in the systems of healthcare delivery. An approach was developed to estimate the number of cases of influenza-like illness (ILI) occurring because of infection with pandemic influenza virus.

METHODS AND FINDINGS

The number of cases was estimated each week in England on the basis of total number of patients consulting healthcare services with ILI; estimates of the proportion of individuals in the community experiencing an ILI-seeking health care; and the proportion of these positive on laboratory testing. Almost 800,000 cases (range 375,000-1·6 million) of symptomatic ILI cases were estimated to have occurred over the course of the two waves of pandemic activity in England. More cases were estimated to have occurred in the second wave than in the first.

CONCLUSIONS

These results underestimate the total number of infections as they do not include asymptomatic infections nor those with mild illness not meeting the definition of a case of ILI. Nevertheless, the case number estimates provide a useful indicator of the trend in influenza activity and weekly data were extensively used in media reports. Although surveillance methods differ between countries, the approach of synthesising available data sources to produce an overall estimate of case numbers could be applied more widely to provide comparative data.

Seroprevalence of antibodies to pandemic (H1N1) 2009 influenza virus among health care workers in two general hospitals after first outbreak in Kobe, Japan

OBJECTIVE

To assess the prevalence including asymptomatic infection, infection risk of exposure to patients, and effectiveness of personal protective equipment (PPE) among health care workers (HCWs) during the first pandemic (H1N1) 2009 (pH1N1) outbreak in Kobe, Japan in May 2009.

METHODS

A cross-sectional seroepidemiological study was conducted on 268 HCWs in the two hospitals in Kobe to which all pH1N1 inpatients were directed. Participating HCWs completed a self-administrated questionnaire and provided a single serum sample which was analyzed using a hemagglutination-inhibition (HI) antibody test.

RESULTS

Of 268 subjects, 14 (5.2%) were found to have positive antibodies to the pH1N1 by HI assay; only 1 reported a febrile episode. Among the 14 seropositive cases, 8 received chemoprophylaxis. 162 HCWs (60.4%) had been exposed to patients. The seropositive rate (SPR) for pH1N1 of the exposed group was higher than that of the unexposed group, however not statistically significant (6.8% vs. 3.1%, p = 0.197). There were no statistically significant differences in SPR for any PPE.

CONCLUSION

The SPR for pH1N1 in the exposed group was higher than that of the unexposed group in HCWs; however, most of these individuals were asymptomatic. There was no statistically significant association between PPE implementation and pH1N1 seropositivity.

Relationship between clinical signs and transmission of an infectious disease and the implications for control

Control of many infectious diseases relies on the detection of clinical cases and the isolation, removal, or treatment of cases and their contacts. The success of such "reactive" strategies is influenced by the fraction of transmission occurring before signs appear. We performed experimental studies of foot-and-mouth disease transmission in cattle and estimated this fraction at less than half the value expected from detecting virus in body fluids, the standard proxy measure of infectiousness. This is because the infectious period is shorter (mean 1.7 days) than currently realized, and animals are not infectious until, on average, 0.5 days after clinical signs appear. These results imply that controversial preemptive control measures may be unnecessary; instead, efforts should be directed at early detection of infection and rapid intervention.

Frequent detection of respiratory viruses without symptoms: toward defining clinically relevant cutoff values

Highly sensitive techniques, such as PCR, have greatly improved the detection of respiratory viruses. However, the sensitivity of PCR tests also complicates clinical interpretation, as the presence of small amounts of viral targets may not necessarily have clinical relevance. We performed a prospective case-control study in asymptomatic and symptomatic young children. PCR detection of 14 respiratory viruses was performed in nasal washes, and results were quantified in copies per milliliter. A total of 141 cases and 157 controls were included. In 72% of the cases and 28% of the controls, at least one virus was identified. When stratified for age, at least one virus was identified in 47% of the controls younger than 1 year old. Rhinovirus (RV) was frequently detected in both symptomatic and asymptomatic individuals. Receiver operating characteristic analysis for quantitative rhinovirus detection showed that cutoff values for clinical relevance are feasible for RV. In contrast to rhinovirus, respiratory syncytial virus (RSV) was rarely detected in controls, suggesting that a positive RSV test result is almost always of clinical relevance, independent of viral quantity. In conclusion, our study shows that asymptomatic carriage of a respiratory virus occurs frequently in young children. However, significant differences in the amount of virus present were observed between cases and controls. This suggests that defining cutoff levels should be feasible and represents the next necessary step for diagnosing viral respiratory infections using molecular tests.

Fever screening during the influenza (H1N1-2009) pandemic at Narita International Airport, Japan

Background

Entry screening tends to start with a search for febrile international passengers, and infrared thermoscanners have been employed for fever screening in Japan. We aimed to retrospectively assess the feasibility of detecting influenza cases based on fever screening as a sole measure.

Methods

Two datasets were collected at Narita International Airport during the 2009 pandemic. The first contained confirmed influenza cases (n = 16) whose diagnosis took place at the airport during the early stages of the pandemic, and the second contained a selected and suspected fraction of passengers (self-reported or detected by an infrared thermoscanner; n = 1,049) screened from September 2009 to January 2010. The sensitivity of fever (38.0°C) for detecting H1N1-2009 was estimated, and the diagnostic performances of the infrared thermoscanners in detecting hyperthermia at cut-off levels of 37.5°C, 38.0°C and 38.5°C were also estimated.

Results

The sensitivity of fever for detecting H1N1-2009 cases upon arrival was estimated to be 22.2% (95% confidence interval: 0, 55.6) among nine confirmed H1N1-2009 cases, and 55.6% of the H1N1-2009 cases were under antipyretic medications upon arrival. The sensitivity and specificity of the infrared thermoscanners in detecting hyperthermia ranged from 50.8-70.4% and 63.6-81.7%, respectively. The positive predictive value appeared to be as low as 37.3-68.0%.

Conclusions

The sensitivity of entry screening is a product of the sensitivity of fever for detecting influenza cases and the sensitivity of the infrared thermoscanners in detecting fever. Given the additional presence of confounding factors and unrestricted medications among passengers, reliance on fever alone is unlikely to be feasible as an entry screening measure.

Explaining rapid reinfections in multiple-wave influenza outbreaks: Tristan da Cunha 1971 epidemic as a case study

Influenza usually spreads through the human population in multiple-wave outbreaks. Successive reinfection of individuals over a short time interval has been explicitly reported during past pandemics. However, the causes of rapid reinfection and the role of reinfection in driving multiple-wave outbreaks remain poorly understood. To investigate these issues, we focus on a two-wave influenza A/H3N2 epidemic that occurred on the remote island of Tristan da Cunha in 1971. Over 59 days, 273 (96%) of 284 islanders experienced at least one attack and 92 (32%) experienced two attacks. We formulate six mathematical models invoking a variety of antigenic and immunological reinfection mechanisms. Using a maximum-likelihood analysis to confront model predictions with the reported incidence time series, we demonstrate that only two mechanisms can be retained: some hosts with either a delayed or deficient humoral immune response to the primary influenza infection were reinfected by the same strain, thus initiating the second epidemic wave. Both mechanisms are supported by previous empirical studies and may arise from a combination of genetic and ecological causes. We advocate that a better understanding and account of heterogeneity in the human immune response are essential to analysis of multiple-wave influenza outbreaks and pandemic planning.

A multiplicative hazard regression model to assess the risk of disease transmission at hospital during community epidemics

Background

During community epidemics, infections may be imported within hospital and transmitted to hospitalized patients. Hospital outbreaks of communicable diseases have been increasingly reported during the last decades and have had significant consequences in terms of patient morbidity, mortality, and associated costs. Quantitative studies are thus needed to estimate the risks of communicable diseases among hospital patients, taking into account the epidemiological process outside, hospital and host-related risk factors of infection and the role of other patients and healthcare workers as sources of infection.

Methods

We propose a multiplicative hazard regression model to analyze the risk of acquiring a communicable disease by patients at hospital. This model derives from epidemiological data on communicable disease epidemics in the community, hospital ward, patient susceptibility to infection, and exposure of patients to infection at hospital. The model estimates the relative effect of each of these factors on a patient's risk of communicable disease.

Results

Using individual data on patients and health care workers in a teaching hospital during the 2004-2005 influenza season in Lyon (France), we show the ability of the model to assess the risk of influenza-like illness among hospitalized patients. The significant effects on the risk of influenza-like illness were those of old age, exposure to infectious patients or health care workers, and a stay in a medical care unit.

Conclusions

The proposed multiplicative hazard regression model could be an interesting epidemiological tool to quantify the risk of communicable disease at hospital during community epidemics and the uncertainty inherent in such quantification. Furthermore, key epidemiological, environmental, host, or exposure factors that influence this risk can be identified.

Lack of evidence for pre-symptomatic transmission of pandemic influenza virus A(H1N1) 2009 in an outbreak among teenagers; Germany, 2009

Please cite this paper as: Hermes et al. (2011) Lack of evidence for pre‐symptomatic transmission of pandemic influenza virus A(H1N1) 2009 in an outbreak among teenagers; Germany, 2009. Influenza and Other Respiratory Viruses 5(6), e499–e503.

Background  Observations on the role of pre‐symptomatic transmission in the spread of influenza virus are scanty. In June 2009, an outbreak of pandemic A(H1N1) 2009 infection occurred at a teenager’s party in Germany. The objective of this study was to identify risk factors for pandemic A(H1N1) 2009 infection.

Methods  We performed a retrospective cohort study among party guests. A case was defined as pandemic A(H1N1) 2009 infection confirmed by rRT‐PCR who developed influenza‐like illness between 1 and 5 June 2009. Contact patterns among party guests were evaluated.

Results  In eight (36%) of 27 party guests, the outcome was ascertained. A travel returnee from a country with endemic pandemic A(H1N1) 2009 who fell ill toward the end of the party was identified as the source case. Party guests with pandemic A(H1N1) 2009 infection had talked significantly longer to the source case than non‐infected persons (P‐value: 0·001). Importantly, none (0/9) of those who had left the party prior to the source case’s symptom onset became infected compared to 7 (41%) of 17 who stayed overnight (P = 0·06), and these persons all had transmission‐prone contacts to the source case.

Conclusions  In this outbreak with one index case, there was no evidence to support pre‐symptomatic transmission of pandemic A(H1N1) 2009. Further evidence is required, ideally from larger studies with multiple index cases, to more accurately characterize the potential for pre‐symptomatic transmission of influenza virus.

Onset of a pandemic: characterizing the initial phase of the swine flu (H1N1) epidemic in Israel

Background

The swine influenza H1N1 first identified in Mexico, spread rapidly across the globe and is considered the fastest moving pandemic in history. The early phase of an outbreak, in which data is relatively scarce, presents scientific challenges on key issues such as: scale, severity and immunity which are fundamental for establishing sound and rapid policy schemes. Our analysis of an Israeli dataset aims at understanding the spatio-temporal dynamics of H1N1 in its initial phase.

Methods

We constructed and analyzed a unique dataset from Israel on all confirmed cases (between April 26 to July 7, 2009), representing most swine flu cases in this period. We estimated and characterized fundamental epidemiological features of the pandemic in Israel (e.g. effective reproductive number, age-class distribution, at-risk social groups, infections between sexes, and spatial dynamics). Contact data collected during this stage was used to estimate the generation time distribution of the pandemic.

Results

We found a low effective reproductive number (R_e = 1.06), an age-class distribution of infected individuals (skewed towards ages 18-25), at-risk social groups (soldiers and ultra Orthodox Jews), and significant differences in infections between sexes (skewed towards males). In terms of spatial dynamics, the pandemic spread from the central coastal plain of Israel to other regions, with higher infection rates in more densely populated sub-districts with higher income households.

Conclusions

Analysis of high quality data holds much promise in reducing uncertainty regarding fundamental aspects of the initial phase of an outbreak (e.g. the effective reproductive number R_e, age-class distribution, at-risk social groups). The formulation for determining the effective reproductive number R_e used here has many advantages for studying the initial phase of the outbreak since it neither assumes exponential growth of infectives and is independent of the reporting rate. The finding of a low R_e (close to unity threshold), combined with identification of social groups with high transmission rates would have enabled the containment of swine flu during the summer in Israel. Our unique use of contact data provided new insights into the differential dynamics of influenza in different ages and sexes, and should be promoted in future epidemiological studies. Thus our work highlights the importance of conducting a comprehensive study of the initial stage of a pandemic in real time.

Performance characteristics of a rapid immunochromatographic assay for detection of pandemic influenza A (H1N1) virus in children

Rapid tests for diagnosis of influenza are valuable assets in the management of influenza in pediatric patients. However, test performance fluctuates with virus subtypes. We assessed the test characteristics of Influenzatop®, a rapid immunochromatographic influenza A and B test, in detecting pandemic 2009 influenza A (H1N1) in children up to 18 years of age, using reverse transcriptase polymerase chain reaction (RT-PCR) as the gold standard. Three hundred and one pediatric outpatients with influenza-like illness were included into the study. Overall sensitivity of Influenzatop® was 64% (95% confidence interval (CI) 56-71%) but increased to 92% (95% CI, 80-97%) when performed between 24 and 48 h after onset of symptoms. Positive Influenzatop® results among RT-PCR-positive patients were associated with higher viral load. No significant variation in test performance could be detected when analyzed by age and high versus low prevalence period. Overall test specificity was 99% (95% CI, 95-100%); positive and negative predictive values were 98% (95% CI, 93-99%) and 70% (95% CI, 63-76%), respectively.

CONCLUSION

Influenzatop® rapid influenza test is a sound tool in the diagnosis of H1N1 in pediatric patients when employed 24-48 h after onset of symptoms.

Shedding of pandemic (H1N1) 2009 virus among health care personnel, Seattle, Washington, USA

The Centers for Disease Control and Prevention (CDC) recommends that health care personnel (HCP) infected with pandemic influenza (H1N1) 2009 virus not work until 24 hours after fever subsides without the use of antipyretics. During an influenza outbreak, we examined the association between viral shedding and fever among infected HCP. Participants recorded temperatures daily and provided nasal wash specimens for 2 weeks after symptom onset. Specimens were tested by using PCR and culture. When they met CDC criteria for returning to work, 12 of 16 HCP (75%) (95% confidence interval 48%-93%) had virus detected by PCR, and 9 (56%) (95% confidence interval 30%-80%) had virus detected by culture. Fever was not associated with shedding duration (p = 0.65). HCP might shed virus even when meeting CDC exclusion guidelines. Further research is needed to clarify the association between viral shedding, symptoms, and infectiousness.

Transmission parameters of the A/H1N1 (2009) influenza virus pandemic: a review

Please cite this paper as: Boëlle P‐Y et al. (2011) Transmission parameters of the A/H1N1 (2009) influenza virus pandemic: a review. Influenza and Other Respiratory Viruses 5(5), 306–316.

Background  The new influenza virus A/H1N1 (2009), identified in mid‐2009, rapidly spread over the world. Estimating the transmissibility of this new virus was a public health priority.

Methods  We reviewed all studies presenting estimates of the serial interval or generation time and the reproduction number of the A/H1N1 (2009) virus infection.

Results  Thirteen studies documented the serial interval from household or close‐contact studies, with overall mean 3 days (95% CI: 2·4, 3·6); taking into account tertiary transmission reduced this estimate to 2·6 days. Model‐based estimates were more variable, from 1·9 to 6 days. Twenty‐four studies reported reproduction numbers for community‐based epidemics at the town or country level. The range was 1·2–3·1, with larger estimates reported at the beginning of the pandemic. Accounting for under‐reporting in the early period of the pandemic and limiting variation because of the choice of the generation time interval, the reproduction number was between 1·2 and 2·3 with median 1·5.

Discussion  The serial interval of A/H1N1 (2009) flu was typically short, with mean value similar to the seasonal flu. The estimates of the reproduction number were more variable. Compared with past influenza pandemics, the median reproduction number was similar (1968) or slightly smaller (1889, 1918, 1957).

Swine outbreak of pandemic influenza A virus on a Canadian research farm supports human-to-swine transmission

BACKGROUND

Swine outbreaks of pandemic influenza A (pH1N1) suggest human introduction of the virus into herds. This study investigates a pH1N1 outbreak occurring on a swine research farm with 37 humans and 1300 swine in Alberta, Canada, from 12 June through 4 July 2009.

METHODS

The staff was surveyed about symptoms, vaccinations, and livestock exposures. Clinical findings were recorded, and viral testing and molecular characterization of isolates from humans and swine were performed. Human serological testing and performance of the human influenza-like illness (ILI) case definition were also studied.

RESULTS

Humans were infected before swine. Seven of 37 humans developed ILI, and 2 (including the index case) were positive for pH1N1 by reverse-transcriptase polymerase chain reaction (RT-PCR). Swine were positive for pH1N1 by RT-PCR 6 days after contact with the human index case and developed symptoms within 24 h of their positive viral test results. Molecular characterization of the entire viral genomes from both species showed minor nucleotide heterogeneity, with 1 amino acid change each in the hemagglutinin and nucleoprotein genes. Sixty-seven percent of humans with positive serological test results and 94% of swine with positive swab specimens had few or no symptoms. Compared with serological testing, the human ILI case definition had a specificity of 100% and sensitivity of 33.3%. The only factor associated with seropositivity was working in the swine nursery.

CONCLUSIONS

Epidemiologic data support human-to-swine transmission, and molecular characterization confirms that virtually identical viruses infected humans and swine in this outbreak. Both species had mild illness and recovered without sequelae.

Requirements and design of the PROSPER protocol for implementation of information infrastructures supporting pandemic response: a Nominal Group study

BACKGROUND

Advanced technical systems and analytic methods promise to provide policy makers with information to help them recognize the consequences of alternative courses of action during pandemics. Evaluations still show that response programs are insufficiently supported by information systems. This paper sets out to derive a protocol for implementation of integrated information infrastructures supporting regional and local pandemic response programs at the stage(s) when the outbreak no longer can be contained at its source.

METHODS

Nominal group methods for reaching consensus on complex problems were used to transform requirements data obtained from international experts into an implementation protocol. The analysis was performed in a cyclical process in which the experts first individually provided input to working documents and then discussed them in conferences calls. Argument-based representation in design patterns was used to define the protocol at technical, system, and pandemic evidence levels.

RESULTS

The Protocol for a Standardized information infrastructure for Pandemic and Emerging infectious disease Response (PROSPER) outlines the implementation of information infrastructure aligned with pandemic response programs. The protocol covers analyses of the community at risk, the response processes, and response impacts. For each of these, the protocol outlines the implementation of a supporting information infrastructure in hierarchical patterns ranging from technical components and system functions to pandemic evidence production.

CONCLUSIONS

The PROSPER protocol provides guidelines for implementation of an information infrastructure for pandemic response programs both in settings where sophisticated health information systems already are used and in developing communities where there is limited access to financial and technical resources. The protocol is based on a generic health service model and its functions are adjusted for community-level analyses of outbreak detection and progress, and response program effectiveness. Scientifically grounded reporting principles need to be established for interpretation of information derived from outbreak detection algorithms and predictive modeling.

Did modeling overestimate the transmission potential of pandemic (H1N1-2009)? Sample size estimation for post-epidemic seroepidemiological studies

BACKGROUND

Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.

METHODOLOGY/PRINCIPAL FINDINGS

Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study (R = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from R = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against R = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with R = 1.15, but final sizes in nine studies indicate overestimation if the value R = 1.90 is used.

CONCLUSIONS

Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when R = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.

Optimal design of studies of influenza transmission in households. I: case-ascertained studies

Case-ascertained household transmission studies, in which households including an 'index case' are recruited and followed up, are invaluable to understanding the epidemiology of influenza. We used a simulation approach parameterized with data from household transmission studies to evaluate alternative study designs. We compared studies that relied on self-reported illness in household contacts vs. studies that used home visits to collect swab specimens for virological confirmation of secondary infections, allowing for the trade-off between sample size vs. intensity of follow-up given a fixed budget. For studies estimating the secondary attack proportion, 2-3 follow-up visits with specimens collected from all members regardless of illness were optimal. However, for studies comparing secondary attack proportions between two or more groups, such as controlled intervention studies, designs with reactive home visits following illness reports in contacts were most powerful, while a design with one home visit optimally timed also performed well.