Planning for the next influenza H1N1 season: a modelling study

Individual Vaccination as Nash Equilibrium in a SIR Model with Application to the 2009-2010 Influenza A (H1N1) Epidemic in France

The vaccination against ongoing epidemics is seldom compulsory but remains one of the most classical means to fight epidemic propagation. However, recent debates concerning the innocuity of vaccines and their risk with respect to the risk of the epidemic itself lead to severe vaccination campaign failures, and new mass behaviors appeared driven by individual self-interest. Prompted by this context, we analyze, in a Susceptible-Infected-Recovered model, whether egocentric individuals can reach an equilibrium with the rest of the society. Using techniques from the "Mean Field Games" theory, we extend previous results and show that an equilibrium exists and characterizes completely the individual best vaccination strategy (with or without discounting). We also compare with a strategy based only on overall societal optimization and exhibit a situation with nonnegative price of anarchy. Finally, we apply the theory to the 2009-2010 Influenza A (H1N1) vaccination campaign in France and hint that a group of individuals stopped vaccinating at levels that indicated a pessimistic perception of the risk of the vaccine.

An operational epidemiological model for calibrating agent-based simulations of pandemic influenza outbreaks

Uncertainty of pandemic influenza viruses continue to cause major preparedness challenges for public health policymakers. Decisions to mitigate influenza outbreaks often involve tradeoff between the social costs of interventions (e.g., school closure) and the cost of uncontrolled spread of the virus. To achieve a balance, policymakers must assess the impact of mitigation strategies once an outbreak begins and the virus characteristics are known. Agent-based (AB) simulation is a useful tool for building highly granular disease spread models incorporating the epidemiological features of the virus as well as the demographic and social behavioral attributes of tens of millions of affected people. Such disease spread models provide excellent basis on which various mitigation strategies can be tested, before they are adopted and implemented by the policymakers. However, to serve as a testbed for the mitigation strategies, the AB simulation models must be operational. A critical requirement for operational AB models is that they are amenable for quick and simple calibration. The calibration process works as follows: the AB model accepts information available from the field and uses those to update its parameters such that some of its outputs in turn replicate the field data. In this paper, we present our epidemiological model based calibration methodology that has a low computational complexity and is easy to interpret. Our model accepts a field estimate of the basic reproduction number, and then uses it to update (calibrate) the infection probabilities in a way that its effect combined with the effects of the given virus epidemiology, demographics, and social behavior results in an infection pattern yielding a similar value of the basic reproduction number. We evaluate the accuracy of the calibration methodology by applying it for an AB simulation model mimicking a regional outbreak in the US. The calibrated model is shown to yield infection patterns closely replicating the input estimates of the basic reproduction number. The calibration method is also tested to replicate an initial infection incidence trend for a H1N1 outbreak like that of 2009.

Community-Based Risk Communication Survey: Risk Prevention Behaviors in Communities during the H1N1 crisis, 2010

OBJECTIVES

The present study aimed to investigate the prevalence of and factors associated with H1N1 preventive behaviors in a community-based population.

METHODS

A cross-sectional study was conducted in three urban and two rural communities in Korea. Interviews were conducted with 3462 individuals (1608 men and 1854 women) aged ≥ 19 years during February-March 2010. Influenza-related information including anxiety, preventive behaviors and their perceived effectiveness, vaccination status, past influenza-like illness symptoms, and sources of and trust in information was obtained.

RESULTS

Among 3462 participants, 173 reported experiencing influenza-like illness symptoms within the past 12 months. The mean H1N1 preventive behavior score was 25.5 ± 5.5 (out of a possible 40). The percent of participants reporting high perceived effectiveness and high anxiety was 46.2% and 21.4%, respectively. After controlling for potential confounders, H1N1 preventive behavior scores were predicted by a high (β = 3.577, p < 0.001) or moderate (β = 2.529, p < 0.001) perception of their effectiveness. Similarly, moderate (β = 1.516, p < 0.001) and high (β = 4.103, p < 0.001) anxiety scores predicted high preventive behavior scores.

CONCLUSION

Effective methods of promoting population behavior change may be nationwide campaigns through mass media, as well as education and promotion by health care providers and broadcasters.

Too much of a good thing? When to stop catch-up vaccination

During the 20th century, deaths from a range of serious infectious diseases decreased dramatically due to the development of safe and effective vaccines. However, infant immunization coverage has increased only marginally since the 1960s, and many people remain susceptible to vaccine-preventable diseases. "Catch-up vaccination" for age groups beyond infancy can be an attractive and effective means of immunizing people who were missed earlier. However, as newborn vaccination rates increase, catch-up vaccination becomes less attractive: the number of susceptible people decreases, so the cost to find and vaccinate each unvaccinated person may increase; in addition, the number of infected individuals decreases, so each unvaccinated person faces a lower risk of infection. This article presents a general framework for determining the optimal time to discontinue a catch-up vaccination program. We use a cost-effectiveness framework: we consider the cost per quality-adjusted life year gained of catch-up vaccination efforts as a function of newborn immunization rates over time and consequent disease prevalence and incidence. We illustrate our results with the example of hepatitis B catch-up vaccination in China. We contrast results from a dynamic modeling approach with an approach that ignores the impact of vaccination on future disease incidence. The latter approach is likely to be simpler for decision makers to understand and implement because of lower data requirements.

Factors associated with post-seasonal serological titer and risk factors for infection with the pandemic A/H1N1 virus in the French general population

The CoPanFlu-France cohort of households was set up in 2009 to study the risk factors for infection by the pandemic influenza virus (H1N1pdm) in the French general population. The authors developed an integrative data-driven approach to identify individual, collective and environmental factors associated with the post-seasonal serological H1N1pdm geometric mean titer, and derived a nested case-control analysis to identify risk factors for infection during the first season. This analysis included 1377 subjects (601 households). The GMT for the general population was 47.1 (95% confidence interval (CI): 45.1, 49.2). According to a multivariable analysis, pandemic vaccination, seasonal vaccination in 2009, recent history of influenza-like illness, asthma, chronic obstructive pulmonary disease, social contacts at school and use of public transports by the local population were associated with a higher GMT, whereas history of smoking was associated with a lower GMT. Additionally, young age at inclusion and risk perception of exposure to the virus at work were identified as possible risk factors, whereas presence of an air humidifier in the living room was a possible protective factor. These findings will be interpreted in light of the longitudinal analyses of this ongoing cohort.

Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm

BACKGROUND

Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches.

METHODS

We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability.

RESULTS

Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model.

CONCLUSIONS

Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models.

Modeling rapidly disseminating infectious disease during mass gatherings

We discuss models for rapidly disseminating infectious diseases during mass gatherings (MGs), using influenza as a case study. Recent innovations in modeling and forecasting influenza transmission dynamics at local, regional, and global scales have made influenza a particularly attractive model scenario for MG. We discuss the behavioral, medical, and population factors for modeling MG disease transmission, review existing model formulations, and highlight key data and modeling gaps related to modeling MG disease transmission. We argue that the proposed improvements will help integrate infectious-disease models in MG health contingency plans in the near future, echoing modeling efforts that have helped shape influenza pandemic preparedness plans in recent years.

Integrative study of pandemic A/H1N1 influenza infections: design and methods of the CoPanFlu-France cohort

BACKGROUND

The risk of influenza infection depends on biological characteristics, individual or collective behaviors and the environmental context. The Cohorts for Pandemic Influenza (CoPanFlu) France study was set up in 2009 after the identification of the novel swine-origin A/H1N1 pandemic influenza virus. This cohort of 601 households (1450 subjects) representative for the general population aims at using an integrative approach to study the risk and characteristics of influenza infection as a complex combination of data collected from questionnaires regarding sociodemographic, medical, behavioral characteristics of subjects and indoor environment, using biological samples or environmental databases.

METHODS/DESIGN

Households were included between December 2009 and July 2010. The design of this study relies on systematic follow-up visits between influenza seasons and additional visits during influenza seasons, when an influenza-like illness is detected in a household via an active surveillance system. During systematic visits, a nurse collects individual and environmental data on questionnaires and obtains blood samples from all members of the household. When an influenza-like-illness is detected, a nurse visits the household three times during the 12 following days, and collects data on questionnaires regarding exposure and symptoms, and biological samples (including nasal swabs) from all subjects in the household. The end of the follow-up period is expected in fall 2012.

DISCUSSION

The large amount of data collected throughout the follow-up will permit a multidisciplinary study of influenza infections. Additional data is being collected and analyzed in this ongoing cohort. The longitudinal analysis of these households will permit integrative analyses of complex phenomena such as individual, collective and environmental risk factors of infection, routes of transmission, or determinants of the immune response to infection or vaccination.

A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels

BACKGROUND

In recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several concerns about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these concerns and identify means of enhancing the current models for higher operational use.

METHODS

We surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers.

RESULTS

While examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values.

CONCLUSIONS

To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.

Incidence of H1N1 2009 virus infection through the analysis of paired plasma specimens among blood donors, France

Background

Knowledge of the age-specific prevalence of seroprotection and incidence of seroconversion infection is necessary to complement clinical surveillance data and statistical models. It provides the basis for estimating the future impact of influenza A (H1N1pdm09) and implementing appropriate prevention and response strategies.

Methods

Using a cross-sectional design, two-stage stratified sampling and paired plasma samples, we estimated the age-specific prevalence of a protective level of H1N1pdm09 antibodies in the French adult population before and after the 2009/10 pandemic, and the proportion of those susceptible that seroconverted due to infection, from a single sample of 1,936 blood donors aged 20–70 years in mainland France in June 2010. Samples with a haemagglutination inhibition (HI) titre ≥1∶40 were considered seropositive, and seroconversion due to infection was defined as a 4-fold increase in titre in the absence of H1N1pdm09 vaccination or pre-pandemic seropositivity.

Results

Out of the 1,936 donors, 1,708 were included in the analysis. Seroprevalence before the pandemic was 6.7% (95% CI 5.0, 8.9) with no significant differences by age-group (p = 0.3). Seroprevalence afterwards was 23.0% (95% CI 17.7, 29.3) with 20–29 year olds having a higher level than older groups (p<0.001). Seroconversion due to infection was 12.2% (95% CI 6.9, 20.5). Younger age-group, vaccination against H1N1 and being seropositive before the pandemic were strongly associated with post-pandemic seropositivity.

Conclusions

Before the 2009/2010 winter influenza season, only 6.7% of the French mainland population aged 20–70 had a level of antibodies usually considered protective. During the first pandemic wave, 12.2% of the population seroconverted due to infection and the seroprevalence after the wave rose to 23%, either due to prepandemic seropositivity, infection or vaccination. This relatively low latter figure contributed to an extension of target groups for influenza vaccination for the 2010/2011 season.

Safety and immunogenicity of a virus-like particle pandemic influenza A (H1N1) 2009 vaccine in a blinded, randomized, placebo-controlled trial of adults in Mexico

Virus-like particles (VLPs) can be rapidly developed from influenza virus genetic sequences in order to supply vaccine after the onset of a pandemic. The safety and immunogenicity of one or two doses of a recombinant A (H1N1) 2009 influenza VLP vaccine was evaluated in a two-stage, Phase 2, randomized, double-blind, placebo-controlled study conducted in 4563 healthy adults, 18–64 years of age, during the H1N1 2009 pandemic in Mexico. In Part A, 1013 subjects were randomized into four treatment groups (5 μg, 15 μg, or 45 μg hemagglutinin [HA] VLP vaccine or placebo) and vaccinated 21 days apart, with sera collected on Days 1, 14 and 36 for hemagglutination inhibition (HAI) testing. After review of safety and immunogenicity data from Part A, additional subjects were immunized with a single dose of 15 μg VLP vaccine (N = 2537) or placebo (N = 1011) and assessed for safety in Part B. Results showed the H1N1 2009 VLP vaccine was safe and well-tolerated. Systemic solicited events were similar between placebo and VLP vaccinated groups with no vaccine-related serious adverse events. Dose response trends for solicited local adverse events were observed, with higher incidences of local pain, swelling, tenderness, and redness reported in the higher VLP dose groups (15 μg and 45 μg) compared to the placebo and 5 μg VLP groups following both vaccinations. Although the majority of local AEs were mild in severity, a dose trend in events of moderate or greater severity was also noted for these solicited events. The VLP vaccine groups demonstrated robust HAI immune responses after a single vaccination, with high rates of seroprotection (≥40 HAI titer) in 82–92% of all subjects and in 64–85% of subjects who were seronegative at the time of immunization. HAI geometric mean titers (GMTs), geometric mean ratios (GMRs) and seroconversion rates were also all statistically higher in the VLP groups compared to placebo for both post-baseline time points. Based on these data, additional clinical trials are in development to evaluate influenza vaccine candidate antigens manufactured using Spodoptera frugiperda (Sf9)/baculovirus-based VLP technology.

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.