Estimating influenza vaccine efficacy from challenge and community-based study data

Mathematical modeling for estimating influenza vaccine efficacy: A case study of the Valencian Community, Spain

Vaccine efficacy and its quantification is a crucial concept for the proper design of public health vaccination policies. In this work we proposed a mathematical model to estimate the efficacy of the influenza vaccine in a real-word scenario. In particular, our model is a SEIR-type epidemiological model, which distinguishes vaccinated and unvaccinated populations. Mathematically, its dynamics is governed by a nonlinear system of ordinary differential equations, where the non-linearity arises from the effective contacts between susceptible and infected individuals. Two key aspects of this study is that we use a vaccine distribution over time that is based on real data specific to the elderly people in the Valencian Community and the calibration process takes into account that over one influenza season a specific proportion of the population becomes infected with influenza. To consider the effectiveness of the vaccine, the model incorporates a parameter, the vaccine attenuation factor, which is related with the vaccine efficacy against the influenza virus. With this framework, in order to calibrate the model parameters and to obtain an influenza vaccine efficacy estimation, we considered the 2016-2017 influenza season in the Valencian Community, Spain, using the influenza reported cases of vaccinated and unvaccinated. In order to ensure the model identifiability, we choose to deterministically calibrate the parameters for different scenarios and we find the one with the minimum error in order to determine the vaccine efficacy. The calibration results suggest that the influenza vaccine developed for 2016-2017 influenza season has an efficacy of approximately 76.7%, and that the risk of becoming infected is five times higher for an unvaccinated individual in comparison with a vaccinated one. This estimation partially agrees with some previous studies related to the influenza vaccine. This study presents a new integrated mathematical approach to study the influenza vaccine efficacy and gives further insight into this important public health topic.

Influenza vaccine coverage and factors associated with non-vaccination among caregiving and care-receiving adults in the Canadian Longitudinal Study on Aging (CLSA)

BACKGROUND

Influenza vaccination is recommended for those at increased risk of influenza complications and their household contacts to help reduce influenza exposure. Adults who require care often experience health issues that could increase the risk of severe influenza and have close contact with caregivers. Assessing influenza vaccination prevalence in caregivers and care recipients can provide important information about uptake.

OBJECTIVES

We aimed to (1) estimate influenza non-vaccination prevalence and (2) assess factors associated with non-vaccination among caregivers aged ≥ 45 years and among care recipients aged ≥ 65 years.

METHODS

We conducted an analysis of cross-sectional data from the Canadian Longitudinal Study on Aging collected 2015-2018. We estimated non-vaccination prevalence and reported adjusted odds ratios with 95% confidence intervals from logistic regression models to identify factors associated with non-vaccination among caregivers and care recipients.

RESULTS

Of the 23,500 CLSA participants who reported providing care, 41.4% (95% CI: 40.8%, 42.0%) reported not receiving influenza vaccine in the previous 12 months. Among the 5,559 participants who reported receiving professional or non-professional care, 24.8% (95% CI: 23.7%, 26.0%) reported not receiving influenza vaccine during the same period. For both groups, the odds of non-vaccination were higher for those who had not visited a family doctor in the past year, were daily smokers, and those who identified as non-white.

DISCUSSION

Identifying groups at high risk of severe influenza and their close contacts can inform public health efforts to reduce the risk of influenza. Our results suggest sub-optimal influenza vaccination uptake among caregivers and care recipients. Efforts are needed to increase influenza vaccination and highlight the direct and indirect benefits for caregiver-care recipient pairs.

CONCLUSION

The proportions of both caregivers and care recipients who had not been vaccinated for influenza was high, despite the benefits of vaccination. Influenza vaccination campaigns could target undervaccinated, high-risk groups to increase coverage.

Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19

With vaccination against COVID-19 stalled in some countries, increasing vaccine accessibility and distribution could help keep transmission under control. Here, we study the impact of reactive vaccination targeting schools and workplaces where cases are detected, with an agent-based model accounting for COVID-19 natural history, vaccine characteristics, demographics, behavioural changes and social distancing. In most scenarios, reactive vaccination leads to a higher reduction in cases compared with non-reactive strategies using the same number of doses. The reactive strategy could however be less effective than a moderate/high pace mass vaccination program if initial vaccination coverage is high or disease incidence is low, because few people would be vaccinated around each case. In case of flare-ups, reactive vaccination could better mitigate spread if it is implemented quickly, is supported by enhanced test-trace-isolate and triggers an increased vaccine uptake. These results provide key information to plan an adaptive vaccination rollout.

The authors use an agent-based model to investigate the potential of reactive vaccination strategies for COVID-19 outbreak mitigation. They find that distributing vaccines in schools and workplaces where cases are detected is more impactful than non-reactive strategies in a wide range of epidemic scenarios.

Rural prioritization may increase the impact of COVID-19 vaccines in a representative COVAX AMC country setting due to ongoing internal migration: A modeling study

How COVID-19 vaccine is distributed within low- and middle-income countries has received little attention outside of equity or logistical concerns but may ultimately affect campaign impact in terms of infections, severe cases, or deaths averted. In this study we examined whether subnational (urban-rural) prioritization may affect the cumulative two-year impact on disease transmission and burden of a vaccination campaign using an agent-based model of COVID-19 in a representative COVID-19 Vaccines Global Access (COVAX) Advanced Market Commitment (AMC) setting. We simulated a range of vaccination strategies that differed by urban-rural prioritization, age group prioritization, timing of introduction, and final coverage level. Urban prioritization averted more infections in only a narrow set of scenarios, when internal migration rates were low and vaccination was started by day 30 of an outbreak. Rural prioritization was the optimal strategy for all other scenarios, e.g., with higher internal migration rates or later start dates, due to the presence of a large immunological naive rural population. Among other factors, timing of the vaccination campaign was important to determining maximum impact, and delays as short as 30 days prevented larger campaigns from having the same impact as smaller campaigns that began earlier. The optimal age group for prioritization depended on choice of metric, as prioritizing older adults consistently averted more deaths across all of the scenarios. While guidelines exist for these latter factors, urban-rural allocation is an orthogonal factor that we predict to affect impact and warrants consideration as countries plan the scale-up of their vaccination campaigns.

Assessment of the efficacy of SARS-CoV-2 vaccines in non-human primate studies: a systematic review

Background: The outbreak of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) triggered the rapid and successful development of vaccines to help mitigate the effect of COVID-19 and circulation of the virus. Vaccine efficacy is often defined as capacity of vaccines to prevent (severe) disease. However, the efficacy to prevent transmission or infectiousness is equally important at a population level. This is not routinely assessed in clinical trials. Preclinical vaccine trials provide a wealth of information about the presence and persistence of viruses in different anatomical sites. Methods: We systematically reviewed all available preclinical SARS-CoV-2 candidate vaccine studies where non-human primates were challenged after vaccination (PROSPERO registration: CRD42021231199). We extracted the underlying data, and recalculated the reduction in viral shedding. We summarized the efficacy of  vaccines to reduce viral RNA shedding after challenge by standardizing and stratifying the results by different anatomical sites and diagnostic methods. We considered shedding of viral RNA as a proxy measure for infectiousness. Results: We found a marked heterogeneity between the studies in the experimental design and the assessment of the outcomes. The best performing vaccine candidate per study caused only low (6 out of 12 studies), or moderate (5 out of 12) reduction of viral genomic RNA, and low (5 out of 11 studies) or moderate (3 out of 11 studies) reduction of subgenomic RNA in the upper respiratory tract, as assessed with nasal samples. Conclusions: Since most of the tested vaccines only triggered a low or moderate reduction of viral RNA in the upper respiratory tract, we need to consider that most SARS-CoV-2 vaccines that protect against disease might not fully protect against infectiousness and vaccinated individuals might still contribute to SARS-CoV-2 transmission. Careful assessment of secondary attack rates from vaccinated individuals is warranted. Standardization in design and reporting of preclinical trials is necessary.

A Systematic Review of Campylobacter jejuni Vaccine Candidates for Chickens

Campylobacter jejuni infection linked to the consumption of contaminated poultry products is one of the leading causes of human enteric illness worldwide. Vaccination of chickens is one of the potential strategies that could be used to control C. jejuni colonization. To date, various C. jejuni vaccines using potential antigens have been evaluated, but a challenge in identifying the most effective formulation is the wide variability in vaccine efficacies reported. A systematic review was undertaken to compare C. jejuni vaccine studies. Based upon specific selection criteria eligible papers were identified and included in the analysis. Vaccine efficacy reported from different C. jejuni antigens, vaccine types, and vaccination regimens reported in these papers were reviewed. Our analysis shows that total outer membrane proteins and cysteine ABC transporter substrate-binding protein were among the most efficacious vaccine antigen candidates reported. This review also highlights the importance of the need for increased consistency in the way C. jejuni vaccine studies in poultry are designed and reported in order to be able to undertake a robust comparison of C. jejuni vaccine candidates.

What defines an efficacious COVID-19 vaccine? A review of the challenges assessing the clinical efficacy of vaccines against SARS-CoV-2

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused more than 1 million deaths in the first 6 months of the pandemic and huge economic and social upheaval internationally. An efficacious vaccine is essential to prevent further morbidity and mortality. Although some countries might deploy COVID-19 vaccines on the strength of safety and immunogenicity data alone, the goal of vaccine development is to gain direct evidence of vaccine efficacy in protecting humans against SARS-CoV-2 infection and COVID-19 so that manufacture of efficacious vaccines can be selectively upscaled. A candidate vaccine against SARS-CoV-2 might act against infection, disease, or transmission, and a vaccine capable of reducing any of these elements could contribute to disease control. However, the most important efficacy endpoint, protection against severe disease and death, is difficult to assess in phase 3 clinical trials. In this Review, we explore the challenges in assessing the efficacy of candidate SARS-CoV-2 vaccines, discuss the caveats needed to interpret reported efficacy endpoints, and provide insight into answering the seemingly simple question, "Does this COVID-19 vaccine work?"

Impact of influenza vaccine-modified infectivity on attack rate, case fatality ratio and mortality

Generally, vaccines are designed to provide protection against infection (susceptibility), disease (symptoms and transmissibility), and/or complications. In a recent study of influenza vaccination, it was observed that vaccinated yet infected individuals experienced increased transmission levels. In this paper, using a mathematical model of infection and transmission, we study the impact of vaccine-modified effects, including susceptibility and infectivity, on important epidemiological outcomes of an immunization program. The balance between vaccine-modified susceptibility, infectivity and recovery needed in preventing an influenza outbreak, or in mitigating the health outcomes of the outbreak is studied using the SIRV-type of disease transmission model. We also investigate the impact of influenza vaccination program on the infection risk of vaccinated and non-vaccinated individuals.

Unraveling R0: Considerations for Public Health Applications

We assessed public health use of R 0, the basic reproduction number, which estimates the speed at which a disease is capable of spreading in a population. These estimates are of great public health interest, as evidenced during the 2009 influenza A (H1N1) virus pandemic.

We reviewed methods commonly used to estimate R 0, examined their practical utility, and assessed how estimates of this epidemiological parameter can inform mitigation strategy decisions.

In isolation, R 0 is a suboptimal gauge of infectious disease dynamics across populations; other disease parameters may provide more useful information. Nonetheless, estimation of R 0 for a particular population is useful for understanding transmission in the study population. Considered in the context of other epidemiologically important parameters, the value of R 0 may lie in better understanding an outbreak and in preparing a public health response.

Estimation of seasonal influenza vaccine effectiveness using data collected in primary care in France: comparison of the test-negative design and the screening method

OBJECTIVES

We discussed which method between the test-negative design (TND) and the screening method (SM) could provide more robust real-time and end-of-season vaccine effectiveness (VE) estimates using data collected from routine influenza surveillance in primary care.

METHODS

We used data collected during two influenza seasons, 2014-15 and 2015-16. Using the SM, we estimated end-of-season VE in preventing medically attended influenza-like illness and laboratory-confirmed influenza among the population at risk. Using the TND, we estimated end-of-season VE in preventing influenza among both the general and the at-risk population. We estimated real-time VE using both methods.

RESULTS

For the SM, the overall adjusted end-of-season VE was 24% (95% confidence interval (CI), 16 to 32) and 12% (95% CI, -16 to 33) during season 2014-15, and 53% (95% CI, 44 to 60) and 47% (95% CI, 23 to 64) during season 2015-16, in preventing influenza-like illness and laboratory-confirmed influenza, respectively. For the TND, the overall adjusted end-of-season VE was -17% (95% CI, -79 to 24) and -38% (95% CI, -199 to 13) in 2014-15, and 10% (95% CI, -31 to 39) and 18% (95% CI, -33 to 50) in 2015-16, among the general and at-risk population, respectively. Real-time VE estimates obtained through the TND showed more variability across each season and lower precision than those estimated with the SM.

CONCLUSIONS

Although the worldwide use of the TND allows for comparison of overall VE estimates among countries, the SM performs better in providing robust real-time VE estimates among the population at risk.

Estimating Direct and Indirect Protective Effect of Influenza Vaccination in the United States

With influenza vaccination rates in the United States recently exceeding 45% of the population, it is important to understand the impact that vaccination is having on influenza transmission. In this study, we used a Bayesian modeling approach, combined with a simple dynamical model of influenza transmission, to estimate this impact. The combined framework synthesized evidence from a range of data sources relating to influenza transmission and vaccination in the United States. We found that, for seasonal epidemics, the number of infections averted ranged from 9.6 million in the 2006-2007 season (95% credible interval (CI): 8.7, 10.9) to 37.2 million (95% CI: 34.1, 39.6) in the 2012-2013 season. Expressed in relative terms, the proportion averted ranged from 20.8% (95% CI: 16.8, 24.3) of potential infections in the 2011-2012 season to 47.5% (95% CI: 43.7, 50.8) in the 2008-2009 season. The percentage averted was only 1.04% (95% CI: 0.15, 3.2) for the 2009 H1N1 pandemic, owing to the late timing of the vaccination program in relation to the pandemic in the Northern hemisphere. In the future, further vaccination coverage, as well as improved influenza vaccines (especially those offering better protection in the elderly), could have an even stronger effect on annual influenza epidemics.

Modelling community-control strategies to protect hospital resources during an influenza pandemic in Ottawa, Canada

BACKGROUND

A novel influenza virus has emerged to produce a global pandemic four times in the past one hundred years, resulting in millions of infections, hospitalizations and deaths. There is substantial uncertainty about when, where and how the next influenza pandemic will occur.

METHODS

We developed a novel mathematical model to chart the evolution of an influenza pandemic. We estimate the likely burden of future influenza pandemics through health and economic endpoints. An important component of this is the adequacy of existing hospital-resource capacity. Using a simulated population reflective of Ottawa, Canada, we model the potential impact of a future influenza pandemic under different combinations of pharmaceutical and non-pharmaceutical interventions.

RESULTS

There was substantial variation in projected pandemic impact and outcomes across intervention scenarios. In a population of 1.2 million, the illness attack rate ranged from 8.4% (all interventions) to 54.5% (no interventions); peak acute care hospital capacity ranged from 0.2% (all interventions) to 13.8% (no interventions); peak ICU capacity ranged from 1.1% (all interventions) to 90.2% (no interventions); and mortality ranged from 11 (all interventions) to 363 deaths (no interventions). Associated estimates of economic burden ranged from CAD $115 million to over $2 billion when extended mass school closure was implemented.

DISCUSSION

Children accounted for a disproportionate number of pandemic infections, particularly in household settings. Pharmaceutical interventions effectively reduced peak and total pandemic burden without affecting timing, while non-pharmaceutical measures delayed and attenuated pandemic wave progression. The timely implementation of a layered intervention bundle appeared likely to protect hospital resource adequacy in Ottawa. The adaptable nature of this model provides value in informing pandemic preparedness policy planning in situations of uncertainty, as scenarios can be updated in real time as more data become available. However-given the inherent uncertainties of model assumptions-results should be interpreted with caution.

Deriving effective vaccine allocation strategies for pandemic influenza: Comparison of an agent-based simulation and a compartmental model

Individuals are prioritized based on their risk profiles when allocating limited vaccine stocks during an influenza pandemic. Computationally expensive but realistic agent-based simulations and fast but stylized compartmental models are typically used to derive effective vaccine allocation strategies. A detailed comparison of these two approaches, however, is often omitted. We derive age-specific vaccine allocation strategies to mitigate a pandemic influenza outbreak in Seattle by applying derivative-free optimization to an agent-based simulation and also to a compartmental model. We compare the strategies derived by these two approaches under various infection aggressiveness and vaccine coverage scenarios. We observe that both approaches primarily vaccinate school children, however they may allocate the remaining vaccines in different ways. The vaccine allocation strategies derived by using the agent-based simulation are associated with up to 70% decrease in total cost and 34% reduction in the number of infections compared to the strategies derived by using the compartmental model. Nevertheless, the latter approach may still be competitive for very low and/or very high infection aggressiveness. Our results provide insights about potential differences between the vaccine allocation strategies derived by using agent-based simulations and those derived by using compartmental models.

Impact of vaccination on influenza mortality in children <5years old in Mexico

BACKGROUND

Influenza is a leading cause of respiratory tract infections among children. In Mexico, influenza vaccination was included in the National Immunization Program since 2004. However, the population health effects of the vaccine on children have not been fully described. Thus, we estimated the impact of influenza immunization in terms of mortality associated with this virus among children younger than 5years of age in Mexico.

METHODS

Mortality rates and years of life lost associated with influenza were estimated using national mortality register data for the period 1998-2012. Age-stratified and cause-specific mortality rates were estimated for all-cause, respiratory and cardiovascular events. Influenza-associated mortality was compared between the period prior to introduction of the influenza vaccine as part of the National Immunization Program (1998-2004) and the period thereafter (2004-2012).

RESULTS

During the 1998-2012 winter seasons, the average number of all-cause, respiratory and cardiovascular deaths attributable to influenza were 1186, 794 and 21, respectively. Influenza-associated mortality was higher prior to the vaccination period than after influenza was included in the immunization program for all-cause (mean 1660 vs. 780) and respiratory (mean 1063 vs. 563) mortality, but no reduction was seen for cardiovascular mortality. The proportion of all-cause and respiratory deaths attributable to influenza was significantly lower in the post-vaccine period compared with the pre-vaccine period (P<0.001), but no reduction was seen in the proportion of cardiovascular deaths. There was an average annual reduction of 66,558years of life lost in the post-vaccine compared with the pre-vaccine period.

CONCLUSION

The introduction of influenza vaccination within the Mexican Immunization Program was associated with a reduction in mortality rates attributable to this virus among children younger than 5years of age.

Mechanisms of replacement of circulating viruses by seasonal and pandemic influenza A viruses

BACKGROUND

Seasonal influenza causes annual epidemics by the accumulation of antigenic changes. Pandemic influenza occurs through a major antigenic change of the influenza A virus, which can originate from other hosts. Although new antigenic variants of the influenza A virus replace formerly circulating seasonal and pandemic viruses, replacement mechanisms remain poorly understood.

METHODS

A stochastic individual-based SEIR (susceptible-exposed-infectious-recovered) model with two viral strains (formerly circulating old strain and newly emerged strain) was developed for simulations to elucidate the replacement mechanisms.

RESULTS

Factors and conditions of virus and host populations affecting the replacement were identified. Replacement is more likely to occur in tropical regions than temperate regions. The magnitude of the ongoing epidemic by the old strain, herd immunity against the old strain, and timing of appearance of the new strain are not that important for replacement. It is probable that the frequency of replacement by a pandemic virus is higher than a seasonal virus because of the high initial susceptibility and high basic reproductive number of the pandemic virus.

CONCLUSIONS

The findings of this study on replacement mechanisms could lead to a better understanding of virus transmission dynamics and may possibly be helpful in establishing an effective strategy to mitigate the impact of seasonal and pandemic influenza.

Multi-epitope Models Explain How Pre-existing Antibodies Affect the Generation of Broadly Protective Responses to Influenza

The development of next-generation influenza vaccines that elicit strain-transcendent immunity against both seasonal and pandemic viruses is a key public health goal. Targeting the evolutionarily conserved epitopes on the stem of influenza’s major surface molecule, hemagglutinin, is an appealing prospect, and novel vaccine formulations show promising results in animal model systems. However, studies in humans indicate that natural infection and vaccination result in limited boosting of antibodies to the stem of HA, and the level of stem-specific antibody elicited is insufficient to provide broad strain-transcendent immunity. Here, we use mathematical models of the humoral immune response to explore how pre-existing immunity affects the ability of vaccines to boost antibodies to the head and stem of HA in humans, and, in particular, how it leads to the apparent lack of boosting of broadly cross-reactive antibodies to the stem epitopes. We consider hypotheses where binding of antibody to an epitope: (i) results in more rapid clearance of the antigen; (ii) leads to the formation of antigen-antibody complexes which inhibit B cell activation through Fcγ receptor-mediated mechanism; and (iii) masks the epitope and prevents the stimulation and proliferation of specific B cells. We find that only epitope masking but not the former two mechanisms to be key in recapitulating patterns in data. We discuss the ramifications of our findings for the development of vaccines against both seasonal and pandemic influenza.

The current influenza vaccine requires frequent updating in order to protect against small changes in the virus from one year to the next as well as larger changes associated with the emergence of new influenza strains from zoonotic reservoirs that cause pandemics. There is a considerable interest in developing “universal” vaccines that will boost immune responses to the conserved regions of the virus, in particular, to the stem region of the major virus surface molecule hemagglutinin (HA). However, recent data reveals that vaccination results in very limited boosting of antibodies to the stem of HA. We use mathematical models to explore different hypotheses that may explain why vaccination does not boost antibodies to the conserved parts of the virus. By confronting our models with the data from the human vaccination trials we found that the key mechanism preventing effective boosting of the responses to the stem of HA is masking of the stem by pre-existing antibodies developed during previous infections and vaccinations. We discuss how this masking effect could be overcome in a “universal” influenza vaccine.

Methods for Health Economic Evaluation of Vaccines and Immunization Decision Frameworks: A Consensus Framework from a European Vaccine Economics Community

BACKGROUND

Incremental cost-effectiveness and cost-utility analyses [health economic evaluations (HEEs)] of vaccines are routinely considered in decision making on immunization in various industrialized countries. While guidelines advocating more standardization of such HEEs (mainly for curative drugs) exist, several immunization-specific aspects (e.g. indirect effects or discounting approach) are still a subject of debate within the scientific community.

OBJECTIVE

The objective of this study was to develop a consensus framework for HEEs of vaccines to support the development of national guidelines in Europe.

METHODS

A systematic literature review was conducted to identify prevailing issues related to HEEs of vaccines. Furthermore, European experts in the field of health economics and immunization decision making were nominated and asked to select relevant aspects for discussion. Based on this, a workshop was held with these experts. Aspects on 'mathematical modelling', 'health economics' and 'decision making' were debated in group-work sessions (GWS) to formulate recommendations and/or--if applicable--to state 'pros' and 'contras'.

RESULTS

A total of 13 different aspects were identified for modelling and HEE: model selection, time horizon of models, natural disease history, measures of vaccine-induced protection, duration of vaccine-induced protection, indirect effects apart from herd protection, target population, model calibration and validation, handling uncertainty, discounting, health-related quality of life, cost components, and perspectives. For decision making, there were four aspects regarding the purpose and the integration of HEEs of vaccines in decision making as well as the variation of parameters within uncertainty analyses and the reporting of results from HEEs. For each aspect, background information and an expert consensus were formulated.

CONCLUSIONS

There was consensus that when HEEs are used to prioritize healthcare funding, this should be done in a consistent way across all interventions, including vaccines. However, proper evaluation of vaccines implies using tools that are not commonly used for therapeutic drugs. Due to the complexity of and uncertainties around vaccination, transparency in the documentation of HEEs and during subsequent decision making is essential.

One versus two doses: What is the best use of vaccine in an influenza pandemic?

Avian influenza A (H7N9), emerged in China in April 2013, sparking fears of a new, highly pathogenic, influenza pandemic. In addition, avian influenza A (H5N1) continues to circulate and remains a threat. Currently, influenza H7N9 vaccines are being tested to be stockpiled along with H5N1 vaccines. These vaccines require two doses, 21 days apart, for maximal protection. We developed a mathematical model to evaluate two possible strategies for allocating limited vaccine supplies: a one-dose strategy, where a larger number of people are vaccinated with a single dose, or a two-dose strategy, where half as many people are vaccinated with two doses. We prove that there is a threshold in the level of protection obtained after the first dose, below which vaccinating with two doses results in a lower illness attack rate than with the one-dose strategy; but above the threshold, the one-dose strategy would be better. For reactive vaccination, we show that the optimal use of vaccine depends on several parameters, with the most important one being the level of protection obtained after the first dose. We describe how these vaccine dosing strategies can be integrated into effective pandemic control plans.

Estimating dynamic transmission model parameters for seasonal influenza by fitting to age and season-specific influenza-like illness incidence

Dynamic transmission models are essential to design and evaluate control strategies for airborne infections. Our objective was to develop a dynamic transmission model for seasonal influenza allowing to evaluate the impact of vaccinating specific age groups on the incidence of infection, disease and mortality. Projections based on such models heavily rely on assumed 'input' parameter values. In previous seasonal influenza models, these parameter values were commonly chosen ad hoc, ignoring between-season variability and without formal model validation or sensitivity analyses. We propose to directly estimate the parameters by fitting the model to age-specific influenza-like illness (ILI) incidence data over multiple influenza seasons. We used a weighted least squares (WLS) criterion to assess model fit and applied our method to Belgian ILI data over six influenza seasons. After exploring parameter importance using symbolic regression, we evaluated a set of candidate models of differing complexity according to the number of season-specific parameters. The transmission parameters (average R0, seasonal amplitude and timing of the seasonal peak), waning rates and the scale factor used for WLS optimization, influenced the fit to the observed ILI incidence the most. Our results demonstrate the importance of between-season variability in influenza transmission and our estimates are in line with the classification of influenza seasons according to intensity and vaccine matching.

Household transmissibility of avian influenza A (H7N9) virus, China, February to May 2013 and October 2013 to March 2014

To study human-to-human transmissibility of the avian influenza A (H7N9) virus in China, household contact information was collected for 125 index cases during the spring wave (February to May 2013), and for 187 index cases during the winter wave (October 2013 to March 2014). Using a statistical model, we found evidence for human-to-human transmission, but such transmission is not sustainable. Under plausible assumptions about the natural history of disease and the relative transmission frequencies in settings other than household, we estimate the household secondary attack rate (SAR) among humans to be 1.4% (95% CI: 0.8 to 2.3), and the basic reproductive number R0 to be 0.08 (95% CI: 0.05 to 0.13). The estimates range from 1.3% to 2.2% for SAR and from 0.07 to 0.12 for R0 with reasonable changes in the assumptions. There was no significant change in the human-to-human transmissibility of the virus between the two waves, although a minor increase was observed in the winter wave. No sex or age difference in the risk of infection from a human source was found. Human-to-human transmissibility of H7N9 continues to be limited, but it needs to be closely monitored for potential increase via genetic reassortment or mutation.

Seasonal influenza vaccination and technologies

Seasonal influenza is a serious respiratory illness that causes annual worldwide epidemics resulting in significant morbidity and mortality. Influenza pandemics occur about every 40 yrs, and may carry a greater burden of illness and death than seasonal influenza. Both seasonal influenza and pandemic influenza have profound economic consequences. The combination of current vaccine efficacy and viral antigenic drifts and shifts necessitates annual vaccination. New manufacturing technologies in influenza vaccine development employ cell culture and recombinant techniques. Both allow more rapid vaccine creation and production. In the past 5 years, brisk, highly creative activity in influenza vaccine research and development has begun. New vaccine technologies and vaccination strategies are addressing the need for viable alternatives to egg production methods and improved efficacy. At present, stubborn problems of sub-optimal efficacy and the need for annual immunization persist. There is an obvious need for more efficacious vaccines and improved vaccination strategies to make immunization easier for providers and patients. Mitigating this serious annual health threat remains an important public health priority.

Positive network assortativity of influenza vaccination at a high school: implications for outbreak risk and herd immunity

Schools are known to play a significant role in the spread of influenza. High vaccination coverage can reduce infectious disease spread within schools and the wider community through vaccine-induced immunity in vaccinated individuals and through the indirect effects afforded by herd immunity. In general, herd immunity is greatest when vaccination coverage is highest, but clusters of unvaccinated individuals can reduce herd immunity. Here, we empirically assess the extent of such clustering by measuring whether vaccinated individuals are randomly distributed or demonstrate positive assortativity across a United States high school contact network. Using computational models based on these empirical measurements, we further assess the impact of assortativity on influenza disease dynamics. We found that the contact network was positively assortative with respect to influenza vaccination: unvaccinated individuals tended to be in contact more often with other unvaccinated individuals than with vaccinated individuals, and these effects were most pronounced when we analyzed contact data collected over multiple days. Of note, unvaccinated males contributed substantially more than unvaccinated females towards the measured positive vaccination assortativity. Influenza simulation models using a positively assortative network resulted in larger average outbreak size, and outbreaks were more likely, compared to an otherwise identical network where vaccinated individuals were not clustered. These findings highlight the importance of understanding and addressing heterogeneities in seasonal influenza vaccine uptake for prevention of large, protracted school-based outbreaks of influenza, in addition to continued efforts to increase overall vaccine coverage.

Characteristics of vaccine failures in a randomized placebo-controlled trial of inactivated influenza vaccine in children

BACKGROUND

Infections occurring among vaccinated persons (vaccine failures) are known to occur in vaccines with imperfect efficacy. Failures among vaccinated children who were infected with vaccine-matched influenza B virus strain have not been adequately characterized.

METHODS

Taking advantage of a randomized controlled trial of trivalent seasonal influenza vaccine (TIV), the viral shedding and clinical symptoms associated with reverse transcriptase polymerase chain reaction-confirmed influenza B infection and serum hemaggluttination inhibiting antibody response to vaccine were compared between children 6 and 17 years receiving TIV and placebo.

RESULTS

Vaccine failures were observed to show lower antibody response to TIV compared with other vaccine recipients. We did not find any evidence that vaccination reduced the severity or duration of clinical symptoms of reverse transcriptase polymerase chain reaction-confirmed vaccine-matched influenza B infections. Vaccination was not observed to alter viral load or shedding duration.

CONCLUSIONS

TIV was not observed to ameliorate clinical symptoms or viral shedding among vaccine failures compared with infected placebo recipients. Lower antibody response might have explained vaccine failure and also lack of effect in reducing clinical symptoms and viral shedding upon infection. Our results are based on a randomized controlled trial of split virus inactivated vaccine and may not be applicable to other vaccine types. Further studies in vaccine failure among children will be important in future vaccine development.

Dynamics of infectious diseases

Modern infectious disease epidemiology has a strong history of using mathematics both for prediction and to gain a deeper understanding. However the study of infectious diseases is a highly interdisciplinary subject requiring insights from multiple disciplines, in particular a biological knowledge of the pathogen, a statistical description of the available data and a mathematical framework for prediction. Here we begin with the basic building blocks of infectious disease epidemiology--the SIS and SIR type models--before considering the progress that has been made over the recent decades and the challenges that lie ahead. Throughout we focus on the understanding that can be developed from relatively simple models, although accurate prediction will inevitably require far greater complexity beyond the scope of this review. In particular, we focus on three critical aspects of infectious disease models that we feel fundamentally shape their dynamics: heterogeneously structured populations, stochasticity and spatial structure. Throughout we relate the mathematical models and their results to a variety of real-world problems.

Unraveling R0: considerations for public health applications

We assessed public health use of R0, the basic reproduction number, which estimates the speed at which a disease is capable of spreading in a population. These estimates are of great public health interest, as evidenced during the 2009 influenza A (H1N1) virus pandemic. We reviewed methods commonly used to estimate R0, examined their practical utility, and assessed how estimates of this epidemiological parameter can inform mitigation strategy decisions. In isolation, R0 is a suboptimal gauge of infectious disease dynamics across populations; other disease parameters may provide more useful information. Nonetheless, estimation of R0 for a particular population is useful for understanding transmission in the study population. Considered in the context of other epidemiologically important parameters, the value of R0 may lie in better understanding an outbreak and in preparing a public health response.

The impact of model building on the transmission dynamics under vaccination: observable (symptom-based) versus unobservable (contagiousness-dependent) approaches

Background

The way we formulate a mathematical model of an infectious disease to capture symptomatic and asymptomatic transmission can greatly influence the likely effectiveness of vaccination in the presence of vaccine effect for preventing clinical illness. The present study aims to assess the impact of model building strategy on the epidemic threshold under vaccination.

Methodology/Principal Findings

We consider two different types of mathematical models, one based on observable variables including symptom onset and recovery from clinical illness (hereafter, the “observable model”) and the other based on unobservable information of infection event and infectiousness (the “unobservable model”). By imposing a number of modifying assumptions to the observable model, we let it mimic the unobservable model, identifying that the two models are fully consistent only when the incubation period is identical to the latent period and when there is no pre-symptomatic transmission. We also computed the reproduction numbers with and without vaccination, demonstrating that the data generating process of vaccine-induced reduction in symptomatic illness is consistent with the observable model only and examining how the effective reproduction number is differently calculated by two models.

Conclusions

To explicitly incorporate the vaccine effect in reducing the risk of symptomatic illness into the model, it is fruitful to employ a model that directly accounts for disease progression. More modeling studies based on observable epidemiological information are called for.

Optimal vaccine allocation for the early mitigation of pandemic influenza

With new cases of avian influenza H5N1 (H5N1AV) arising frequently, the threat of a new influenza pandemic remains a challenge for public health. Several vaccines have been developed specifically targeting H5N1AV, but their production is limited and only a few million doses are readily available. Because there is an important time lag between the emergence of new pandemic strain and the development and distribution of a vaccine, shortage of vaccine is very likely at the beginning of a pandemic. We coupled a mathematical model with a genetic algorithm to optimally and dynamically distribute vaccine in a network of cities, connected by the airline transportation network. By minimizing the illness attack rate (i.e., the percentage of people in the population who become infected and ill), we focus on optimizing vaccine allocation in a network of 16 cities in Southeast Asia when only a few million doses are available. In our base case, we assume the vaccine is well-matched and vaccination occurs 5 to 10 days after the beginning of the epidemic. The effectiveness of all the vaccination strategies drops off as the timing is delayed or the vaccine is less well-matched. Under the best assumptions, optimal vaccination strategies substantially reduced the illness attack rate, with a maximal reduction in the attack rate of 85%. Furthermore, our results suggest that cooperative strategies where the resources are optimally distributed among the cities perform much better than the strategies where the vaccine is equally distributed among the network, yielding an illness attack rate 17% lower. We show that it is possible to significantly mitigate a more global epidemic with limited quantities of vaccine, provided that the vaccination campaign is extremely fast and it occurs within the first weeks of transmission.

Vaccination and clinical severity: is the effectiveness of contact tracing and case isolation hampered by past vaccination?

While contact tracing and case isolation are considered as the first choice of interventions against a smallpox bioterrorist event, their effectiveness under vaccination is questioned, because not only susceptibility of host and infectiousness of case but also the risk of severe clinical manifestations among cases is known to be reduced by vaccine-induced immunity, thereby potentially delaying the diagnosis and increasing mobility among vaccinated cases. We employed a multi-type stochastic epidemic model, aiming to assess the feasibility of contact tracing and case isolation in a partially vaccinated population and identify data gaps. We computed four epidemiological outcome measures, i.e., (i) the threshold of a major epidemic under the interventions; (ii) the expected total number of cases; (iii) the probability of extinction, and (iv) the expected duration of an outbreak, demonstrating that all of these outcomes critically depend on the clinical impact of past vaccination on the diagnosis and movement of vaccinated cases. We discuss that, even in the absence of smallpox in the present day, one should consider the way to empirically quantify the delay in case detection and an increase in the frequency of contacts among previously vaccinated cases compared to unvaccinated during the early stage of an epidemic so that the feasibility of contact tracing and case isolation in a vaccinated population can be explicitly assessed.

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.

Under-explored assumptions in influenza vaccination models: implications for the universal vaccination of children

The aim of this study was to explore several important (but uncertain) assumptions in influenza models which affect the estimated benefits of vaccination programs. We combined consideration of these factors with the seasonal variability of influenza transmissibility to gain a better understanding of how they may influence influenza control efforts. As our case study, we considered the potential impact of universal seasonal childhood vaccination in Australia using a simplified age-stratified Susceptible Exposed Infectious Recovered (SEIR) model to simulate influenza epidemics and the impact of vaccination. We found that the choice of vaccine efficacy model was influential in determining the impact of vaccination. This choice interacted with other model assumption such as those around the infectiousness of asymptomatic cases and the match of the vaccine to the circulating strains. The methodological approach used to estimate influenza hospitalisations was also highly influential. Our study highlights the role that key modelling assumptions play when estimating the impact of vaccination against influenza.

Impact of cross-protective vaccines on epidemiological and evolutionary dynamics of influenza

Large-scale immunization has profoundly impacted control of many infectious diseases such as measles and smallpox because of the ability of vaccination campaigns to maintain long-term herd immunity and, hence, indirect protection of the unvaccinated. In the case of human influenza, such potential benefits of mass vaccination have so far proved elusive. The central difficulty is a considerable viral capacity for immune escape; new pandemic variants, as well as viral escape mutants in seasonal influenza, compromise the buildup of herd immunity from natural infection or deployment of current vaccines. Consequently, most current influenza vaccination programs focus mainly on protection of specific risk groups, rather than mass prophylactic protection. Here, we use epidemiological models to show that emerging vaccine technologies, aimed at broad-spectrum protection, could qualitatively alter this picture. We demonstrate that sustained immunization with such vaccines could--through potentially lowering transmission rates and improving herd immunity--significantly moderate both influenza pandemic and seasonal epidemics. More subtly, phylodynamic models indicate that widespread cross-protective immunization could slow the antigenic evolution of seasonal influenza; these effects have profound implications for a transition to mass vaccination strategies against human influenza, and for the management of antigenically variable viruses in general.

Efficacy and effectiveness of influenza vaccines: a systematic review and meta-analysis

BACKGROUND

No published meta-analyses have assessed efficacy and effectiveness of licensed influenza vaccines in the USA with sensitive and highly specific diagnostic tests to confirm influenza.

METHODS

We searched Medline for randomised controlled trials assessing a relative reduction in influenza risk of all circulating influenza viruses during individual seasons after vaccination (efficacy) and observational studies meeting inclusion criteria (effectiveness). Eligible articles were published between Jan 1, 1967, and Feb 15, 2011, and used RT-PCR or culture for confirmation of influenza. We excluded some studies on the basis of study design and vaccine characteristics. We estimated random-effects pooled efficacy for trivalent inactivated vaccine (TIV) and live attenuated influenza vaccine (LAIV) when data were available for statistical analysis (eg, at least three studies that assessed comparable age groups).

FINDINGS

We screened 5707 articles and identified 31 eligible studies (17 randomised controlled trials and 14 observational studies). Efficacy of TIV was shown in eight (67%) of the 12 seasons analysed in ten randomised controlled trials (pooled efficacy 59% [95% CI 51-67] in adults aged 18-65 years). No such trials met inclusion criteria for children aged 2-17 years or adults aged 65 years or older. Efficacy of LAIV was shown in nine (75%) of the 12 seasons analysed in ten randomised controlled trials (pooled efficacy 83% [69-91]) in children aged 6 months to 7 years. No such trials met inclusion criteria for children aged 8-17 years. Vaccine effectiveness was variable for seasonal influenza: six (35%) of 17 analyses in nine studies showed significant protection against medically attended influenza in the outpatient or inpatient setting. Median monovalent pandemic H1N1 vaccine effectiveness in five observational studies was 69% (range 60-93).

INTERPRETATION

Influenza vaccines can provide moderate protection against virologically confirmed influenza, but such protection is greatly reduced or absent in some seasons. Evidence for protection in adults aged 65 years or older is lacking. LAIVs consistently show highest efficacy in young children (aged 6 months to 7 years). New vaccines with improved clinical efficacy and effectiveness are needed to further reduce influenza-related morbidity and mortality.

FUNDING

Alfred P Sloan Foundation.

Critical immune and vaccination thresholds for determining multiple influenza epidemic waves

Previous influenza pandemics (1918, 1957, and 1968) have all had multiple waves. The 2009 pandemic influenza A (H1N1) (pandemic H1N1) started in April 2009 and was followed, in the United States (US) and temperate Northern Hemisphere, by a second wave during the fall of 2009. The ratio of susceptible and immune individuals in a population at the end of a wave determines the potential and magnitude of a subsequent wave. As influenza vaccines are not completely protective, there was a combined immunity in the population at the beginning of 2010 (due to vaccination and due to previous natural infection), and it was uncertain if this mixture of herd immunity was enough to prevent a third wave of pandemic influenza during the winter of 2010. Motivated by this problem, we developed a mathematical deterministic two-group epidemic model with vaccination and calibrated it for the 2009 pandemic H1N1. Then, applying methods from mathematical epidemiology we developed a scheme that allowed us to determine critical thresholds for vaccine-induced and natural immunity that would prevent the spread of influenza. Finally, we estimated the level of combined immunity in the US during winter 2010. Our results suggest that a third wave was unlikely if the basic reproduction number R(0) were below 1.6, plausible if the original R(0) was 1.6, and likely if the original R(0) was 1.8 or higher. Given that the estimates for the basic reproduction number for pandemic influenza place it in the range between 1.4 and 1.6 (Bacaer and Ait Dads, 2011; Fraser et al., 2009; Munayco et al., 2009; Pourbohloul et al., 2009; Tuite et al., 2010; White et al., 2009; Yang et al., 2009), our approach accurately predicted the absence of a third wave of influenza in the US during the winter of 2010. We also used this scheme to accurately predict the second wave of pandemic influenza in London and the West Midlands, UK during the fall of 2009.

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.

The global transmission and control of influenza

New strains of influenza spread around the globe via the movement of infected individuals. The global dynamics of influenza are complicated by different patterns of influenza seasonality in different regions of the world. We have released an open-source stochastic mathematical model of the spread of influenza across 321 major, strategically located cities of the world. Influenza is transmitted between cities via infected airline passengers. Seasonality is simulated by increasing the transmissibility in each city at the times of the year when influenza has been observed to be most prevalent. The spatiotemporal spread of pandemic influenza can be understood through clusters of global transmission and links between them, which we identify using the epidemic percolation network (EPN) of the model. We use the model to explain the observed global pattern of spread for pandemic influenza A(H1N1) 2009–2010 (pandemic H1N1 2009) and to examine possible global patterns of spread for future pandemics depending on the origin of pandemic spread, time of year of emergence, and basic reproductive number (). We also use the model to investigate the effectiveness of a plausible global distribution of vaccine for various pandemic scenarios. For pandemic H1N1 2009, we show that the biggest impact of vaccination was in the temperate northern hemisphere. For pandemics starting in the temperate northern hemisphere in May or April, vaccination would have little effect in the temperate southern hemisphere and a small effect in the tropics. With the increasing interconnectedness of the world's population, we must take a global view of infectious disease transmission. Our open-source, computationally simple model can help public health officials plan for the next pandemic as well as deal with interpandemic influenza.

Comparison of antibody and T-cell responses elicited by licensed inactivated- and live-attenuated influenza vaccines against H3N2 hemagglutinin

T cells are being increasingly recognized as a significant component of influenza-specific immune responses in humans. Although an inactivated- and a live-attenuated influenza vaccine are now licensed for use in humans, their comparative ability to elicit T-cell responses against influenza is not well understood. Using the rapidly evolving H3N2 hemagglutinin (HA) as an antigenic model, we compared immune responses elicited by the trivalent inactivated influenza vaccine (TIV) and the live-attenuated influenza vaccine (LAIV) in a cohort of healthy adults 18-49 years of age. TIV elicited higher geometrical mean antibody titers than LAIV, whereas, LAIV elicited superior T-cell responses. Importantly, LAIV elicited higher magnitude T-cell responses toward the rapidly drifting variant region of HA that is prone to escape from antibody responses. These results have important implications for the deployment of influenza vaccines in years of antigenic mismatch and shift.

Diagnosis and antiviral intervention strategies for mitigating an influenza epidemic

BACKGROUND

Many countries have amassed antiviral stockpiles for pandemic preparedness. Despite extensive trial data and modelling studies, it remains unclear how to make optimal use of antiviral stockpiles within the constraints of healthcare infrastructure. Modelling studies informed recommendations for liberal antiviral distribution in the pandemic phase, primarily to prevent infection, but failed to account for logistical constraints clearly evident during the 2009 H1N1 outbreaks. Here we identify optimal delivery strategies for antiviral interventions accounting for logistical constraints, and so determine how to improve a strategy's impact.

METHODS AND FINDINGS

We extend an existing SEIR model to incorporate finite diagnostic and antiviral distribution capacities. We evaluate the impact of using different diagnostic strategies to decide to whom antivirals are delivered. We then determine what additional capacity is required to achieve optimal impact. We identify the importance of sensitive and specific case ascertainment in the early phase of a pandemic response, when the proportion of false-positive presentations may be high. Once a substantial percentage of ILI presentations are caused by the pandemic strain, identification of cases for treatment on syndromic grounds alone results in a greater potential impact than a laboratory-dependent strategy. Our findings reinforce the need for a decentralised system capable of providing timely prophylaxis.

CONCLUSIONS

We address specific real-world issues that must be considered in order to improve pandemic preparedness policy in a practical and methodologically sound way. Provision of antivirals on the scale proposed for an effective response is infeasible using traditional public health outbreak management and contact tracing approaches. The results indicate to change the transmission dynamics of an influenza epidemic with an antiviral intervention, a decentralised system is required for contact identification and prophylaxis delivery, utilising a range of existing services and infrastructure in a "whole of society" response.

From the patient perspective: the economic value of seasonal and H1N1 influenza vaccination

Although studies have suggested that a patient's perceived cost-benefit of a medical intervention could affect his or her utilization of the intervention, the economic value of influenza vaccine from the patient's perspective remains unclear. Therefore, we developed a stochastic decision analytic computer model representing an adult's decision of whether to get vaccinated. Different scenarios explored the impact of the patient being insured versus uninsured, influenza attack rate, vaccine administration costs and vaccination time costs. Results indicated that the cost of avoiding influenza was fairly low (with one driver being required vaccination time). To encourage vaccination, decision makers may want to focus on ways to reduce this time, such as vaccinating at work, churches, or other normally frequented locations.

Modelling the strategies for age specific vaccination scheduling during influenza pandemic outbreaks

Finding optimal policies to reduce the morbidity and mortality of the ongoing pandemic is a top public health priority. Using a compartmental model with age structure and vaccination status, we examined the effect of age specific scheduling of vaccination during a pandemic influenza outbreak, when there is a race between the vaccination campaign and the dynamics of the pandemic. Our results agree with some recent studies on that age specificity is paramount to vaccination planning. However, little is known about the effectiveness of such control measures when they are applied during the outbreak. Comparing five possible strategies, we found that age specific scheduling can have a huge impact on the outcome of the epidemic. For the best scheme, the attack rates were up to 10% lower than for other strategies. We demonstrate the importance of early start of the vaccination campaign, since ten days delay may increase the attack rate by up to 6%. Taking into account the delay between developing immunity and vaccination is a key factor in evaluating the impact of vaccination campaigns. We provide a general framework which will be useful for the next pandemic waves as well.

Projection of seasonal influenza severity from sequence and serological data

Severity of seasonal influenza A epidemics is related to the antigenic novelty of the predominant viral strains circulating each year. Support for a strong correlation between epidemic severity and antigenic drift comes from infectious challenge experiments on vaccinated animals and human volunteers, field studies of vaccine efficacy, prospective studies of subjects with laboratory-confirmed prior infections, and analysis of the connection between drift and severity from surveillance data. We show that, given data on the antigenic and sequence novelty of the hemagglutinin protein of clinical isolates of H3N2 virus from a season along with the corresponding data from prior seasons, we can accurately predict the influenza severity for that season. This model therefore provides a framework for making projections of the severity of the upcoming season using assumptions based on viral isolates collected in the current season. Our results based on two independent data sets from the US and Hong Kong suggest that seasonal severity is largely determined by the novelty of the hemagglutinin protein although other factors, including mutations in other influenza genes, co-circulating pathogens and weather conditions, might also play a role. These results should be helpful for the control of seasonal influenza and have implications for improvement of influenza surveillance.