Optimal control for pandemic influenza: The role of limited antiviral treatment and isolation

The implementation of optimal control strategies involving antiviral treatment and/or isolation measures can reduce significantly the number of clinical cases of influenza. Pandemic-level control measures must be carefully assessed specially in resource-limited situations. A model for the transmission dynamics of influenza is used to evaluate the impact of isolation and/or antiviral drug delivery measures during an influenza pandemic. Five pre-selected control strategies involving antiviral treatment and isolation are tested under the “unlimited” resource assumption followed by an exploration of the impact of these “optimal” policies when resources are limited in the context of a 1918-type influenza pandemic scenario. The implementation of antiviral treatment at the start of a pandemic tends to reduce the magnitude of epidemic peaks, spreading the maximal impact of an outbreak over an extended window in time. Hence, the controls’ timing and intensity can reduce the pressures placed on the health care infrastructure by a pandemic reducing the stress put on the system during epidemic peaks. The role of isolation strategies is highlighted in this study particularly when access to antiviral resources is limited.     

Epidemiologic and economic effectiveness of school closure during influenza epidemics and pandemics

Epidemiologic and economic effectiveness of school closure during influenza epidemics and pandemics is discussed. Optimal effect of school closure is observed when this measure is taken at the start of the epidemic or pandemic and for a sufficiently long time. School closure during high morbidity among schoolchildren, in the middle (at the peak) and by the end of epidemic or pandemic does not influence significantly the spread of influenza or morbidity. Significant economic losses and other negative consequences of school closure are noted. School closure may be the most appropriate during the emergence of influenza pandemic when the pandemic vaccine is not yet available, however timely mass immunization of schoolchildren against influenza may be a more appropriate measure than school closure for the reduction of influenza morbidity and spread during seasonal influenza epidemics.     

Public health strategies for distribution of influenza vaccine during an influenza pandemic

In order to consider the ethical issues around vaccine distribution during an influenza pandemic, it is critical to have an understanding of the role of influenza vaccine in a pandemic, the rate at which vaccine is likely to be come available, who will likely produce and "own" the vaccine, how vaccine distribution and administration might be accomplished, and which are the groups that might be deemed highest priority to be vaccinated against influenza. The United States and Connecticut have been considering the more challenging of these issues and have learned from Canada, which previously discussed and made decisions on the challenges related to vaccine distribution. Although there is still some critical advance thinking that needs to be done, planning for the response to an influenza pandemic is now at an advanced stage. The keys to preparedness at this stage are to be aware of the vaccine distribution options, to know the benefits and limitations of each option, and to be flexible but nimble in dealing with a real pandemic.     

Molecular basis of the variability of epidemic strains of human influenza viruses

The data from literature and authors own studies are reviewed on variability of human influenza viral strains, isolated during the same epidemic season in different periods of pandemic cycle. The data obtained indicate that variability of epidemic strains of human influenza virus deals with the genes coding for outer membrane proteins (hemagglutinin and neuraminidase) as well as nonglycosylated proteins. Circulation of a number of viral variants of the same serotype, differing in antigenic specificity of outer membrane proteins or in the genes coding for nonglycosylated proteins was registered during one and the same season of one epidemic. During circulation of viral variants of the same serotype recombination may take place. Heterogeneity of viral strains circulating during different epidemic seasons of the same pandemic cycle is different. The possible mechanisms of development of the new epidemic variants of human influenza virus are discussed.     

Coinfection can trigger multiple pandemic waves

Sequences of epidemic waves have been observed in past influenza pandemics, such as the Spanish influenza. Possible explanations may be sought either in mechanisms altering the structure of the network of contacts, such as those induced by changes in the rates of movement of people or by public health measures, or in the genetic drift of the influenza virus, since the appearance of new strains can reduce or eliminate herd immunity. The pandemic outbreaks may also be influenced by coinfection with other acute respiratory infections (ARI) that increase transmissibility of influenza virus (by coughing, sneezing, running nose). In fact, some viruses (e.g., Rhinovirus and Adenovirus) have been found to induce “clouds” of bacteria and increase the transmissibility of Staphylococcus aureus. Moreover, Rhinovirus and Adenovirus were detected in patients during past pandemics, and their presence is linked to superspreading events. In this paper, by assuming increased transmissibility in coinfected individuals, we propose and study a model where multiple pandemic waves are triggered by coinfection with ARI. The model agrees well with mortality excess data during the 1918 pandemic influenza, thereby providing indications for potential pandemic mitigation.     

Transmission dynamics of the great influenza pandemic of 1918 in Geneva, Switzerland: Assessing the effects of hypothetical interventions

Recurrent outbreaks of the avian H5N1 influenza virus in Asia represent a constant global pandemic threat. We characterize and evaluate hypothetical public health measures during the 1918 influenza pandemic in the Canton of Geneva, Switzerland. The transmission rate, the recovery rate, the diagnostic rate, the relative infectiousness of asymptomatic cases, and the proportion of clinical cases are estimated through least-squares fitting of the model to epidemic curve data of the cumulative number of hospital notifications. The latent period and the case fatality proportion are taken from published literature. We determine the variance and identifiability of model parameters via a simulation study. Our epidemic model agrees well with the observed epidemic data. We estimate the basic reproductive number for the spring wave R1;=1.49 (95% CI: 1.45-1.53) and the reproductive number for the fall wave R2;=3.75 (95% CI: 3.57-3.93). In addition, we estimate the clinical reporting for these two waves to be 59.7% (95% CI: 55.7-63.7) and 83% (95% CI: 79-87). We surmise that the lower reporting in the first wave can be explained by a lack of initial awareness of the epidemic and the relative higher severity of the symptoms experienced during the fall wave. We found that effective isolation measures in hospital clinics at best would only ensure control with probability 0.87 while reducing the transmission rate by >76.5% guarantees stopping an epidemic.     

Mitigation strategies for pandemic influenza A: balancing conflicting policy objectives

Mitigation of a severe influenza pandemic can be achieved using a range of interventions to reduce transmission. Interventions can reduce the impact of an outbreak and buy time until vaccines are developed, but they may have high social and economic costs. The non-linear effect on the epidemic dynamics means that suitable strategies crucially depend on the precise aim of the intervention. National pandemic influenza plans rarely contain clear statements of policy objectives or prioritization of potentially conflicting aims, such as minimizing mortality (depending on the severity of a pandemic) or peak prevalence or limiting the socio-economic burden of contact-reducing interventions. We use epidemiological models of influenza A to investigate how contact-reducing interventions and availability of antiviral drugs or pre-pandemic vaccines contribute to achieving particular policy objectives. Our analyses show that the ideal strategy depends on the aim of an intervention and that the achievement of one policy objective may preclude success with others, e.g., constraining peak demand for public health resources may lengthen the duration of the epidemic and hence its economic and social impact. Constraining total case numbers can be achieved by a range of strategies, whereas strategies which additionally constrain peak demand for services require a more sophisticated intervention. If, for example, there are multiple objectives which must be achieved prior to the availability of a pandemic vaccine (i.e., a time-limited intervention), our analysis shows that interventions should be implemented several weeks into the epidemic, not at the very start. This observation is shown to be robust across a range of constraints and for uncertainty in estimates of both R(0) and the timing of vaccine availability. These analyses highlight the need for more precise statements of policy objectives and their assumed consequences when planning and implementing strategies to mitigate the impact of an influenza pandemic.     

“Pandemic Public Health Paradox”: Time Series Analysis of the 2009/10 Influenza A / H1N1 Epidemiology,Media Attention,Risk Perception and Public Reactions in 5 European Countries

In 2009, influenza A H₁N₁ caused the first pandemic of the 21^st century. Although a vaccine against this influenza subtype was offered before or at the onset of the second epidemic wave that caused most of the fatal cases in Europe, vaccination rates for that season were lower than expected. We propose that the contradiction between high risk of infection and low use of available prevention measures represents a pandemic public health paradox. This research aims for a better understanding of this paradox by exploring the time-dependent interplay among changing influenza epidemiology, media attention, pandemic control measures, risk perception and public health behavior among five European countries (Czech Republic, Denmark, Germany, Spain and the UK). Findings suggest that asynchronicity between media curves and epidemiological curves may potentially explain the pandemic public health paradox; media attention for influenza A H₁N₁ in Europe declined long before the epidemic reached its peak, and public risk perceptions and behaviors may have followed media logic, rather than epidemiological logic.     

Evolutionary dynamics of local pandemic H1N1/2009 influenza virus lineages revealed by whole-genome analysis

Virus gene sequencing and phylogenetics can be used to study the epidemiological dynamics of rapidly evolving viruses. With complete genome data, it becomes possible to identify and trace individual transmission chains of viruses such as influenza virus during the course of an epidemic. Here we sequenced 153 pandemic influenza H1N1/09 virus genomes from United Kingdom isolates from the first (127 isolates) and second (26 isolates) waves of the 2009 pandemic and used their sequences, dates of isolation, and geographical locations to infer the genetic epidemiology of the epidemic in the United Kingdom. We demonstrate that the epidemic in the United Kingdom was composed of many cocirculating lineages, among which at least 13 were exclusively or predominantly United Kingdom clusters. The estimated divergence times of two of the clusters predate the detection of pandemic H1N1/09 virus in the United Kingdom, suggesting that the pandemic H1N1/09 virus was already circulating in the United Kingdom before the first clinical case. Crucially, three clusters contain isolates from the second wave of infections in the United Kingdom, two of which represent chains of transmission that appear to have persisted within the United Kingdom between the first and second waves. This demonstrates that whole-genome analysis can track in fine detail the behavior of individual influenza virus lineages during the course of a single epidemic or pandemic.     

Influenza A/H1N1 2009 Pandemic and Respiratory Virus Infections,Beijing, 2009–2010

To determine the role of the pandemic influenza A/H1N1 2009 (A/H1N1 2009pdm) in acute respiratory tract infections (ARTIs) and its impact on the epidemic of seasonal influenza viruses and other common respiratory viruses, nasal and throat swabs taken from 7,776 patients with suspected viral ARTIs from 2006 through 2010 in Beijing, China were screened by real-time PCR for influenza virus typing and subtyping and by multiplex or single PCR tests for other common respiratory viruses. We observed a distinctive dual peak pattern of influenza epidemic during the A/H1N1 2009pdm in Beijing, China, which was formed by the A/H1N1 2009pdm, and a subsequent influenza B epidemic in year 2009/2010. Our analysis also shows a small peak formed by a seasonal H3N2 epidemic prior to the A/H1N1 2009pdm peak. Parallel detection of multiple respiratory viruses shows that the epidemic of common respiratory viruses, except human rhinovirus, was delayed during the pandemic of the A/H1N1 2009pdm. The H1N1 2009pdm mainly caused upper respiratory tract infections in the sampled patients; patients infected with H1N1 2009pdm had a higher percentage of cough than those infected with seasonal influenza or other respiratory viruses. Our findings indicate that A/H1N1 2009pdm and other respiratory viruses except human rhinovirus could interfere with each other during their transmission between human beings. Understanding the mechanisms and effects of such interference is needed for effective control of future influenza epidemics.     

Zoonotic (avian) influenza. Prognoses of pandemic and reality

Likelihood of a pandemic emergence in the near future was discussed. The majority of science-based arguments point to anthroponotic nature of future pandemic, which can be caused by return to circulation of H2N2 virus silently persisting in population from 1968 or in animals as part of various reassortants. Outbreaks of zoonotic (avian) influenza in humans emerged recently reflect natural epidemic manifestations of epizootic process which had become more intense due to specific social and natural conditions in densely populated countries of South-East Asia. This suggestion is confirmed by predominance of poultry workers between patients with avian influenza. Likelihood of pandemic influenza A virus emergence as a result of reassortation between human and avian influenza viruses is not high. Similarity of antigenic structure of human and animal influenza viruses points to their common roots, but yet humans remain the biological dead-end for reassortant viruses. Rationale for epidemiologic surveillance as well as for prophylactic and antiepidemic measures with respect to influenza A is obvious basing on anthroponotic nature of its causative agents. Although the likelihood of adaptation of animal influenza viruses to human organism and formation of anthroponotic mechanisms of transmission is small, epidemiological and, especially, epizootic surveillance for zoonotic influenza are essential.     

A Simulation Optimization Approach to Epidemic Forecasting

Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area.     

Pandemic policy and planning considerations for universities: findings from a tabletop exercise

The potential for a novel influenza virus to cause a pandemic represents a significant threat to global health. Planning for pandemic flu, as compared to planning for other types of hazards, presents some unique challenges to businesses, communities, and education institutions. To identify and address the challenges that may be faced by major metropolitan universities during a flu pandemic, a tabletop exercise was developed, offered, and evaluated. Its purpose was to assess existing University of Washington (UW) plans and policies for responding to an influenza pandemic. On May 31, 2006, more than 50 participants, including UW administrators and unit leaders and a number of key external partners, participated in a tabletop exercise designed to simulate all phases of an influenza pandemic. This exercise revealed existing gaps in university pandemic influenza plans and policies, including issues related to isolation and quarantine, continuity of operations, disaster mental health services, integration of volunteers into a disaster response, tracking travel of university students and personnel, communication problems, and ways to meet the needs of resident and foreign students and faculty during an outbreak. Policy and planning recommendations are offered that address each of these challenges faced by UW as well as other major research universities and colleges.     

Transmission of a 2009 pandemic influenza virus shows a sensitivity to temperature and humidity similar to that of an H3N2 seasonal strain

In temperate regions of the world, influenza epidemics follow a highly regular seasonal pattern, in which activity peaks in midwinter. Consistently with this epidemiology, we have shown previously that the aerosol transmission of a seasonal H3N2 influenza virus is most efficient under cold, dry conditions. With the 2009 H1N1 pandemic, an exception to the standard seasonality of influenza developed: during 2009 in the Northern Hemisphere, an unusually high level of influenza virus activity over the spring and summer months was followed by a widespread epidemic which peaked in late October, approximately 2.5 months earlier than usual. Herein we show that aerosol transmission of a 2009 pandemic strain shows a dependence on relative humidity and temperature very similar to that of a seasonal H3N2 influenza virus. Our data indicate that the observed differences in the timings of outbreaks with regard to the seasons are most likely not due to intrinsic differences in transmission between the pandemic H1N1 and seasonal H3N2 influenza viruses.     

Qualitative analysis of the level of cross-protection between epidemic waves of the 1918-1919 influenza pandemic

The 1918-1919 influenza pandemic was composed of multiple waves within a period of nine months in several regions of the world. Increasing our understanding of the mechanisms responsible for this multi-wave profile has important public health implications. We model the transmission dynamics of two strains of influenza interacting via cross-immunity to simulate two temporal waves of influenza and explore the impact of the basic reproduction number, as a measure of transmissibility associated to each influenza strain, cross-immunity and the timing of the onset of the second influenza epidemic on the pandemic profile. We use time series of case notifications during the 1918 influenza pandemic in Geneva, Switzerland, for illustration. We calibrate our mathematical model to the initial wave of infection to estimate the basic reproduction number of the first wave and the corresponding timing of onset of the second influenza variant. We use this information to explore the impact of cross-immunity levels on the dynamics of the second wave of influenza. Our results for the 1918 pandemic in Geneva, Switzerland, indicate that a second wave can occur whenever R₀₁<1.5 or when cross-immunity levels are less than 0.58 for our estimated R₀₂ of 2.4. We also explore qualitatively profiles of two-wave pandemics and compare them with real temporal profiles of the 1918 influenza pandemic in other regions of the world including several Scandinavian cities, New York City, England and Wales, and Sydney, Australia. Pandemic profiles are classified into three broad categories namely “right-handed”, “left-handed”, and “M-shape”. Our results indicate that avoiding a second influenza epidemic is plausible given sufficient levels of cross-protection are attained via natural infection during an early (herald) wave of infection or vaccination campaigns prior to a second wave. Furthermore, interventions aimed at mitigating the first pandemic wave may be counterproductive by increasing the chances of a second wave of infection that could potentially be more virulent than the first.     

Comparing pandemic to seasonal influenza mortality: moderate impact overall but high mortality in young children

Background

We assessed the severity of the 2009 influenza pandemic by comparing pandemic mortality to seasonal influenza mortality. However, reported pandemic deaths were laboratory-confirmed – and thus an underestimation – whereas seasonal influenza mortality is often more inclusively estimated. For a valid comparison, our study used the same statistical methodology and data types to estimate pandemic and seasonal influenza mortality.

Methods and Findings

We used data on all-cause mortality (1999–2010, 100% coverage, 16.5 million Dutch population) and influenza-like-illness (ILI) incidence (0.8% coverage). Data was aggregated by week and age category. Using generalized estimating equation regression models, we attributed mortality to influenza by associating mortality with ILI-incidence, while adjusting for annual shifts in association. We also adjusted for respiratory syncytial virus, hot/cold weather, other seasonal factors and autocorrelation. For the 2009 pandemic season, we estimated 612 (range 266–958) influenza-attributed deaths; for seasonal influenza 1,956 (range 0–3,990). 15,845 years-of-life-lost were estimated for the pandemic; for an average seasonal epidemic 17,908. For 0–4 yrs of age the number of influenza-attributed deaths during the pandemic were higher than in any seasonal epidemic; 77 deaths (range 61–93) compared to 16 deaths (range 0–45). The ≥75 yrs of age showed a far below average number of deaths. Using pneumonia/influenza and respiratory/cardiovascular instead of all-cause deaths consistently resulted in relatively low total pandemic mortality, combined with high impact in the youngest age category.

Conclusion

The pandemic had an overall moderate impact on mortality compared to 10 preceding seasonal epidemics, with higher mortality in young children and low mortality in the elderly. This resulted in a total number of pandemic deaths far below the average for seasonal influenza, and a total number of years-of-life-lost somewhat below average. Comparing pandemic and seasonal influenza mortality as in our study will help assessing the worldwide impact of the 2009 pandemic.     

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.     

Ubiquitous reassortments in influenza A viruses

The influenza A virus is a negative-stranded RNA virus composed of eight segmented RNA molecules, including polymerases (PB2, PB1, PA), hemagglutinin (HA), nucleoprotein (NP), neuraminidase (NA), matrix protein (MP), and nonstructure gene (NS). The influenza A viruses are notorious for rapid mutations, frequent reassortments, and possible recombinations. Among these evolutionary events, reassortments refer to exchanges of discrete RNA segments between co-infected influenza viruses, and they have facilitated the generation of pandemic and epidemic strains. Thus, identification of reassortments will be critical for pandemic and epidemic prevention and control. This paper presents a reassortment identification method based on distance measurement using complete composition vector (CCV) and segment clustering using a minimum spanning tree (MST) algorithm. By applying this method, we identified 34 potential reassortment clusters among 2,641 PB2 segments of influenza A viruses. Among the 83 serotypes tested, at least 56 (67.46%) exchanged their fragments with another serotype of influenza A viruses. These identified reassortments involve 1,957 H2N1 and 1,968 H3N2 influenza pandemic strains as well as H5N1 avian influenza virus isolates, which have generated the potential for a future pandemic threat. More frequent reassortments were found to occur in wild birds, especially migratory birds. This MST clustering program is written in Java and will be available upon request.     

The influenza pandemic of 2009: lessons and implications

Influenza is a moving target, which evolves in unexpected directions and is recurrent annually. The 2009 influenza A/H1N1 pandemic virus was unlike the 2009 seasonal virus strains and originated in pigs prior to infecting humans. Three strains of viruses gave rise to the pandemic virus by antigenic shift, reassortment, and recombination, which occurred in pigs as 'mixing vessels'. The three strains of viruses had originally been derived from birds, pigs, and humans. The influenza hemagglutinin (HA) and neuraminidase (NA) external proteins are used to categorize and group influenza viruses. The internal proteins (PB1, PB1-F2, PB2, PA, NP, M, and NS) are involved in the pathogenesis of influenza infection. A major difference between the 1918 and 2009 pandemic viruses is the lack of the pathogenic protein PB1-F2 in the 2009 pandemic strains, which was present in the more virulent 1918 pandemic strains. We provide an overview of influenza infection since 1847 and the advent of influenza vaccination since 1944. Vaccines and chemotherapy help reduce the spread of influenza, reduce morbidity and mortality, and are utilized by the global rapid-response organizations associated with the WHO. Immediate identification of impending epidemic and pandemic strains, as well as sustained vigilance and collaboration, demonstrate continued success in combating influenza.     

The 1918-1919 influenza pandemic in England and Wales: spatial patterns in transmissibility and mortality impact

Spatial variations in disease patterns of the 1918-1919 influenza pandemic remain poorly studied. We explored the association between influenza death rates, transmissibility and several geographical and demographic indicators for the autumn and winter waves of the 1918-1919 pandemic in cities, towns and rural areas of England and Wales. Average measures of transmissibility, estimated by the reproduction number, ranged between 1.3 and 1.9, depending on model assumptions and pandemic wave and showed little spatial variation. Death rates varied markedly with urbanization, with 30-40% higher rates in cities and towns compared with rural areas. In addition, death rates varied with population size across rural settings, where low population areas fared worse. By contrast, we found no association between transmissibility, death rates and indicators of population density and residential crowding. Further studies of the geographical mortality patterns associated with the 1918-1919 influenza pandemic may be useful for pandemic planning.