Pandemic Influenza Outbreak Research Articles

PIN120 - A Dynamic Decision Analysis Process For Evaluating Pandemic Influenza Intervention Strategies

Public health authorities (PHA) experience challenges during outbreaks of pandemic influenza. There are (1) a high public attention and pressure for fast and effective actions, (2) uncertainty regarding development and potential impact of the pandemic, (3) complexity of the decisions on selecting interventions – this involves considering multitude of criteria and interests of stakeholders. Therefore we suggest a dynamic decision analysis process, which supports comprehensiveness, transparency, and consistency in decisions regarding interventions. The process design was informed by scenario analysis, multi-criteria decision analysis methodology, and adapted to the Swedish context. The process was then validated in a pilot decision conference (DC) conducted at the PHA of Sweden. The process consists of elicitation and evaluation. Elicitation comprises developing scenarios, intervention strategies, and a multi-criteria model (MCM) against which the strategies are evaluated. In the evaluation process the strategies are scored, criteria weighted, and the results analyzed. In the pilot DC we considered a mild pandemic scenario, similar to the influenza pandemic of 2009. Three intervention strategies were evaluated, based on four interventions; vaccination (VAC), antiviral treatment (AVT), antiviral prophylaxis (AVP), school closure (SC). Strategy (1) included AVT to everyone with influenza-like illness (ILI), VAC to risk-groups, (2) AVT and AVP to risk-groups, SC, and VAC to everyone, and (3) AVT to everyone with ILI, AVP to risk-groups, and VAC to everyone. Guided by the process the seven participants outlined the MCM through a post-it session, evaluated the strategies against the criteria, and expressed criteria weights. Finally the results were aggregated and visualized. The analysis showed that strategy (1) was the most preferred. The process supports a dynamic, comprehensive, and transparent decision-making. An integrated decision-support toolset will be developed in forthcoming research along with contextualising and validating the process with Sweden related stakeholders.

Real-time modelling of a pandemic influenza outbreak.

Real-time modelling is an essential component of the public health response to an outbreak of pandemic influenza in the UK. A model for epidemic reconstruction based on realistic epidemic surveillance data has been developed, but this model needs enhancing to provide spatially disaggregated epidemic estimates while ensuring that real-time implementation is feasible. To advance state-of-the-art real-time pandemic modelling by (1) developing an existing epidemic model to capture spatial variation in transmission, (2) devising efficient computational algorithms for the provision of timely statistical analysis and (3) incorporating the above into freely available software. Markov chain Monte Carlo (MCMC) sampling was used to derive Bayesian statistical inference using 2009 pandemic data from two candidate modelling approaches: (1) a parallel-region (PR) approach, splitting the pandemic into non-interacting epidemics occurring in spatially disjoint regions; and (2) a meta-region (MR) approach, treating the country as a single meta-population with long-range contact rates informed by census data on commuting. Model discrimination is performed through posterior mean deviance statistics alongside more practical considerations. In a real-time context, the use of sequential Monte Carlo (SMC) algorithms to carry out real-time analyses is investigated as an alternative to MCMC using simulated data designed to sternly test both algorithms. SMC-derived analyses are compared with 'gold-standard' MCMC-derived inferences in terms of estimation quality and computational burden. The PR approach provides a better and more timely fit to the epidemic data. Estimates of pandemic quantities of interest are consistent across approaches and, in the PR approach, across regions (e.g. R0 is consistently estimated to be 1.76-1.80, dropping by 43-50% during an over-summer school holiday). A SMC approach was developed, which required some tailoring to tackle a sudden 'shock' in the data resulting from a pandemic intervention. This semi-automated SMC algorithm outperforms MCMC, in terms of both precision of estimates and their timely provision. Software implementing all findings has been developed and installed within Public Health England (PHE), with key staff trained in its use. The PR model lacks the predictive power to forecast the spread of infection in the early stages of a pandemic, whereas the MR model may be limited by its dependence on commuting data to describe transmission routes. As demand for resources increases in a severe pandemic, data from general practices and on hospitalisations may become unreliable or biased. The SMC algorithm developed is semi-automated; therefore, some statistical literacy is required to achieve optimal performance. Following the objectives, this study found that timely, spatially disaggregate, real-time pandemic inference is feasible, and a system that assumes data as per pandemic preparedness plans has been developed for rapid implementation. Modelling studies investigating the impact of pandemic interventions (e.g. vaccination and school closure); the utility of alternative data sources (e.g. internet searches) to augment traditional surveillance; and the correct handling of test sensitivity and specificity in serological data, propagating this uncertainty into the real-time modelling. Current Controlled Trials ISRCTN40334843. This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in Health Technology Assessment; Vol. 21, No. 58. See the NIHR Journals Library website for further project information. Daniela De Angelis was supported by the UK Medical Research Council (Unit Programme Number U105260566) and by PHE. She received funding under the NIHR grant for 10% of her time. The rest of her salary was provided by the MRC and PHE jointly.

Revised protocol of extracorporeal membrane oxygenation (ECMO) therapy in severe ARDS. Recommendations of the Veno-venous ECMO Expert Panel appointed in February 2016 by the national consultant on anesthesiology and intensive care.

Extracorporeal Membrane Oxygenation (ECMO) has become well established technique of the treatment of severe acute respiratory failure (Veno-Venous ECMO) or circulatory failure (Veno-Arterial ECMO) which enables effective blood oxygenation and carbon dioxide removal for several weeks. Veno-Venous ECMO (V-V ECMO ) is a lifesaving treatment of patients in whom severe ARDS makes artificial lung ventilation unlikely to provide satisfactory blood oxygenation for preventing further vital organs damage and progression to death. The protocol below regards exclusively veno-venous ECMO treatment as a support for blood gas conditioning by means of extracorporeal circuit in adult patients with severe ARDS. V-V ECMO does not provide treatment for acutely and severely diseased lungs, but it enables patient to survive the critical phase of severe ARDS until recovery of lung function. Besides avoiding patients death from hypoxemia, this technique can also prevent further progression of the lung damage due to artificial ventilation. Recent experience of ECMO treatment since the outbreak of AH1N1 influenza pandemic in 2009, along with technical progress and advancement in understanding pathophysiology of ventilator-induced lung injury, have contributed to significant improvement of the results of ECMO treatment. Putative factors related to increased survival include patients retrieval after connecting them to ECMO, and less intensive anticoagulation protocols. The aim of presenting this revised protocol was to improve the effects of ECMO treatment in patients with severe ARDS, to enhance ECMO accessibility for patients who might possibly benefit from this treatment, to reduce time until patient's connection to ECMO, and to avoid ECMO treatment in futile cases. The authors believe that this protocol, based on recent papers and their own experience, can provide help and advice both for the centers which develop V-V ECMO program, and for doctors who will refer their patients for the treatment in an ECMO center.

Influenza viruses production: Evaluation of a novel avian cell line DuckCelt®-T17

The influenza vaccine manufacturing industry is looking for production cell lines that are easily scalable, highly permissive to multiple viruses, and more effective in term of viral productivity. One critical characteristic of such cell lines is their ability to grow in suspension, in serum free conditions and at high cell densities. Influenza virus causing severe epidemics both in human and animals is an important threat to world healthcare. The repetitive apparition of influenza pandemic outbreaks in the last 20years explains that manufacturing sector is still looking for more effective production processes to replace/supplement embryonated egg-based process. Cell-based production strategy, with a focus on avian cell lines, is one of the promising solutions. Three avian cell lines, namely duck EB66®cells (Valneva), duck AGE.CR® cells (Probiogen) and quail QOR/2E11 cells (Baxter), are now competing with traditional mammalian cell platforms (Vero and MDCK cells) used for influenza vaccine productions and are currently at advance stage of commercial development for the manufacture of influenza vaccines.The DuckCelt®-T17 cell line presented in this work is a novel avian cell line developed by Transgene. This cell line was generated from primary embryo duck cells with the constitutive expression of the duck telomerase reverse transcriptase (dTERT). The DuckCelt®-T17 cells were able to grow in batch suspension cultures and serum-free conditions up to 6.5×106cell/ml and were easily scaled from 10ml up to 3l bioreactor. In the present study, DuckCelt®-T17 cell line was tested for its abilities to produce various human, avian and porcine influenza strains. Most of the viral strains were produced at significant infectious titers (>5.8 log TCID50/ml) with optimization of the infection conditions. Human strains H1N1 and H3N2, as well as all the avian strains tested (H5N2, H7N1, H3N8, H11N9, H12N5) were the most efficiently produced with highest titre reached of 9.05 log TCID50/ml for A/Panama/2007/99 influenza H3N2. Porcine strains were also greatly rescued with titres from 4 to 7 log TCID50/ml depending of the subtypes. Interestingly, viral kinetics showed maximal titers reached at 24h post-infection for most of the strains, allowing early harvest time (Time Of Harvest: TOH). The B strains present specific production kinetics with a delay of 24h before reaching the maximal viral particle release. Process optimization on H1N1 2009 human pandemic strain allowed identifying best operating conditions for production (MOI, trypsin concentration, cell density at infection) allowing improving the production level by 2 log. Our results suggest that the DuckCelt®-T17 cell line is a very promising platform for industrial production of influenza viruses and particularly for avian viral strains.

Combined interventions for mitigation of an influenza A (H1N1) 2009 outbreak in a physical training camp in Beijing, China

Many studies have suggested the effectiveness of single control measures in the containment and mitigation of pandemic influenza A (H1N1) 2009. The effects of combined interventions by multiple control measures in reducing the impact of an influenza A (H1N1) 2009 outbreak in a closed physical training camp in Beijing, China were evaluated. Oseltamivir was prescribed for the treatment of confirmed cases and possible cases and as prophylaxis for all other participants in this training camp. Public health control measures were applied simultaneously, including the isolation of patients and possible cases, personal protection and hygiene, and social distancing measures. Symptom surveillance of all participants was initiated, and the actual attack rate was calculated. For comparison, the theoretical attack rate for this outbreak was projected using the Newton-Raphson numerical method. A total of 3256 persons were present at the physical training camp. During the outbreak, 405 (68.3%) possible cases and 26 (4.4%) confirmed cases were reported before the intervention and completed oseltamivir treatment; 162 (27.3%) possible cases were reported after the intervention and received part treatment and part prophylaxis. The other 2663 participants completed oseltamivir prophylaxis. Of the possible cases, 181 with fever ≥38.5°C were isolated. The actual attack rate for this outbreak of pandemic influenza A (H1N1) 2009 was 18.2%, which is much lower than the theoretical attack rate of 80% projected. Combined interventions of large-scale antiviral ring prophylaxis and treatment and public health control measures could be applied to reduce the magnitude of influenza A (H1N1) 2009 outbreaks in closed settings.

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.

An Alternative Method to Produce Shikimic Acid Chemical Feedstock by Applying Glyphosate to Forage Crops

Shikimic acid is the critical precursor to manufacture the antiviral drug Oseltamivir phosphate, also known as Tamiflu. Current sources of shikimic acid feedstock have been strained during pandemic influenza outbreaks. This article proposes an alternative process to produce shikimic acid feedstock through the bioenhancement of graminoid crops using glyphosate. To demonstrate, a Gulf cultivar of Italian annual ryegrass (Lolium multiflorum Lam.) was grown in controlled greenhouse conditions and Yamhill winter wheat (Triticum aestivum L.) was grown in a field trial. Plants were treated with a range of glyphosate doses and harvested between 48 and 298 h following application. Samples were dried, extracted, and analyzed by high performance liquid chromatography. Shikimic acid levels increased up to four‐fold in glyphosate‐treated Gulf annual ryegrass, a plant with high native shikimic acid. Concentrations of shikimic acid in Yamhill winter wheat, a plant low in native shikimic acid, increased from below method detection limits (<19 mg kg−1) in the control groups to >2000 mg kg−1 in treatment groups harvested after 169 h post application. Estimated biomass accumulation of shikimic acid were projected using concentrations observed in these trials for annual ryegrass, alfalfa (Medicago sativa L.), and winter wheat based on published biomass yields resulted in 21.1 to 299 kg of potential shikimic acid production per hectare, which meet or exceed current feedstocks. This study provides evidence that glyphosate can be used to bioenhance graminoid forage crops to produce elevated shikimic acid levels throughout the entire plant biomass, resulting in a substantially greater yield of shikimic acid than current botanical sources on a per‐hectare basis.

Characterising pandemic severity and transmissibility from data collected during first few hundred studies

Early estimation of the probable impact of a pandemic influenza outbreak can assist public health authorities to ensure that response measures are proportionate to the scale of the threat. Recently, frameworks based on transmissibility and severity have been proposed for initial characterization of pandemic impact. Data requirements to inform this assessment may be provided by “First Few Hundred” (FF100) studies, which involve surveillance—possibly in person, or via telephone—of household members of confirmed cases. This process of enhanced case finding enables detection of cases across the full spectrum of clinical severity, including the date of symptom onset. Such surveillance is continued until data for a few hundred cases, or satisfactory characterization of the pandemic strain, has been achieved.We present a method for analysing these data, at the household level, to provide a posterior distribution for the parameters of a model that can be interpreted in terms of severity and transmissibility of a pandemic strain. We account for imperfect case detection, where individuals are only observed with some probability that can increase after a first case is detected. Furthermore, we test this methodology using simulated data generated by an independent model, developed for a different purpose and incorporating more complex disease and social dynamics. Our method recovers transmissibility and severity parameters to a high degree of accuracy and provides a computationally efficient approach to estimating the impact of an outbreak in its early stages.

Patterns of medication use and factors associated with antibiotic use among adult fever patients at Singapore primary care clinics.

BackgroundAntimicrobial resistance is a public health problem of global importance. In Singapore, much focus has been given to antibiotic usage patterns in hospital settings. Data on antibiotic use in primary care is lacking. We describe antibiotic usage patterns and assess factors contributing to antibiotic usage among adults presenting with acute febrile illness (AFI) in primary care settings in Singapore.MethodsWe analyzed data from the Early Dengue infection and outcome study. Adults with AFI presenting at 5 Singapore polyclinics were included. We used multivariable logistic regression to assess demographic, clinical and laboratory factors associated with antibiotic usage among adults with AFI.ResultsBetween December 2007 and February 2013, 1884 adult AFI patients were enrolled. Overall, 16% of adult AFI patients reported antibiotic use. We observed a rise in the use of over-the-counter medications in late 2009 and a decrease in antibiotic use during 2010, possibly related to the outbreak of pandemic influenza A H1N1 virus. After adjusting for age, gender, polyclinic and year of enrolment, the following factors were associated with higher odds of antibiotic use: living in landed property (compared to public housing) (OR = 1.73; 95% CI: 1.06–2.80); body mass index (BMI) <18.5 (OR = 1.87; 95% CI: 1.19–2.93); elevated white blood cell (WBC) count (OR = 1.98; 95% CI: 1.42–2.78); and persistence of initial symptoms at 2–3 days follow-up with OR (95% CI) for categories of 1, 2, 3, and ≥4 persisting symptoms being 2.00 (1.38–2.92), 2.67 (1.80–3.97), 4.26 (2.73–6.64), and 2.79 (1.84–4.24) respectively.ConclusionsOur study provides insights on antibiotic usage among adult patients presenting to primary care clinics with febrile illness, and suggests that high socio-economic status, and risk factors of a severe illness, that is, low BMI and persistence of initial symptoms, are associated with higher antibiotic use. Further work to understand trends of antibiotic usage in both private and public primary care clinics, and factors that influence patient expectation and physician prescribing of antibiotics is important.

Did exposure to a severe outbreak of pandemic influenza in 1918 impact on long-term survival?

There is some suggestion that infection with pandemic influenza may increase long-term mortality risks. Therefore we aimed to determine if exposure to a severe outbreak of pandemic influenza on a troopship in 1918 impacted on lifespan in the survivors. The troopship with the outbreak cohort had 1107 personnel and the comparison cohort was from two contemporaneous troopships (1108 randomly selected personnel). Data were collected from online individual military files. The main finding was that there was no statistically significant difference in the lifespan of the outbreak cohort and the comparison cohort (means of 71·5 and 71·0 years, respectively). Indeed, the outbreak cohort was actually more likely to survive into the period from 1950 onwards (P = 0·036) and to participate in the Second World War (P = 0·043). There were no significant differences between the cohorts in terms of occupational class, but the comparison cohort had a higher proportion of rural occupations (33·3% vs. 27·0%, P < 0·001) and was very slightly older in mid-1918 (27·8 vs. 27·2 years, P = 0·028). In conclusion, this study found no support for the hypothesis that exposure to the 1918 influenza pandemic adversely impacted on the lifespan in the survivors, at least in this male and military-age population.

Maternal immunization: Optimizing protection for the mother and infant

Immunizing the pregnant woman to protect both the mother and her infant from infection has been utilized increasingly over the last decade. New outbreaks of pandemic influenza and the resurgence of pertussis have resulted in policy changes and shifts in health authority recommendations for a number of vaccines aimed to protect both pregnant women and their infants in the first months of life. The ability of maternal immunoglobulin IgG antibodies to be transported readily across the healthy intact placenta depends on many different factors including gestational age in the pregnancy, nature and timing of the immunization and presence of maternal HIV or malaria infections. In this paper, the history of maternal immunization is described, and specifically the studies that prompted the recommendations for tetanus, influenza, pertussis, and, when needed, meningococcus vaccines in pregnant women are reviewed. Ongoing research may result in new maternal vaccines against other pathogens including respiratory syncytial virus and group B streptococcus. Both scientific and regulatory considerations remain challenging in licensure of vaccines specifically for maternal immunization.

Total Economic Consequences of an Influenza Outbreak in the United States

Pandemic influenza represents a serious threat not only to the population of the United States, but also to its economy. In this study, we analyze the total economic consequences of potential influenza outbreaks in the United States for four cases based on the distinctions between disease severity and the presence/absence of vaccinations. The analysis is based on data and parameters on influenza obtained from the Centers for Disease Control and the general literature. A state-of-the-art economic impact modeling approach, computable general equilibrium, is applied to analyze a wide range of potential impacts stemming from the outbreaks. This study examines the economic impacts from changes in medical expenditures and workforce participation, and also takes into consideration different types of avoidance behavior and resilience actions not previously fully studied. Our results indicate that, in the absence of avoidance and resilience effects, a pandemic influenza outbreak could result in a loss in U.S. GDP of $25.4 billion, but that vaccination could reduce the losses to $19.9 billion. When behavioral and resilience factors are taken into account, a pandemic influenza outbreak could result in GDP losses of $45.3 billion without vaccination and $34.4 billion with vaccination. These results indicate the importance of including a broader set of causal factors to achieve more accurate estimates of the total economic impacts of not just pandemic influenza but biothreats in general. The results also highlight a number of actionable items that government policymakers and public health officials can use to help reduce potential economic losses from the outbreaks.

Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations

ObjectivesReliable monitoring of influenza seasons and pandemic outbreaks is essential for response planning, but compilations of reports on detection and prediction algorithm performance in influenza control practice are largely missing....

Evaluating Syndromic Surveillance Systems

Syndromic surveillance systems are commonly presented in the literature but few are rigorously evaluated. We present and test an evaluation framework to examine which events can and cannot be detected, the time to detection and the efficacy of different syndromic surveillance data streams. This was achieved using four national syndromic surveillance systems in England and simulating a number of possible disease events (e.g. outbreak of pandemic influenza, (Cryptosporidium) outbreak and deliberate anthrax release). This methodology can be widely adopted to provide more empirical analysis of the effectiveness of syndromic surveillance systems worldwide.

A Diagnostic Platform Predicts Presymptomatic Exposure to Respiratory Viral Infection.

INTRODUCTION Communicable diseases can be a significant burden during military operations in which missions rely on each and every team member for optimal performance. Community-acquired acute respiratory infections are particularly difficult to contain, given their rapid transmissibility and the lack of early diagnostic tests. Disease prevention using vaccination has shown low effectiveness, notably the overall flu vaccine effectiveness estimate for 2014–2015 flu season was 19%. Contemporary approaches to diagnose respiratory infection rely on time-consuming pathogen-specific assessment during the symptomatic window of the infection. In the last decade, however, the genomics revolution—and specifically, systems genomics approaches combined with mathematical modeling, has led to better understanding of disease at the individual level and the emergence of new diagnostic and therapeutic paradigms. The diagnostic limitations to control infections nonetheless remain a medical challenge as evidenced by the most recent H1N1 influenza pandemic outbreak of 2009. As emphasized by a U.S. Department of Health and Human Services retrospective analysis: “The absence of readily available, rapid, simple, and highly sensitive diagnostic tests which could detect the 2009 H1N1 virus made infection control more difficult.” In 2006, the Defense Advanced Research Projects Agency (DARPA) launched an ambitious program to devise models and methods that predict incipient infection after exposure. The program known as Predicting Health and Disease (PHD) aimed to enable the capability to diagnose individuals with a respiratory infection before they become symptomatic. DEVELOPMENT AND VALIDATION OF MODELS THAT DISTINGUISH INFLUENZA INFECTED SYMPTOMATIC PATIENTS To develop novel predictive models of incipient respiratory infectious disease, a group at Duke University led by Dr. Geoff Ginsburg, in collaboration with Dr. Alfred Hero at University of Michigan, performed studies with healthy volunteers experimentally infected with live unattenuated Influenza (H1N1 or H3N2), respiratory syncytial virus, or human rhinovirus, and who underwent serial blood sampling at regular time intervals preand postinoculation. Using factor analysis of mRNA expression arrays with ~22,000 genes, the team identified patterns of “host-derived gene expression signatures” in blood that were highly specific for infection in symptomatic individuals, and distinct from asymptomatic subjects. The discriminatory factors (designated viral factors) encompassed a collection of highly correlated host genes involved in antiviral responses (i.e. Toll-like receptor signaling genes, Interferon response genes, etc.), and were closely associated with disease dynamics and symptom scores.

Influenza A virus ribonucleoproteins modulate host recycling by competing with Rab11 effectors.

Influenza A virus assembly is an unclear process, whereby individual virion components form an infectious particle. The segmented nature of the influenza A genome imposes a problem to assembly because it requires packaging of eight distinct RNA particles (vRNPs). It also allows genome mixing from distinct parental strains, events associated with influenza pandemic outbreaks. It is important to public health to understand how segmented genomes assemble, a process that is dependent on the transport of components to assembly sites. Previously, it has been shown that vRNPs are carried by recycling endosome vesicles, resulting in a change of Rab11 distribution. Here, we describe that vRNP binding to recycling endosomes impairs recycling endosome function, by competing for Rab11 binding with family-interacting proteins, and that there is a causal relationship between Rab11 ability to recruit family-interacting proteins and Rab11 redistribution. This competition reduces recycling sorting at an unclear step, resulting in clustering of single- and double-membraned vesicles. These morphological changes in Rab11 membranes are indicative of alterations in protein and lipid homeostasis during infection. Vesicular clustering creates hotspots of the vRNPs that need to interact to form an infectious particle.

Assessing the Capacity of the US Health Care System to Use Additional Mechanical Ventilators During a Large-Scale Public Health Emergency

A large-scale public health emergency, such as a severe influenza pandemic, can generate large numbers of critically ill patients in a short time. We modeled the number of mechanical ventilators that could be used in addition to the number of hospital-based ventilators currently in use. We identified key components of the health care system needed to deliver ventilation therapy, quantified the maximum number of additional ventilators that each key component could support at various capacity levels (ie, conventional, contingency, and crisis), and determined the constraining key component at each capacity level. Our study results showed that US hospitals could absorb between 26,200 and 56,300 additional ventilators at the peak of a national influenza pandemic outbreak with robust pre-pandemic planning. The current US health care system may have limited capacity to use additional mechanical ventilators during a large-scale public health emergency. Emergency planners need to understand their health care systems' capability to absorb additional resources and expand care. This methodology could be adapted by emergency planners to determine stockpiling goals for critical resources or to identify alternatives to manage overwhelming critical care need.

Healthcare Workers’ Willingness to Report to Work During an Influenza Pandemic: A Systematic Literature Review

AbstractObjective:An effective response to a pandemic influenza assumes an adequate supply of a variety of skilled healthcare workers (HCWs). But what is known of HCWs’ willingness to report to work?Method:We identified 206 relevant studies from 9 healthcare databases. Of 28 heterogeneous studies that met inclusion criteria, 18 used common methods for calculating percentages of HCWs willing to report to work. Their findings were evaluated in greater depth.Results:In 28 studies that summarized willingness to report to work of 31,633 workers, the percentage of HCWs willing to report to work varied between 23.10% and 93.10%. Scale type and dichotomization of self-report scales led to significantly different results. In 4 studies where 2097 workers responded to a survey during an actual event (2009 H1N1 pandemic influenza) percentage willing to report to work varied from 23.1% to 90.1%. In 6 studies with hypothetical scenarios of avian influenza 50%–82.50% of HCWs were willing to report to work.Conclusion:The studies we reviewed have methodological problems and inconsistencies in findings, even within relatively homogenous study subgroups. We caution emergency planners from relying on findings from any single study that estimates HCWs’ mean willingness to report to work in a pandemic influenza outbreak.

Incidence of respiratory viruses in Peruvian children with acute respiratory infections.

Acute respiratory infections are responsible for high morbi–mortality in Peruvian children. However, the etiological agents are poorly identified. This study, conducted during the pandemic outbreak of H1N1 influenza in 2009, aims to determine the main etiological agents responsible for acute respiratory infections in children from Lima, Peru. Nasopharyngeal swabs collected from 717 children with acute respiratory infections between January 2009 and December 2010 were analyzed by multiplex RT‐PCR for 13 respiratory viruses: influenza A, B, and C virus; parainfluenza virus (PIV) 1, 2, 3, and 4; and human respiratory syncytial virus (RSV) A and B, among others. Samples were also tested with direct fluorescent‐antibodies (DFA) for six respiratory viruses. RT‐PCR and DFA detected respiratory viruses in 240 (33.5%) and 85 (11.9%) cases, respectively. The most common etiological agents were RSV‐A (15.3%), followed by influenza A (4.6%), PIV‐1 (3.6%), and PIV‐2 (1.8%). The viruses identified by DFA corresponded to RSV (5.9%) and influenza A (1.8%). Therefore, respiratory syncytial viruses (RSV) were found to be the most common etiology of acute respiratory infections. The authors suggest that active surveillance be conducted to identify the causative agents and improve clinical management, especially in the context of possible circulation of pandemic viruses. J. Med. Virol. 87:917–924, 2015. © 2015 Wiley Periodicals, Inc.

Public preferences for vaccination and antiviral medicines under different pandemic flu outbreak scenarios.

BackgroundDuring the 2009-2010 A(H1N1) pandemic, many people did not seek care quickly enough, failed to take a full course of antivirals despite being authorised to receive them, and were not vaccinated. Understanding facilitators and barriers to the uptake of vaccination and antiviral medicines will help inform campaigns in future pandemic influenza outbreaks. Increasing uptake of vaccines and antiviral medicines may need to address a range of drivers of behaviour. The aim was to identify facilitators of and barriers to being vaccinated and taking antiviral medicines in uncertain and severe pandemic influenza scenarios using a theoretical model of behaviour change, COM-B.MethodsFocus groups and interviews with 71 members of the public in England who varied in their at-risk status. Participants responded to uncertain and severe scenarios, and to messages giving advice on vaccination and antiviral medicines. Data were thematically analysed using the theoretical framework provided by the COM-B model.ResultsInfluences on uptake of vaccines and antiviral medicines - capabilities, motivations and opportunities - are part of an inter-related behavioural system and different components influenced each other. An identity of being healthy and immune from infection was invoked to explain feelings of invulnerability and hence a reduced need to be vaccinated, especially during an uncertain scenario. The identity of being a ‘healthy person’ also included beliefs about avoiding medicine and allowing the body to fight disease ‘naturally’. This was given as a reason for using alternative precautionary behaviours to vaccination. This identity could be held by those not at-risk and by those who were clinically at-risk.ConclusionsPromoters and barriers to being vaccinated and taking antiviral medicines are multi-dimensional and communications to promote uptake are likely to be most effective if they address several components of behaviour. The benefit of using the COM-B model is that it is at the core of an approach that can identify effective strategies for behaviour change and communications for the future. Identity beliefs were salient for decisions about vaccination. Communications should confront identity beliefs about being a ‘healthy person’ who is immune from infection by addressing how vaccination can boost wellbeing and immunity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-015-1541-8) contains supplementary material, which is available to authorized users.