Swine-Origin Influenza A Outbreak 2009 at Shinshu University, Japan

<p>Abstract</p> <p>Background</p> <p>A worldwide outbreak of swine flu H1N1 pandemic influenza occurred in April 2009. To determine the mechanism underlying the spread of infection, we prospectively evaluated a survey implemented at a local university.</p> <p>Methods</p> <p>Between August 2009 and March 2010, we surveyed 3 groups of subjects: 2318 children in six schools attached to the Faculty of Education, 11424 university students, and 3344 staff members. Subjects with influenza-like symptoms who were diagnosed with swine flu at hospitals or clinics were defined as swine flu patients and asked to make a report using a standardized form.</p> <p>Results</p> <p>After the start of the pandemic, a total of 2002 patients (11.7%) were registered in the survey. These patients included 928 schoolchildren (40.0%), 1016 university students (8.9%), and 58 staff members (1.7%). The incidence in schoolchildren was significantly higher than in the other 2 groups (<it>P </it>< 0.0001) but there were no within group differences in incidence rate between males and females. During the period of the survey, three peaks of patient numbers were observed, in November 2009, December 2009, and January 2010. The first peak consisted mainly of schoolchildren, whereas the second and third peaks included many university students. Staff members did not contribute to peak formation. Among the university students, the most common suspected route of transmission was club activity. Interventions, such as closing classes, schools, and clubs, are likely to affect the epidemic curves.</p> <p>Conclusion</p> <p>Schoolchildren and university students are vulnerable to swine flu, suggesting that avoidance of close contact, especially among these young people, may be effective way in controlling future severe influenza pandemics, especially at educational institutions.</p

Clinical outcomes of seasonal influenza and pandemic influenza A (H1N1) in pediatric inpatients

<p>Abstract</p> <p>Background</p> <p>In April 2009, a novel influenza A H1N1 (nH1N1) virus emerged and spread rapidly worldwide. News of the pandemic led to a heightened awareness of the consequences of influenza and generally resulted in enhanced infection control practices and strengthened vaccination efforts for both healthcare workers and the general population. Seasonal influenza (SI) illness in the pediatric population has been previously shown to result in significant morbidity, mortality, and substantial hospital resource utilization. Although influenza pandemics have the possibility of resulting in considerable illness, we must not ignore the impact that we can experience annually with SI.</p> <p>Methods</p> <p>We compared the outcomes of pediatric patients ≤18 years of age at a large urban hospital with laboratory confirmed influenza and an influenza-like illness (ILI) during the 2009 pandemic and two prior influenza seasons. The primary outcome measure was hospital length of stay (LOS). All variables potentially associated with LOS based on univariable analysis, previous studies, or hypothesized relationships were included in the regression models to ensure adjustment for their effects.</p> <p>Results</p> <p>There were 133 pediatric cases of nH1N1 admitted during 2009 and 133 cases of SI admitted during the prior 2 influenza seasons (2007-8 and 2008-9). Thirty-six percent of children with SI and 18% of children with nH1N1 had no preexisting medical conditions (p = 0.14). Children admitted with SI had 1.73 times longer adjusted LOS than children admitted for nH1N1 (95% CI 1.35 - 2.13). There was a trend towards more children with SI requiring mechanical ventilation compared with nH1N1 (16 vs.7, p = 0.08).</p> <p>Conclusions</p> <p>This study strengthens the growing body of evidence demonstrating that SI results in significant morbidity in the pediatric population. Pandemic H1N1 received considerable attention with strong media messages urging people to undergo vaccination and encouraging improved infection control efforts. We believe that this attention should become an annual effort for SI. Strong unified messages from health care providers and the media encouraging influenza vaccination will likely prove very useful in averting some of the morbidity related to influenza for future epidemics.</p

Determinants of the Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for Real-Time Modelling

Influenza pandemics in the last century were characterized by successive waves and differences in impact and timing between different regions, for reasons not clearly understood. The 2009 H1N1 pandemic showed rapid global spread, but with substantial heterogeneity in timing within each hemisphere. Even within Europe substantial variation was observed, with the UK being unique in experiencing a major first wave of transmission in early summer and all other countries having a single major epidemic in the autumn/winter, with a West to East pattern of spread. Here we show that a microsimulation model, parameterised using data about H1N1pdm collected by the beginning of June 2009, explains the occurrence of two waves in UK and a single wave in the rest of Europe as a consequence of timing of H1N1pdm spread, fluxes of travels from US and Mexico, and timing of school vacations. The model provides a description of pandemic spread through Europe, depending on intra-European mobility patterns and socio-demographic structure of the European populations, which is in broad agreement with observed timing of the pandemic in different countries. Attack rates are predicted to depend on the socio-demographic structure, with age dependent attack rates broadly agreeing with available serological data. Results suggest that the observed heterogeneity can be partly explained by the between country differences in Europe: marked differences in school calendars, mobility patterns and sociodemographic structures. Moreover, higher susceptibility of children to infection played a key role in determining the epidemiology of the 2009 pandemic. Our work shows that it would have been possible to obtain a broad-brush prediction of timing of the European pandemic well before the autumn of 2009, much more difficult to achieve with simpler models or pre-pandemic parameterisation. This supports the use of models accounting for the structure of complex modern societies for giving insight to policy makers

Influenza Pandemic Waves under Various Mitigation Strategies with 2009 H1N1 as a Case Study

A significant feature of influenza pandemics is multiple waves of morbidity and mortality over a few months or years. The size of these successive waves depends on intervention strategies including antivirals and vaccination, as well as the effects of immunity gained from previous infection. However, the global vaccine manufacturing capacity is limited. Also, antiviral stockpiles are costly and thus, are limited to very few countries. The combined effect of antivirals and vaccination in successive waves of a pandemic has not been quantified. The effect of acquired immunity from vaccination and previous infection has also not been characterized. In times of a pandemic threat countries must consider the effects of a limited vaccine, limited antiviral use and the effects of prior immunity so as to adopt a pandemic strategy that will best aid the population. We developed a mathematical model describing the first and second waves of an influenza pandemic including drug therapy, vaccination and acquired immunity. The first wave model includes the use of antiviral drugs under different treatment profiles. In the second wave model the effects of antivirals, vaccination and immunity gained from the first wave are considered. The models are used to characterize the severity of infection in a population under different drug therapy and vaccination strategies, as well as school closure, so that public health policies regarding future influenza pandemics are better informed

Can Interactions between Timing of Vaccine-Altered Influenza Pandemic Waves and Seasonality in Influenza Complications Lead to More Severe Outcomes?

Vaccination can delay the peak of a pandemic influenza wave by reducing the number of individuals initially susceptible to influenza infection. Emerging evidence indicates that susceptibility to severe secondary bacterial infections following a primary influenza infection may vary seasonally, with peak susceptibility occurring in winter. Taken together, these two observations suggest that vaccinating to prevent a fall pandemic wave might delay it long enough to inadvertently increase influenza infections in winter, when primary influenza infection is more likely to cause severe outcomes. This could potentially cause a net increase in severe outcomes. Most pandemic models implicitly assume that the probability of severe outcomes does not vary seasonally and hence cannot capture this effect. Here we show that the probability of intensive care unit (ICU) admission per influenza infection in the 2009 H1N1 pandemic followed a seasonal pattern. We combine this with an influenza transmission model to investigate conditions under which a vaccination program could inadvertently shift influenza susceptibility to months where the risk of ICU admission due to influenza is higher. We find that vaccination in advance of a fall pandemic wave can actually increase the number of ICU admissions in situations where antigenic drift is sufficiently rapid or where importation of a cross-reactive strain is possible. Moreover, this effect is stronger for vaccination programs that prevent more primary influenza infections. Sensitivity analysis indicates several mechanisms that may cause this effect. We also find that the predicted number of ICU admissions changes dramatically depending on whether the probability of ICU admission varies seasonally, or whether it is held constant. These results suggest that pandemic planning should explore the potential interactions between seasonally varying susceptibility to severe influenza outcomes and the timing of vaccine-altered pandemic influenza waves

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

<p>Abstract</p> <p>Background</p> <p>In recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several <it>concerns </it>about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these <it>concerns </it>and identify means of enhancing the current models for higher operational use.</p> <p>Methods</p> <p>We surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers.</p> <p>Results</p> <p>While examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values.</p> <p>Conclusions</p> <p>To adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.</p

Investigation of Key Interventions for Shigellosis Outbreak Control in China

Scientific Research Program of Health Depatrment of Hunan Province [B2012-138]; Scientific Research Program of Changsha Science and Technology Bureau [K1205028-31]Shigellosis is a major public health concern in China, where waterborne disease outbreaks are common. Shigellosis-containing strategies, mostly single or multiple interventions, are implemented by primary-level health departments. Systematic assessment of the effectiveness of these measures is scarce. To estimate the efficacy of commonly used intervention strategies, we developed a Susceptible-Exposed-Infectious/Asymptomatic-Recovered-Water model. No intervention was predicted to result in a total attack rate (TAR) of 90% of the affected population (95% confidence interval [CI]: 86.65-92.80) and duration of outbreak (DO) of 89 days, and the use of single-intervention strategies can be futile or even counter-productive. Prophylactics and water disinfection did not improve TAR or DO. School closure for up to 3 weeks did not help but only increased DO. Isolation alone significantly increased DO. Only antibiotics treatment could shorten the DO to 35 days with TAR unaffected. We observed that these intervention effects were additive when in combined usage under most circumstances. Combined intervention "Isolation+antibiotics+prophylactics+water disinfection'' was predicted to result in the lowest TAR (41.9%, 95% CI: 36.97-47.04%) and shortest DO (28 days). Our actual Shigellosis control implementation that also included school closure for 1 week, attained comparable results and the modeling produced an epidemic curve of Shigellosis highly similar to our actual outbreak data. This lends a strong support to the reality of our model that provides a possible reference for public health professionals to evaluate their strategies towards Shigellosis control