Transmission Dynamics of an Influenza Model with Vaccination and Antiviral Treatment

Vaccination and antiviral treatment are two important prevention and control measures for the spread of influenza. However, the benefit of antiviral use can be compromised if drug-resistant strains arise. In this paper, we develop a mathematical model to explore the impact of vaccination and antiviral treatment on the transmission dynamics of influenza. The model includes both drug-sensitive and resistant strains. Analytical results of the model show that the quantities ℛ _SC and ℛ _RC , which represent the control reproduction numbers of the sensitive and resistant strains, respectively, provide threshold conditions that determine the competitive outcomes of the two strains. These threshold conditions can be used to gain important insights into the effect of vaccination and treatment on the prevention and control of influenza. Numerical simulations are also conducted to confirm and extend the analytic results. The findings imply that higher levels of treatment may lead to an increase of epidemic size, and the extent to which this occurs depends on other factors such as the rates of vaccination and resistance development. This suggests that antiviral treatment should be implemented appropriately.     

A Two-Year Surveillance of 2009 Pandemic Influenza A (H1N1) in Guangzhou,China: From Pandemic to Seasonal Influenza?

In this two-years surveillance of 2009 pandemic influenza A (H1N1) (pH1N1) in Guangzhou, China, we reported here that the scale and duration of pH1N1 outbreaks, severe disease and fatality rates of pH1N1 patients were significantly lower or shorter in the second epidemic year (May 2010-April 2011) than those in the first epidemic year (May 2009-April 2010) (P<0.05), but similar to those of seasonal influenza (P>0.05). Similar to seasonal influenza, pre-existing chronic pulmonary diseases was a risk factor associated with fatal cases of pH1N1 influenza. Different from seasonal influenza, which occurred in spring/summer seasons annually, pH1N1 influenza mainly occurred in autumn/winter seasons in the first epidemic year, but prolonged to winter/spring season in the second epidemic year. The information suggests a tendency that the epidemics of pH1N1 influenza may probably further shift to spring/summer seasons and become a predominant subtype of seasonal influenza in coming years in Guangzhou, China.     

Detection of Oseltamivir-Resistant Pandemic Influenza A(H1N1)pdm2009 in Brazil: Can Community Transmission Be Ruled Out?

Although surveillance efforts that monitor the emergence of drug-resistant strains of influenza are critical, systematic analysis is overlooked in most developing countries. We report on the occurrence of strains of pandemic influenza A(H1N1)pdm09 with resistance and decreased susceptibility to oseltamivir (OST) in Brazil in 2009, 2011 and 2012. We found 7 mutant viruses, 2 with the mutation S247N and other 5 with the mutation H275Y. Most of these viruses were from samples concentrated in the southern region of Brazil. Some of these resistant viruses were detected prior to the initiation of OST treatment, suggesting that community transmission of mutant viruses may exist. Moreover, we show that one of these OST-resistant (H275Y) strains of A(H1N1)pdm09 was discovered in the tri-border region between Brazil, Argentina and Paraguay, highlighting that this strain could also be found in other Latin American countries. Our findings reinforce the importance of enhanced antiviral resistance surveillance in Brazil and in other Latin American countries to confirm or rule out the community transmission of OST-resistant strains of A(H1N1)pdm09.     

Incidence and Epidemiology of Hospitalized Influenza Cases in Rural Thailand during the Influenza A (H1N1)pdm09 Pandemic, 2009–2010

Background

Data on the burden of the 2009 influenza pandemic in Asia are limited. Influenza A(H1N1)pdm09 was first reported in Thailand in May 2009. We assessed incidence and epidemiology of influenza-associated hospitalizations during 2009–2010.

Methods

We conducted active, population-based surveillance for hospitalized cases of acute lower respiratory infection (ALRI) in all 20 hospitals in two rural provinces. ALRI patients were sampled 1∶2 for participation in an etiology study in which nasopharyngeal swabs were collected for influenza virus testing by PCR.

Results

Of 7,207 patients tested, 902 (12.5%) were influenza-positive, including 190 (7.8%) of 2,436 children aged <5 years; 86% were influenza A virus (46% A(H1N1)pdm09, 30% H3N2, 6.5% H1N1, 3.5% not subtyped) and 13% were influenza B virus. Cases of influenza A(H1N1)pdm09 first peaked in August 2009 when 17% of tested patients were positive. Subsequent peaks during 2009 and 2010 represented a mix of influenza A(H1N1)pdm09, H3N2, and influenza B viruses. The estimated annual incidence of hospitalized influenza cases was 136 per 100,000, highest in ages <5 years (477 per 100,000) and >75 years (407 per 100,000). The incidence of influenza A(H1N1)pdm09 was 62 per 100,000 (214 per 100,000 in children <5 years). Eleven influenza-infected patients required mechanical ventilation, and four patients died, all adults with influenza A(H1N1)pdm09 (1) or H3N2 (3).

Conclusions

Influenza-associated hospitalization rates in Thailand during 2009–10 were substantial and exceeded rates described in western countries. Influenza A(H1N1)pdm09 predominated, but H3N2 also caused notable morbidity. Expanded influenza vaccination coverage could have considerable public health impact, especially in young children.     

Supply of Neuraminidase Inhibitors Related to Reduced Influenza A (H1N1) Mortality during the 2009–2010 H1N1 Pandemic: An Ecological Study

Background

The influenza A (H1N1) pandemic swept across the globe from April 2009 to August 2010 affecting millions. Many WHO Member States relied on antiviral drugs, specifically neuraminidase inhibitors (NAIs) oseltamivir and zanamivir, to treat influenza patients in critical condition. Such drugs have been found to be effective in reducing severity and duration of influenza illness, and likely reduced morbidity during the pandemic. However, it is less clear whether NAIs used during the pandemic reduced H1N1 mortality.

Methods

Country-level data on supply of oseltamivir and zanamivir were used to predict H1N1 mortality (per 100,000 people) from July 2009 to August 2010 in forty-two WHO Member States. Poisson regression was used to model the association between NAI supply and H1N1 mortality, with adjustment for economic, demographic, and health-related confounders.

Results

After adjustment for potential confounders, each 10% increase in kilograms of oseltamivir, per 100,000 people, was associated with a 1.6% reduction in H1N1 mortality over the pandemic period (relative rate (RR) = 0.84 per log increase in oseltamivir supply). While the supply of zanamivir was considerably less than that of oseltamivir in each Member State, each 10% increase in kilogram of active zanamivir, per 100,000, was associated with a 0.3% reduction in H1N1 mortality (RR = 0.97 per log increase).

Conclusion

While there are limitations to the ecologic nature of these data, this analysis offers evidence of a protective relationship between antiviral drug supply and influenza mortality and supports a role for influenza antiviral use in future pandemics.     

Influenza Aerosols in UK Hospitals during the H1N1 (2009) Pandemic – The Risk of Aerosol Generation during Medical Procedures

Background

Nosocomial infection of health-care workers (HCWs) during outbreaks of respiratory infections (e.g. Influenza A H1N1 (2009)) is a significant concern for public health policy makers. World Health Organization (WHO)-defined ‘aerosol generating procedures’ (AGPs) are thought to increase the risk of aerosol transmission to HCWs, but there are presently insufficient data to quantify risk accurately or establish a hierarchy of risk-prone procedures.

Methodology/Principal Findings

This study measured the amount of H1N1 (2009) RNA in aerosols in the vicinity of H1N1 positive patients undergoing AGPs to help quantify the potential risk of transmission to HCWs. There were 99 sampling occasions (windows) producing a total of 198 May stages for analysis in the size ranges 0.86–7.3 µm. Considering stages 2 (4–7.3 µm) and 3 (0.86–4 µm) as comprising one sample, viral RNA was detected in 14 (14.1%) air samples from 10 (25.6%) patients. Twenty three air samples were collected while potential AGPs were being performed of which 6 (26.1%) contained viral RNA; in contrast, 76 May samples were collected when no WHO 2009 defined AGP was being performed of which 8 (10.5%) contained viral RNA (unadjusted OR = 2.84 (95% CI 1.11–7.24) adjusted OR = 4.31 (0.83–22.5)).

Conclusions/Significance

With our small sample size we found that AGPs do not significantly increase the probability of sampling an H1N1 (2009) positive aerosol (OR (95% CI) = 4.31 (0.83–22.5). Although the probability of detecting positive H1N1 (2009) positive aerosols when performing various AGPs on intensive care patients above the baseline rate (i.e. in the absence of AGPs) did not reach significance, there was a trend towards hierarchy of AGPs, placing bronchoscopy and respiratory and airway suctioning above baseline (background) values. Further, larger studies are required but these preliminary findings may be of benefit to infection control teams.     

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.     

Comparison of Influenza Outbreaks in the Republic of Kazakhstan and Russia Induced by 2009 Yearly New Variant of А(H1N1) Influenza Virus

The aim of the work is the comparison of the epidemiology of influenza and acute respiratory virus infections (ARVI) in the Republic of Kazakhstan with the corresponding influenza epidemic in Russia induced by influenza pandemic virus A/California/07/2009 in 2009.Data on influenza and ARVI from the Republic of Kazakhstan and Federal Center of influenza was collected and investigated over the course of several weeks from hospitalized patients with the same diagnosis among all population and in age groups on ...     

Did Modeling Overestimate the Transmission Potential of Pandemic (H1N1-2009)? Sample Size Estimation for Post-Epidemic Seroepidemiological Studies

Background

Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.

Methodology/Principal Findings

Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study (R = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from R = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against R = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with R = 1.15, but final sizes in nine studies indicate overestimation if the value R = 1.90 is used.

Conclusions

Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when R = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.     

Influenza A H1N1 Pandemic Strain Evolution – Divergence and the Potential for Antigenic Drift Variants

The emergence of a novel A(H1N1) strain in 2009 was the first influenza pandemic of the genomic age, and unprecedented surveillance of the virus provides the opportunity to better understand the evolution of influenza. We examined changes in the nucleotide coding regions and the amino acid sequences of the hemagglutinin (HA), neuraminidase (NA), and nucleoprotein (NP) segments of the A(H1N1)pdm09 strain using publicly available data. We calculated the nucleotide and amino acid hamming distance from the vaccine strain A/California/07/2009 for each sequence. We also estimated P_epitope–a measure of antigenic diversity based on changes in the epitope regions–for each isolate. Finally, we compared our results to A(H3N2) strains collected over the same period. Our analysis found that the mean hamming distance for the HA protein of the A(H1N1)pdm09 strain increased from 3.6 (standard deviation [SD]: 1.3) in 2009 to 11.7 (SD: 1.0) in 2013, while the mean hamming distance in the coding region increased from 7.4 (SD: 2.2) in 2009 to 28.3 (SD: 2.1) in 2013. These trends are broadly similar to the rate of mutation in H3N2 over the same time period. However, in contrast to H3N2 strains, the rate of mutation accumulation has slowed in recent years. Our results are notable because, over the course of the study, mutation rates in H3N2 similar to that seen with A(H1N1)pdm09 led to the emergence of two antigenic drift variants. However, while there has been an H1N1 epidemic in North America this season, evidence to date indicates the vaccine is still effective, suggesting the epidemic is not due to the emergence of an antigenic drift variant. Our results suggest that more research is needed to understand how viral mutations are related to vaccine effectiveness so that future vaccine choices and development can be more predictive.     

Does the Effectiveness of Control Measures Depend on the Influenza Pandemic Profile?

Background

Although strategies to contain influenza pandemics are well studied, the characterization and the implications of different geographical and temporal diffusion patterns of the pandemic have been given less attention.

Methodology/Main Findings

Using a well-documented metapopulation model incorporating air travel between 52 major world cities, we identified potential influenza pandemic diffusion profiles and examined how the impact of interventions might be affected by this heterogeneity. Clustering methods applied to a set of pandemic simulations, characterized by seven parameters related to the conditions of emergence that were varied following Latin hypercube sampling, were used to identify six pandemic profiles exhibiting different characteristics notably in terms of global burden (from 415 to >160 million of cases) and duration (from 26 to 360 days). A multivariate sensitivity analysis showed that the transmission rate and proportion of susceptibles have a strong impact on the pandemic diffusion. The correlation between interventions and pandemic outcomes were analyzed for two specific profiles: a fast, massive pandemic and a slow building, long-lasting one. In both cases, the date of introduction for five control measures (masks, isolation, prophylactic or therapeutic use of antivirals, vaccination) correlated strongly with pandemic outcomes. Conversely, the coverage and efficacy of these interventions only moderately correlated with pandemic outcomes in the case of a massive pandemic. Pre-pandemic vaccination influenced pandemic outcomes in both profiles, while travel restriction was the only measure without any measurable effect in either.

Conclusions

Our study highlights: (i) the great heterogeneity in possible profiles of a future influenza pandemic; (ii) the value of being well prepared in every country since a pandemic may have heavy consequences wherever and whenever it starts; (iii) the need to quickly implement control measures and even to anticipate pandemic emergence through pre-pandemic vaccination; and (iv) the value of combining all available control measures except perhaps travel restrictions.     

Oseltamivir–Resistant Pandemic H1N1/2009 Influenza Virus Possesses Lower Transmissibility and Fitness in Ferrets

The neuraminidase (NA) inhibitor oseltamivir offers an important immediate option for the control of influenza, and its clinical use has increased substantially during the recent H1N1 pandemic. In view of the high prevalence of oseltamivir-resistant seasonal H1N1 influenza viruses in 2007–2008, there is an urgent need to characterize the transmissibility and fitness of oseltamivir-resistant H1N1/2009 viruses, although resistant variants have been isolated at a low rate. Here we studied the transmissibility of a closely matched pair of pandemic H1N1/2009 clinical isolates, one oseltamivir-sensitive and one resistant, in the ferret model. The resistant H275Y mutant was derived from a patient on oseltamivir prophylaxis and was the first oseltamivir-resistant isolate of the pandemic virus. Full genome sequencing revealed that the pair of viruses differed only at NA amino acid position 275. We found that the oseltamivir-resistant H1N1/2009 virus was not transmitted efficiently in ferrets via respiratory droplets (0/2), while it retained efficient transmission via direct contact (2/2). The sensitive H1N1/2009 virus was efficiently transmitted via both routes (2/2 and 1/2, respectively). The wild-type H1N1/2009 and the resistant mutant appeared to cause a similar disease course in ferrets without apparent attenuation of clinical signs. We compared viral fitness within the host by co-infecting a ferret with oseltamivir-sensitive and -resistant H1N1/2009 viruses and found that the resistant virus showed less growth capability (fitness). The NA of the resistant virus showed reduced substrate-binding affinity and catalytic activity in vitro and delayed initial growth in MDCK and MDCK-SIAT1 cells. These findings may in part explain its less efficient transmission. The fact that the oseltamivir-resistant H1N1/2009 virus retained efficient transmission through direct contact underlines the necessity of continuous monitoring of drug resistance and characterization of possible evolving viral proteins during the pandemic.     

Evolution of the Hemagglutinin Gene of the Influenza A(H1N1)pdm09 Virus in Morocco During Two Influenza Seasons 2009–2011

To study genetic evolution of Moroccan influenza A(H1N1)pdm09 virus strains, we conducted a molecular characterization of the hemagglutinin gene subunit 1 (HA1) of 36 influenza A(H1N1)pdm09 virus strains. The stains were collected from patients in Rabat and Casablanca during two influenza seasons 2009–2010 and 2010–2011. Nucleotide and amino acid sequences of 14 influenza A(H1N1)pdm09 virus strains from 2009 to 2010 were ~97 and 99 %, respectively, similar to the reference strain A/California/07/2009 (H1N1). Phylogenetic analysis of 22 influenza A(H1N1)pdm09 virus strains from 2010 to 2011 revealed a co-circulation of three well-described different genetic groups. Most important, none of the identified groups showed significant changes at the antigenic site of the virus HA1 subunit which may alter the efficacy of California/07/2009 (H1N1) vaccine.     

Guillain-Barré Syndrome and Adjuvanted Pandemic Influenza A (H1N1) 2009 Vaccines: A Multinational Self-Controlled Case Series in Europe

Background

The risk of Guillain-Barré syndrome (GBS) following the United States'' 1976 swine flu vaccination campaign in the USA led to enhanced active surveillance during the pandemic influenza (A(H1N1)pdm09) immunization campaign. This study aimed to estimate the risk of GBS following influenza A(H1N1)pdm09 vaccination.

Methods

A self-controlled case series (SCCS) analysis was performed in Denmark, Finland, France, Netherlands, Norway, Sweden, and the United Kingdom. Information was collected according to a common protocol and standardised procedures. Cases classified at levels 1–4a of the Brighton Collaboration case definition were included. The risk window was 42 days starting the day after vaccination. Conditional Poisson regression and pooled random effects models estimated adjusted relative incidences (RI). Pseudo likelihood and vaccinated-only methods addressed the potential contraindication for vaccination following GBS.

Results

Three hundred and three (303) GBS and Miller Fisher syndrome cases were included. Ninety-nine (99) were exposed to A(H1N1)pdm09 vaccination, which was most frequently adjuvanted (Pandemrix and Focetria). The unadjusted pooled RI for A(H1N1)pdm09 vaccination and GBS was 3.5 (95% Confidence Interval (CI): 2.2–5.5), based on all countries. This lowered to 2.0 (95% CI: 1.2–3.1) after adjustment for calendartime and to 1.9 (95% CI: 1.1–3.2) when we accounted for contra-indications. In a subset (Netherlands, Norway, and United Kingdom) we further adjusted for other confounders and there the RI decreased from 1.7 (adjusted for calendar month) to 1.4 (95% CI: 0.7–2.8), which is the main finding.

Conclusion

This study illustrates the potential of conducting European collaborative vaccine safety studies. The main, fully adjusted analysis, showed that the RI of GBS was not significantly elevated after influenza A(H1N1)pdm09 vaccination (RI = 1.4 (95% CI: 0.7–2.8). Based on the upper limits of the pooled estimate we can rule out with 95% certainty that the number of excess GBS cases after influenza A(H1N1)pdm09 vaccination would be more than 3 per million vaccinated.     

Improving the Clinical Diagnosis of Influenza—a Comparative Analysis of New Influenza A (H1N1) Cases

Background

The presentation of new influenza A(H1N1) is broad and evolving as it continues to affect different geographic locations and populations. To improve the accuracy of predicting influenza infection in an outpatient setting, we undertook a comparative analysis of H1N1(2009), seasonal influenza, and persons with acute respiratory illness (ARI) in an outpatient setting.

Methodology/Principal Findings

Comparative analyses of one hundred non-matched cases each of PCR confirmed H1N1(2009), seasonal influenza, and ARI cases. Multivariate analysis was performed to look for predictors of influenza infection. Receiver operating characteristic curves were constructed for various combinations of clinical and laboratory case definitions. The initial clinical and laboratory features of H1N1(2009) and seasonal influenza were similar. Among ARI cases, fever, cough, headache, rhinorrhea, the absence of leukocytosis, and a normal chest radiograph positively predict for both PCR-confirmed H1N1-2009 and seasonal influenza infection. The sensitivity and specificity of current WHO and CDC influenza-like illness (ILI) criteria were modest in predicting influenza infection. However, the combination of WHO ILI criteria with the absence of leukocytosis greatly improved the accuracy of diagnosing H1N1(2009) and seasonal influenza (positive LR of 7.8 (95%CI 3.5–17.5) and 9.2 (95%CI 4.1–20.3) respectively).

Conclusions/Significance

The clinical presentation of H1N1(2009) infection is largely indistinguishable from that of seasonal influenza. Among patients with acute respiratory illness, features such as a temperature greater than 38°C, rhinorrhea, a normal chest radiograph, and the absence of leukocytosis or significant gastrointestinal symptoms were all positively associated with H1N1(2009) and seasonal influenza infection. An enhanced ILI criteria that combines both a symptom complex with the absence of leukocytosis on testing can improve the accuracy of predicting both seasonal and H1N1-2009 influenza infection.     

Spatiotemporal Dynamics of the Epidemic Transmission in a Predator-Prey System

Epidemic transmission is one of the critical density-dependent mechanisms that affect species viability and dynamics. In a predator-prey system, epidemic transmission can strongly affect the success probability of hunting, especially for social animals. Predators, therefore, will suffer from the positive density-dependence, i.e., Allee effect, due to epidemic transmission in the population. The rate of species contacting the epidemic, especially for those endangered or invasive, has largely increased due to the habitat destruction caused by anthropogenic disturbance. Using ordinary differential equations and cellular automata, we here explored the epidemic transmission in a predator-prey system. Results show that a moderate Allee effect will destabilize the dynamics, but it is not true for the extreme Allee effect (weak or strong). The predator-prey dynamics amazingly stabilize by the extreme Allee effect. Predators suffer the most from the epidemic disease at moderate transmission probability. Counter-intuitively, habitat destruction will benefit the control of the epidemic disease. The demographic stochasticity dramatically influences the spatial distribution of the system. The spatial distribution changes from oil-bubble-like (due to local interaction) to aggregated spatially scattered points (due to local interaction and demographic stochasticity). It indicates the possibility of using human disturbance in habitat as a potential epidemic-control method in conservation.