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Background: Despite the known relatively high disease burden of influenza, data are lacking regarding a critical epidemiological indicator, the case-fatality ratio. Our objective was to infer age-group and influenza (sub)type specific values by combining modelled estimates of symptomatic incidence and influenza-attributable mortality., Methods: The setting was the Netherlands, 2011/2012 through 2019/2020 seasons. Sentinel surveillance data from general practitioners and laboratory testing were synthesised to supply age-group specific estimates of incidence of symptomatic infection, and ecological additive modelling was used to estimate influenza-attributable deaths. These were combined in an Bayesian inferential framework to estimate case-fatality ratios for influenza A(H3N2), A(H1N1)pdm09 and influenza B, per 5-year age-group., Results: Case-fatality estimates were highest for influenza A(H3N2) followed by influenza B and then A(H1N1)pdm09 and were highest for the 85+ years age-group, at 4.76% (95% credible interval [CrI]: 4.52-5.01%) for A(H3N2), followed by influenza B at 4.08% (95% CrI: 3.77-4.39%) and A(H1N1)pdm09 at 2.51% (95% CrI: 2.09-2.94%). For 55-59 through 85+ years, the case-fatality risk was estimated to double with every 3.7 years of age., Conclusions: These estimated case-fatality ratios, per influenza sub(type) and per age-group, constitute valuable information for public health decision-making, for assessing the retrospective and prospective value of preventative interventions such as vaccination and for health economic evaluations., Competing Interests: The authors declare that they have no competing interests., (© 2023 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

In infectious disease epidemiology, the instantaneous reproduction number [Formula: see text] is a time-varying parameter defined as the average number of secondary infections generated by an infected individual at time t. It is therefore a crucial epidemiological statistic that assists public health decision makers in the management of an epidemic. We present a new Bayesian tool (EpiLPS) for robust estimation of the time-varying reproduction number. The proposed methodology smooths the epidemic curve and allows to obtain (approximate) point estimates and credible intervals of [Formula: see text] by employing the renewal equation, using Bayesian P-splines coupled with Laplace approximations of the conditional posterior of the spline vector. Two alternative approaches for inference are presented: (1) an approach based on a maximum a posteriori argument for the model hyperparameters, delivering estimates of [Formula: see text] in only a few seconds; and (2) an approach based on a Markov chain Monte Carlo (MCMC) scheme with underlying Langevin dynamics for efficient sampling of the posterior target distribution. Case counts per unit of time are assumed to follow a negative binomial distribution to account for potential overdispersion in the data that would not be captured by a classic Poisson model. Furthermore, after smoothing the epidemic curve, a "plug-in'' estimate of the reproduction number can be obtained from the renewal equation yielding a closed form expression of [Formula: see text] as a function of the spline parameters. The approach is extremely fast and free of arbitrary smoothing assumptions. EpiLPS is applied on data of SARS-CoV-1 in Hong-Kong (2003), influenza A H1N1 (2009) in the USA and on the SARS-CoV-2 pandemic (2020-2021) for Belgium, Portugal, Denmark and France., Competing Interests: The authors have declared that no competing interests exist.

Reliable discrimination of recent influenza A infection from previous exposure using hemagglutination inhibition (HI) or virus neutralization tests is currently not feasible. This is due to low sensitivity of the tests and the interference of antibody responses generated by previous infections. Here we investigate the diagnostic characteristics of a newly developed antibody (HA1) protein microarray using data from cross-sectional serological studies carried out before and after the pandemic of 2009. The data are analysed by mixture models, providing a probabilistic classification of sera (susceptible, prior-exposed, recently infected). Estimated sensitivity and specificity for identifying A/2009 infections are low using HI (66% and 51%), and high when using A/2009 microarray data alone or together with A/1918 microarray data (96% and 95%). As a heuristic, a high A/2009 to A/1918 antibody ratio (>1.05) is indicative of recent infection, while a low ratio is indicative of a pre-existing response, even if the A/2009 titer is high. We conclude that highly sensitive and specific classification of individual sera is possible using the protein microarray, thereby enabling precise estimation of age-specific infection attack rates in the population even if sample sizes are small.

A proper understanding of the infection dynamics of influenza A viruses hinges on the availability of reliable estimates of key epidemiologic parameters such as the reproduction number, intrinsic growth rate, and generation interval. Often the generation interval is assumed to be similar in different settings although there is little evidence justifying this. Here we estimate the generation interval for stratifications based on age, cluster size, and social setting (camp, school, workplace, household) using data from 16 clusters of Novel Influenza A (H1N1) in the Netherlands. Our analyses are based on a Bayesian inferential framework, enabling flexible handling of both missing infection links and missing times of symptoms onset. The analysis indicates that a stratification that allows the generation interval to differ by social setting fits the data best. Specifically, the estimated generation interval was shorter in households (2.1 days [95% credible interval = 1.6-2.9]) and camps (2.3 days [1.4-3.4]) than in workplaces (2.7 days [1.9-3.7]) and schools (3.4 days [2.5-4.5]). Our findings could be the result of differences in the number of contacts between settings, differences in prophylactic use of antivirals between settings, and differences in underreporting.

Prior to 2009, The Netherlands had prepared itself extensively for a potential pandemic. Multidisciplinary guidelines had been drafted to control transmission and limit adverse outcomes for both a phase of early incidental introduction and for a phase with widespread transmission. The Ministry of Health had ensured a supply and distribution schedule for antivirals and negotiated a contract for vaccine purchases. During the pandemic, existing surveillance was expanded, the established infectious disease response structure was activated, and the previously prepared protocols for communication, diagnostics, use of antivirals, and vaccination implementation were operationalized and implemented. When the pandemic turned out to be less severe than many had anticipated, risk communication and rapid modification of guidelines and communication became a major challenge. Antivirals and pandemic vaccines were reserved for those at high risk for severe outcomes only. Overall, the impact of the pandemic was comparable to the impact of an average seasonal influenza epidemic, but with a shift in (severe) outcomes from the very young and elderly toward young adults. Established prepared protocols enabled timely coordinated responses. In preparing for the worst, sufficient attention must be given to preparing for a mild scenario as well.

Sizeable quantities of 2009 pandemic influenza A/H1N1 (H1N1pdm) vaccine in the USA became available at the end of 2009 when the autumn wave of the epidemic was declining. At that point, risk factors for H1N1-related mortality for some of the high-risk groups, particularly adults with underlying health conditions, could be estimated. Although those high-risk groups are natural candidates for being in the top priority tier for vaccine allocation, another candidate group is school-aged children through their role as vectors for transmission affecting the whole community. In this paper, we investigate the question of prioritization for vaccine allocation in a declining epidemic between two groups-a group with a high risk of mortality versus a 'core' group with a relatively low risk of mortality but fuelling transmission in the community. We show that epidemic data can be used, under certain assumptions on future decline, seasonality and vaccine efficacy in different population groups, to give a criterion when initial prioritization of a population group with a sufficiently high risk of epidemic-associated mortality is advisable over the policy of prioritizing the core group.

Background: 18,500 laboratory-confirmed deaths caused by the 2009 pandemic influenza A H1N1 were reported worldwide for the period April, 2009, to August, 2010. This number is likely to be only a fraction of the true number of the deaths associated with 2009 pandemic influenza A H1N1. We aimed to estimate the global number of deaths during the first 12 months of virus circulation in each country., Methods: We calculated crude respiratory mortality rates associated with the 2009 pandemic influenza A H1N1 strain by age (0-17 years, 18-64 years, and >64 years) using the cumulative (12 months) virus-associated symptomatic attack rates from 12 countries and symptomatic case fatality ratios (sCFR) from five high-income countries. To adjust crude mortality rates for differences between countries in risk of death from influenza, we developed a respiratory mortality multiplier equal to the ratio of the median lower respiratory tract infection mortality rate in each WHO region mortality stratum to the median in countries with very low mortality. We calculated cardiovascular disease mortality rates associated with 2009 pandemic influenza A H1N1 infection with the ratio of excess deaths from cardiovascular and respiratory diseases during the pandemic in five countries and multiplied these values by the crude respiratory disease mortality rate associated with the virus. Respiratory and cardiovascular mortality rates associated with 2009 pandemic influenza A H1N1 were multiplied by age to calculate the number of associated deaths., Findings: We estimate that globally there were 201,200 respiratory deaths (range 105,700-395,600) with an additional 83,300 cardiovascular deaths (46,000-179,900) associated with 2009 pandemic influenza A H1N1. 80% of the respiratory and cardiovascular deaths were in people younger than 65 years and 51% occurred in southeast Asia and Africa., Interpretation: Our estimate of respiratory and cardiovascular mortality associated with the 2009 pandemic influenza A H1N1 was 15 times higher than reported laboratory-confirmed deaths. Although no estimates of sCFRs were available from Africa and southeast Asia, a disproportionate number of estimated pandemic deaths might have occurred in these regions. Therefore, efforts to prevent influenza need to effectively target these regions in future pandemics., Funding: None., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The rapid spread of the new influenza virus A(H1N1)v in young age groups in 2009 has been partly attributed to a high transmission intensity in schools. However, detailed characterization of the spread of influenza in school populations has been difficult to obtain, simply because it is very hard to identify who infected whom in a large outbreak. Data collected in large outbreak investigations typically miss many transmission events, and some reported transmission events will be incorrect. Here the authors present robust likelihood-based methods that can be used to analyze outbreak data while explicitly accounting for both missing data and erroneous data. They apply this method to a school-based outbreak of pandemic influenza A(H1N1)v that occurred in London, United Kingdom, in April 2009. The authors show that the generation interval in this school-based population was 2.20 days and that the reproduction number declined coincident with school closure, from 1.33 secondary cases per primary case to 0.43 secondary cases per primary case. These results provide quantitative evidence for the change in influenza transmission that is to be expected from school closure.

Despite considerable research efforts in specific subpopulations, reliable estimates of the infection attack rates and severity of 2009 influenza A (H1N1) in the general population remain scarce. Such estimates are essential to the tailoring of future control strategies. Therefore, 2 serial population-based serologic surveys were conducted, before and after the 2009 influenza A (H1N1) epidemic, in the Netherlands. Random age-stratified samples were obtained using a 2-stage cluster design. Participants donated blood and completed a questionnaire. Data on sentinel general practitioner-attended influenza-like illness and nationwide hospitalization and mortality were used to assess the severity of infection. The estimated infection attack rates were low in the general population (7.6%, 95% confidence interval: 3.6, 11) but high in children aged 5-19 years (35%, 95% confidence interval: 25, 45). The estimated hospitalization and mortality rates per infection increased significantly with age (5-19 years: 0.042% and 0.00094%, respectively; 20-39 years: 0.12% and 0.0025%; 40-59 years: 0.68% and 0.032%; 60-75 years: >0.81% and >0.068%). The high infection attack rate in children and the very low attack rate in older adults, together with the low severity of illness per infection in children but substantial severity in older adults, produced an epidemic with a low overall impact.

Following the emergence of a novel strain of influenza A(H1N1) in Mexico and the United States in April 2009, its epidemiology in Europe during the summer was limited to sporadic and localised outbreaks. Only the United Kingdom experienced widespread transmission declining with school holidays in late July. Using statistical modelling where applicable we explored the following causes that could explain this surprising difference in transmission dynamics: extinction by chance, differences in the susceptibility profile, age distribution of the imported cases, differences in contact patterns, mitigation strategies, school holidays and weather patterns. No single factor was able to explain the differences sufficiently. Hence an additive mixed model was used to model the country-specific weekly estimates of the effective reproductive number using the extinction probability, school holidays and weather patterns as explanatory variables. The average extinction probability, its trend and the trend in absolute humidity were found to be significantly negatively correlated with the effective reproduction number - although they could only explain about 3% of the variability in the model. By comparing the initial epidemiology of influenza A (H1N1) across different European countries, our analysis was able to uncover a possible role for the timing of importations (extinction probability), mixing patterns and the absolute humidity as underlying factors. However, much uncertainty remains. With better information on the role of these epidemiological factors, the control of influenza could be improved., (Copyright © 2011 Elsevier B.V. All rights reserved.)

During emerging epidemics of infectious diseases, it is vital to have up-to-date information on epidemic trends, such as incidence or health care demand, because hospitals and intensive care units have limited excess capacity. However, real-time tracking of epidemics is difficult, because of the inherent delay between onset of symptoms or hospitalizations, and reporting. We propose a robust algorithm to correct for reporting delays, using the observed distribution of reporting delays. We apply the algorithm to pandemic influenza A/H1N1 2009 hospitalizations as reported in the Netherlands. We show that the proposed algorithm is able to provide unbiased predictions of the actual number of hospitalizations in real-time during the ascent and descent of the epidemic. The real-time predictions of admissions are useful to adjust planning in hospitals to avoid exceeding their capacity.

Background: Despite impressive advances in our understanding of the biology of novel influenza A(H1N1) virus, little is as yet known about its transmission efficiency in close contact places such as households, schools, and workplaces. These are widely believed to be key in supporting propagating spread, and it is therefore of importance to assess the transmission levels of the virus in such settings., Methodology/principal Findings: We estimate the transmissibility of novel influenza A(H1N1) in 47 households in the Netherlands using stochastic epidemic models. All households contained a laboratory confirmed index case, and antiviral drugs (oseltamivir) were given to both the index case and other households members within 24 hours after detection of the index case. Among the 109 household contacts there were 9 secondary infections in 7 households. The overall estimated secondary attack rate is low (0.075, 95%CI: 0.037-0.13). There is statistical evidence indicating that older persons are less susceptible to infection than younger persons (relative susceptibility of older persons: 0.11, 95%CI: 0.024-0.43. Notably, the secondary attack rate from an older to a younger person is 0.35 (95%CI: 0.14-0.61) when using an age classification of 12 years, and 0.28 (95%CI: 0.12-0.50) when using an age classification of 18 years., Conclusions/significance: Our results indicate that the overall household transmission levels of novel influenza A(H1N1) in antiviral-treated households were low in the early stage of the epidemic. The relatively high rate of adult-to-child transmission indicates that control measures focused on this transmission route will be most effective in minimizing the total number of infections.

In light of the 2009 influenza pandemic and potential future pandemics, Maria Van Kerkhove and colleagues anticipate six public health challenges and the data needed to support sound public health decision making.

We describe a prioritization scheme for an allocation of a sizeable quantity of vaccine or antivirals in a stratified population. The scheme builds on an optimal strategy for reducing the epidemic's initial growth rate in a stratified mass-action model. The strategy is tested on the EpiSims network describing interactions and influenza dynamics in the population of Utah, where the stratification we have chosen is by age (0-6, 7-13, 14-18, adults). No prior immunity information is available, thus everyone is assumed to be susceptible-this may be relevant, possibly with the exception of persons over 50, to the 2009 H1N1 influenza outbreak. We have found that the top priority in an allocation of a sizeable quantity of seasonal influenza vaccinations goes to young children (0-6), followed by teens (14-18), then children (7-13), with the adult share being quite low. These results, which rely on the structure of the EpiSims network, are compared with the current influenza vaccination coverage levels in the US population.

The emergence and global impact of the novel influenza A(H1N1)v highlights the continuous threat to public health posed by a steady stream of new and unexpected infectious disease outbreaks in animals and humans. Once an emerging epidemic is detected, public health authorities will attempt to mitigate the epidemic by, among other measures, reducing further spread as much as possible. Scarce and/or costly control measures such as vaccines, anti-infective drugs, and social distancing must be allocated while epidemiological characteristics of the disease remain uncertain. Here we present first principles for allocating scarce resources with limited data. We show that under a broad class of assumptions, the simple rule of targeting intervention measures at the group with the highest risk of infection per individual will achieve the largest reduction in the transmission potential of a novel infection. For vaccination of susceptible persons, the appropriate risk measure is force of infection; for social distancing, the appropriate risk measure is incidence of infection. Unlike existing methods that rely on detailed knowledge of group-specific transmission rates, the method described here can be implemented using only data that are readily available during an epidemic, and allows ready adaptation as the epidemic progresses. The need to observe risk of infection helps to focus the ongoing planning and design of new infectious disease surveillance programs; from the presented first principles for allocating scarce resources, we can adjust the prioritization of groups for intervention when new observations on an emerging epidemic become available.

Background: The United States was the second country to have a major outbreak of novel influenza A/H1N1 in what has become a new pandemic. Appropriate public health responses to this pandemic depend in part on early estimates of key epidemiological parameters of the virus in defined populations., Methods: We use a likelihood-based method to estimate the basic reproductive number (R(0)) and serial interval using individual level U.S. data from the Centers for Disease Control and Prevention (CDC). We adjust for missing dates of illness and changes in case ascertainment. Using prior estimates for the serial interval we also estimate the reproductive number only., Results: Using the raw CDC data, we estimate the reproductive number to be between 2.2 and 2.3 and the mean of the serial interval (mu) between 2.5 and 2.6 days. After adjustment for increased case ascertainment our estimates change to 1.7 to 1.8 for R(0) and 2.2 to 2.3 days for mu. In a sensitivity analysis making use of previous estimates of the mean of the serial interval, both for this epidemic (mu = 1.91 days) and for seasonal influenza (mu = 3.6 days), we estimate the reproductive number at 1.5 to 3.1., Conclusions: With adjustments for data imperfections we obtain useful estimates of key epidemiological parameters for the current influenza H1N1 outbreak in the United States. Estimates that adjust for suspected increases in reporting suggest that substantial reductions in the spread of this epidemic may be achievable with aggressive control measures, while sensitivity analyses suggest the possibility that even such measures would have limited effect in reducing total attack rates.

Background: An accurate estimate of the total number of cases and severity of illness of an emerging infectious disease is required both to define the burden of the epidemic and to determine the severity of disease. When a novel pathogen first appears, affected individuals with severe symptoms are more likely to be diagnosed. Accordingly, the total number of cases will be underestimated and disease severity overestimated. This problem is manifest in the current epidemic of novel influenza A/H1N1., Methods and Results: We used a simple approach to leverage measures of incident influenza A/H1N1 among a relatively small and well observed group of US, UK, Spanish and Canadian travelers who had visited Mexico to estimate the incidence among a much larger and less well surveyed population of Mexican residents. We estimate that a minimum of 113,000 to 375,000 cases of novel influenza A/H1N1 have occurred in Mexicans during the month of April, 2009. Such an estimate serves as a lower bound because it does not account for underreporting of cases in travelers or for nonrandom mixing between Mexican residents and visitors, which together could increase the estimates by more than an order of magnitude., Conclusions: We find that the number of cases in Mexican residents may exceed the number of confirmed cases by two to three orders of magnitude. While the extent of disease spread is greater than previously appreciated, our estimate suggests that severe disease is uncommon since the total number of cases is likely to be much larger than those of confirmed cases.

Introductions of the new influenza A(H1N1) variant virus in the Netherlands led to enhanced surveillance and infection control. By 24 June 2009, 115 cases were reported, of whom 44% were indigenously acquired. Severity of disease is similar to reports elsewhere. Our point estimate of the effective reproductive number (Re) for the initial phase of the influenza A(H1N1)v epidemic in the Netherlands was below one. Given that the Re estimate is based on a small number of indigenous cases and a limited time period, it needs to be interpreted cautiously.

Intensive surveillance in Mexico, the US, Canada, Spain and the UK has resulted in the first data on the new influenza A (H1N1) virus. As of yet, it is still unknown if, how and where further transmission within the Netherlands will take place. Therefore, intensified surveillance in the Netherlands is essential. Information on individual patients and their contacts is available through the compulsory notification of the new influenza. The number of general practitioners and nursing homes participating in surveillance through national networks is extended. In addition mortality statistics and internet-based surveillance are being used (the Dutch Great Influenza Survey). Based on studies on previous flu epidemics and mathematical models we can estimate the expected effect of non-pharmaceutical interventions.The reduction of social contacts, in particular through the closure of schools, hand washing routines and wearing of facemasks if done correctly, are expected to be effective non-pharmaceutical interventions in slowing the transmission of the new influenza virus.