Studies needed to address public health challenges of the 2009 H1N1 influenza pandemic: insights from modeling

Influenza vaccination reduced myocardial infarctions in United Kingdom older adults: a prior event rate ratio study

OBJECTIVES

We aimed to estimate the real-world effectiveness of the influenza vaccine against myocardial infarction (MI) and influenza in the decade since adults aged ≥ 65 years were first recommended the vaccine.

STUDY DESIGN AND SETTING

We identified annual cohorts, 1997 to 2011, of adults aged ≥ 65 years, without previous influenza vaccination, from UK general practices, registered with the Clinical Practice Research Datalink. Using a quasi-experimental study design to control for confounding bias, we estimated influenza vaccine effectiveness on hospitalization for MI, influenza, and antibiotic prescriptions for lower respiratory tract infections.

RESULTS

Vaccination was moderately effective against influenza, the prior event rate ratio-adjusted hazard ratios ranging from 0.70 in 1999 to 0.99 in 2001. Prior event rate ratio-adjusted hazard ratios demonstrated a protective effect against MIs, varying between 0.40 in 2010 and 0.89 in 2001. Aggregated across the cohorts, influenza vaccination reduced the risk of MIs by 39% (95% confidence interval: 34%, 44%).

CONCLUSION

Effectiveness of the flu vaccine in preventing MIs in older UK adults is consistent with the limited evidence from clinical trials. Similar trends in effectiveness against influenza and against MIs suggest the risk of influenza mediates the effectiveness against MIs, although divergence in some years implies the mechanism may be complex.

A Tale of Three Recent Pandemics: Influenza, HIV and SARS-CoV-2

Emerging infectious diseases are one of the main threats to public health, with the potential to cause a pandemic when the infectious agent manages to spread globally. The first major pandemic to appear in the 20th century was the influenza pandemic of 1918, caused by the influenza A H1N1 strain that is characterized by a high fatality rate. Another major pandemic was caused by the human immunodeficiency virus (HIV), that started early in the 20th century and remained undetected until 1981. The ongoing HIV pandemic demonstrated a high mortality and morbidity rate, with discrepant impacts in different regions around the globe. The most recent major pandemic event, is the ongoing pandemic of COVID-19, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has caused over 5.7 million deaths since its emergence, 2 years ago. The aim of this work is to highlight the main determinants of the emergence, epidemic response and available countermeasures of these three pandemics, as we argue that such knowledge is paramount to prepare for the next pandemic. We analyse these pandemics’ historical and epidemiological contexts and the determinants of their emergence. Furthermore, we compare pharmaceutical and non-pharmaceutical interventions that have been used to slow down these three pandemics and zoom in on the technological advances that were made in the progress. Finally, we discuss the evolution of epidemiological modelling, that has become an essential tool to support public health policy making and discuss it in the context of these three pandemics. While these pandemics are caused by distinct viruses, that ignited in different time periods and in different regions of the globe, our work shows that many of the determinants of their emergence and countermeasures used to halt transmission were common. Therefore, it is important to further improve and optimize such approaches and adapt it to future threatening emerging infectious diseases.

The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models

Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March-June, 2020) in Santa Clara County, California. Although our estimated basic reproduction number ([Formula: see text]) remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number ([Formula: see text]) varied from 0.18 to 1.02 in April before stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model accurately predicted dynamics through June; however, the model did not predict rising summer cases after shelter-in-place orders were relaxed in June, which, in early July, was reflected in cases but not yet in deaths. While models are critical for informing intervention policy early in an epidemic, their performance will be limited as epidemic dynamics evolve. This paper is one of the first to evaluate the accuracy of an early epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics.

Mathematical model of the feedback between global supply chain disruption and COVID-19 dynamics

The pandemic of COVID-19 has become one of the greatest threats to human health, causing severe disruptions in the global supply chain, and compromising health care delivery worldwide. Although government authorities sought to contain the spread of SARS-CoV-2, by restricting travel and in-person activities, failure to deploy time-sensitive strategies in ramping-up of critical resource production exacerbated the outbreak. Here, we developed a mathematical model to analyze the effects of the interaction between supply chain disruption and infectious disease dynamics using coupled production and disease networks built on global data. Analysis of the supply chain model suggests that time-sensitive containment strategies could be created to balance objectives in pandemic control and economic losses, leading to a spatiotemporal separation of infection peaks that alleviates the societal impact of the disease. A lean resource allocation strategy can reduce the impact of supply chain shortages from 11.91 to 1.11% in North America. Our model highlights the importance of cross-sectoral coordination and region-wise collaboration to optimally contain a pandemic and provides a framework that could advance the containment and model-based decision making for future pandemics.

Assessing the severity of influenza: a role for longitudinal telephone surveys?

During the 2009 influenza pandemic, a rapid assessment of disease severity was a challenge as a significant proportion of cases did not seek medical care; care-seeking behaviour changed and the proportion asymptomatic was unknown. A random-digit-dialling telephone survey was undertaken during the 2011/12 winter season in England and Wales to address the feasibility of answering these questions. A proportional quota sampling strategy was employed based on gender, age group, geographical location, employment status and level of education. Households were recruited pre-season and re-contacted immediately following peak seasonal influenza activity. The pre-peak survey was undertaken in October 2011 with 1061 individuals recruited and the post-peak telephone survey in March 2012. Eight hundred and thirty-four of the 1061 (78.6%) participants were successfully re-contacted. Their demographic characteristics compared well to national census data. In total, 8.4% of participants self-reported an influenza-like illness (ILI) in the previous 2 weeks, with 3.2% conforming to the World Health Organization (WHO) ILI case definition. In total, 29.6% of the cases reported consulting their general practitioner. 54.1% of the 1061 participants agreed to be re-contacted about providing biological samples. A population-based cohort was successfully recruited and followed up. Longitudinal survey methodology provides a practical tool to assess disease severity during future pandemics.

Exploiting routinely collected severe case data to monitor and predict influenza outbreaks

Background

Influenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring purposes, data on severe cases of influenza in England are reported weekly to Public Health England. These data are both readily available and have the potential to provide valuable information to estimate and predict the key transmission features of seasonal and pandemic influenza.

Methods

We propose an epidemic model that links the underlying unobserved influenza transmission process to data on severe influenza cases. Within a Bayesian framework, we infer retrospectively the parameters of the epidemic model for each seasonal outbreak from 2012 to 2015, including: the effective reproduction number; the initial susceptibility; the probability of admission to intensive care given infection; and the effect of school closure on transmission. The model is also implemented in real time to assess whether early forecasting of the number of admissions to intensive care is possible.

Results

Our model of admissions data allows reconstruction of the underlying transmission dynamics revealing: increased transmission during the season 2013/14 and a noticeable effect of the Christmas school holiday on disease spread during seasons 2012/13 and 2014/15. When information on the initial immunity of the population is available, forecasts of the number of admissions to intensive care can be substantially improved.

Conclusion

Readily available severe case data can be effectively used to estimate epidemiological characteristics and to predict the evolution of an epidemic, crucially allowing real-time monitoring of the transmission and severity of the outbreak.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5671-7) contains supplementary material, which is available to authorized users.

Resilience management during large-scale epidemic outbreaks

Assessing and managing the impact of large-scale epidemics considering only the individual risk and severity of the disease is exceedingly difficult and could be extremely expensive. Economic consequences, infrastructure and service disruption, as well as the recovery speed, are just a few of the many dimensions along which to quantify the effect of an epidemic on society's fabric. Here, we extend the concept of resilience to characterize epidemics in structured populations, by defining the system-wide critical functionality that combines an individual's risk of getting the disease (disease attack rate) and the disruption to the system's functionality (human mobility deterioration). By studying both conceptual and data-driven models, we show that the integrated consideration of individual risks and societal disruptions under resilience assessment framework provides an insightful picture of how an epidemic might impact society. In particular, containment interventions intended for a straightforward reduction of the risk may have net negative impact on the system by slowing down the recovery of basic societal functions. The presented study operationalizes the resilience framework, providing a more nuanced and comprehensive approach for optimizing containment schemes and mitigation policies in the case of epidemic outbreaks.

Estimating the burden of scrub typhus: A systematic review

BACKGROUND

Scrub typhus is a vector-borne zoonotic disease that can be life-threatening. There are no licensed vaccines, or vector control efforts in place. Despite increasing awareness in endemic regions, the public health burden and global distribution of scrub typhus remains poorly known.

METHODS

We systematically reviewed all literature from public health records, fever studies and reports available on the Ovid MEDLINE, Embase Classic + Embase and EconLit databases, to estimate the burden of scrub typhus since the year 2000.

FINDINGS

In prospective fever studies from Asia, scrub typhus is a leading cause of treatable non-malarial febrile illness. Sero-epidemiological data also suggest that Orientia tsutsugamushi infection is common across Asia, with seroprevalence ranging from 9.3%-27.9% (median 22.2% IQR 18.6-25.7). A substantial apparent rise in minimum disease incidence (median 4.6/100,000/10 years, highest in China with 11.2/100,000/10 years) was reported through passive national surveillance systems in South Korea, Japan, China, and Thailand. Case fatality risks from areas of reduced drug-susceptibility are reported at 12.2% and 13.6% for South India and northern Thailand, respectively. Mortality reports vary widely around a median mortality of 6.0% for untreated and 1.4% for treated scrub typhus. Limited evidence suggests high mortality in complicated scrub typhus with CNS involvement (13.6% mortality), multi-organ dysfunction (24.1%) and high pregnancy miscarriage rates with poor neonatal outcomes.

INTERPRETATION

Scrub typhus appears to be a truly neglected tropical disease mainly affecting rural populations, but increasingly also metropolitan areas. Rising minimum incidence rates have been reported over the past 8-10 years from countries with an established surveillance system. A wider distribution of scrub typhus beyond Asia is likely, based on reports from South America and Africa. Unfortunately, the quality and quantity of the available data on scrub typhus epidemiology is currently too limited for any economical, mathematical modeling or mapping approaches.

Estimation of the Basic Reproduction Number of Novel Influenza A (H1N1) pdm09 in Elementary Schools Using the SIR Model

Background:

The novel influenza A (H1N1) pdm09 (A/H1N1pdm) pandemic of 2009-2010 had a great impact on society.

Objective:

We analyzed data from the absentee survey, conducted in elementary schools of Oita City, to evaluate the A/H1N1pdm pandemic and to estimate the basic reproductive number (R₀ ) of this novel strain.

Method:

We summarized the overall absentee data and calculated the cumulative infection rate. Then, we classified the data into 3 groups according to school size: small (<300 students), medium (300–600 students), and large (>600 students). Last, we estimated the R₀ value by using the Susceptible-Infected-Recovered (SIR) mathematical model.

Results:

Data from 60 schools and 27,403 students were analyzed. The overall cumulative infection rate was 44.4%. There were no significant differences among the grades, but the cumulative infection rate increased as the school size increased, being 37.7%, 44.4%, and 46.6% in the small, medium, and large school groups, respectively. The optimal R₀ value was 1.33, comparable with that previously reported. The data from the absentee survey were reliable, with no missing values. Hence, the R₀ derived from the SIR model closely reflected the observed R₀ . The findings support previous reports that school children are most susceptible to A/H1N1pdm virus infection and suggest that the scale of an outbreak is associated with the size of the school.

Conclusion:

Our results provide further information about the A/H1N1pdm pandemic. We propose that an absentee survey should be implemented in the early stages of an epidemic, to prevent a pandemic.

Key data for outbreak evaluation: building on the Ebola experience

Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'.

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.

Toward Standardizing a Lexicon of Infectious Disease Modeling Terms

Disease modeling is increasingly being used to evaluate the effect of health intervention strategies, particularly for infectious diseases. However, the utility and application of such models are hampered by the inconsistent use of infectious disease modeling terms between and within disciplines. We sought to standardize the lexicon of infectious disease modeling terms and develop a glossary of terms commonly used in describing models’ assumptions, parameters, variables, and outcomes. We combined a comprehensive literature review of relevant terms with an online forum discussion in a virtual community of practice, mod4PH (Modeling for Public Health). Using a convergent discussion process and consensus amongst the members of mod4PH, a glossary of terms was developed as an online resource. We anticipate that the glossary will improve inter- and intradisciplinary communication and will result in a greater uptake and understanding of disease modeling outcomes in heath policy decision-making. We highlight the role of the mod4PH community of practice and the methodologies used in this endeavor to link theory, policy, and practice in the public health domain.

Age-specific genetic and antigenic variations of influenza A viruses in Hong Kong, 2013-2014

Age-specific genetic and antigenic variations of influenza viruses have not been documented in tropical and subtropical regions. We implemented a systematic surveillance program in two tertiary hospitals in Hong Kong Island, to collect 112 A(H1N1)pdm09 and 254 A(H3N2) positive specimens from 2013 to 2014. Of these, 56 and 72 were identified as genetic variants of the WHO recommended vaccine composition strains, respectively. A subset of these genetic variants was selected for hemagglutination-inhibition (HI) tests, but none appeared to be antigenic variants of the vaccine composition strains. We also found that genetic and antigenicity variations were similar across sex and age groups of ≤18 yrs, 18 to 65 yrs, and ≥65 yrs. Our findings suggest that none of the age groups led other age groups in genetic evolution of influenza virus A strains. Future studies from different regions and longer study periods are needed to further investigate the age and sex heterogeneity of influenza viruses.

Lessons from Ebola: Improving infectious disease surveillance to inform outbreak management

The current Ebola virus disease outbreak in West Africa has revealed serious shortcomings in national and international capacity to detect, monitor, and respond to infectious disease outbreaks as they occur. Recent advances in diagnostics, risk mapping, mathematical modeling, pathogen genome sequencing, phylogenetics, and phylogeography have the potential to improve substantially the quantity and quality of information available to guide the public health response to outbreaks of all kinds.

A Large Proportion of the Mexican Population Remained Susceptible to A(H1N1)pdm09 Infection One Year after the Emergence of 2009 Influenza Pandemic

Background

The 2009 H1N1 influenza pandemic initially affected Mexico from April 2009 to July 2010. By August 2010, a fourth of the population had received the monovalent vaccine against the pandemic virus (A(H1N1)pdm09). To assess the proportion of the Mexican population who remained potentially susceptible to infection throughout the summer of 2010, we estimated the population seroprevalence to A(H1N1)pdm09 in a serosurvey of blood donors.

Methods

We evaluated baseline cross-reactivity to the pandemic strain and set the threshold for seropositivity using pre-pandemic (2005–2008) stored serum samples and sera from confirmed A(H1N1)pdm09 infected individuals. Between June and September 2010, a convenience sample serosurvey of adult blood donors, children, and adolescents was conducted in six states of Mexico. Sera were tested by the microneutralization (MN) and hemagglutination inhibition (HI) assays, and regarded seropositive if antibody titers were equal or exceeded 1:40 for MN and 1:20 for HI. Age-standardized seroprevalence were calculated using the 2010 National Census population.

Results

Sera from 1,484 individuals were analyzed; 1,363 (92%) were blood donors, and 121 (8%) children or adolescents aged ≤19 years. Mean age (standard deviation) was 31.4 (11.5) years, and 276 (19%) were women. A total of 516 (35%) participants declared history of influenza vaccination after April 2009. The age-standardized seroprevalence to A(H1N1)pdm09 was 48% by the MN and 41% by the HI assays, respectively. The youngest quintile, aged 1 to 22 years, had the highest the seroprevalence; 61% (95% confidence interval [CI]: 56, 66%) for MN, and 56% (95% CI: 51, 62%) for HI.

Conclusions

Despite high transmission of A(H1N1)pdm09 observed immediately after its emergence and extensive vaccination, over a half of the Mexican population remained potentially susceptible to A(H1N1)pdm09 infection. Subsequent influenza seasons with high transmission of A(H1N1)pdm09, as 2011–2012 and 2013–2014, are compatible with these findings.

A profile of inactive information seekers on influenza prevention: a survey of health care workers in Central Kentucky

OBJECTIVE

This study developed a profile of inactive information seekers by characterising how they are different from active seekers, identifying possible determinants of inactive seekers and understanding characteristics of frequently asked influenza-related questions.

METHODS

A survey and follow-up interviews were conducted between December 2010 and January 2011. A total of 307 health care workers in three hospitals in Central Kentucky (USA) are included.

RESULTS

Four study groups were formed based on their information-seeking and vaccination uptake status: (1) Inactive Seekers with Vaccination (N = 141); (2) Inactive Seekers without Vaccination (N = 49); (3) Active Seekers with Vaccination (N = 107); and (4) Active Seekers without Vaccination (N = 10). Inactive Seekers without Vaccination are found to be least responsive to health outcomes. Inactive Seeker groups do not prefer to use sources such as Internet or family/friends. In predicting inactive seekers, Information Needs and Knowledge Perception made significant contributions to prediction. The most frequently asked questions included information about survival duration of influenza virus (N = 25) followed by the incubation period for influenza (N = 24).

CONCLUSION

Profiling inactive seekers can serve as a way to better design customised influenza information sources and services for health care workers, thus giving hospitals through medical libraries additional tools to reduce the spread of influenza.

A Conserved Secondary Structural Element in the Coding Region of the Influenza A Virus Nucleoprotein (NP) mRNA Is Important for the Regulation of Viral Proliferation

Influenza A virus is a threat to humans due to seasonal epidemics and infrequent, but dangerous, pandemics that lead to widespread infection and death. Eight segments of RNA constitute the genome of this virus and they encode greater than eight proteins via alternative splicing of coding (+)RNAs generated from the genomic (-)RNA template strand. RNA is essential in its life cycle. A bioinformatics analysis of segment 5, which encodes nucleoprotein, revealed a conserved structural motif in the (+)RNA. The secondary structure proposed by energy minimization and comparative analysis agrees with structure predicted based on experimental data using a 121 nucleotide in vitro RNA construct comprising an influenza A virus consensus sequence and also an entire segment 5 (+)RNA (strain A/VietNam/1203/2004 (H5N1)). The conserved motif consists of three hairpins with one being especially thermodynamically stable. The biological importance of this conserved secondary structure is supported in experiments using antisense oligonucleotides in cell line, which found that disruption of this motif led to inhibition of viral fitness. These results suggest that this conserved motif in the segment 5 (+)RNA might be a candidate for oligonucleotide-based antiviral therapy.

Early Characterization of the Severity and Transmissibility of Pandemic Influenza Using Clinical Episode Data from Multiple Populations

The potential rapid availability of large-scale clinical episode data during the next influenza pandemic suggests an opportunity for increasing the speed with which novel respiratory pathogens can be characterized. Key intervention decisions will be determined by both the transmissibility of the novel strain (measured by the basic reproductive number R0) and its individual-level severity. The 2009 pandemic illustrated that estimating individual-level severity, as described by the proportion pC of infections that result in clinical cases, can remain uncertain for a prolonged period of time. Here, we use 50 distinct US military populations during 2009 as a retrospective cohort to test the hypothesis that real-time encounter data combined with disease dynamic models can be used to bridge this uncertainty gap. Effectively, we estimated the total number of infections in multiple early-affected communities using the model and divided that number by the known number of clinical cases. Joint estimates of severity and transmissibility clustered within a relatively small region of parameter space, with 40 of the 50 populations bounded by: pC, 0.0133-0.150 and R0, 1.09-2.16. These fits were obtained despite widely varying incidence profiles: some with spring waves, some with fall waves and some with both. To illustrate the benefit of specific pairing of rapidly available data and infectious disease models, we simulated a future moderate pandemic strain with pC approximately ×10 that of 2009; the results demonstrating that even before the peak had passed in the first affected population, R0 and pC could be well estimated. This study provides a clear reference in this two-dimensional space against which future novel respiratory pathogens can be rapidly assessed and compared with previous pandemics.

Hospitalization Fatality Risk of Influenza A(H1N1)pdm09: A Systematic Review and Meta-Analysis

During the 2009 influenza pandemic, uncertainty surrounding the severity of human infections with the influenza A(H1N1)pdm09 virus hindered the calibration of the early public health response. The case fatality risk was widely used to assess severity, but another underexplored and potentially more immediate measure is the hospitalization fatality risk (HFR), defined as the probability of death among H1N1pdm09 cases who required hospitalization for medical reasons. In this review, we searched for relevant studies published in MEDLINE (PubMed) and EMBASE between April 1, 2009, and January 9, 2014. Crude estimates of the HFR ranged from 0% to 52%, with higher estimates from tertiary-care referral hospitals in countries with a lower gross domestic product, but in wealthy countries the estimate was 1%-3% in all settings. Point estimates increased substantially with age and with lower gross domestic product. Early in the next pandemic, estimation of a standardized HFR may provide a picture of the severity of infection, particularly if it is presented in comparison with a similarly standardized HFR for seasonal influenza in the same setting.

Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks

Estimating the case-fatality risk (CFR)-the probability that a person dies from an infection given that they are a case-is a high priority in epidemiologic investigation of newly emerging infectious diseases and sometimes in new outbreaks of known infectious diseases. The data available to estimate the overall CFR are often gathered for other purposes (e.g., surveillance) in challenging circumstances. We describe two forms of bias that may affect the estimation of the overall CFR-preferential ascertainment of severe cases and bias from reporting delays-and review solutions that have been proposed and implemented in past epidemics. Also of interest is the estimation of the causal impact of specific interventions (e.g., hospitalization, or hospitalization at a particular hospital) on survival, which can be estimated as a relative CFR for two or more groups. When observational data are used for this purpose, three more sources of bias may arise: confounding, survivorship bias, and selection due to preferential inclusion in surveillance datasets of those who are hospitalized and/or die. We illustrate these biases and caution against causal interpretation of differential CFR among those receiving different interventions in observational datasets. Again, we discuss ways to reduce these biases, particularly by estimating outcomes in smaller but more systematically defined cohorts ascertained before the onset of symptoms, such as those identified by forward contact tracing. Finally, we discuss the circumstances in which these biases may affect non-causal interpretation of risk factors for death among cases.

Population-level susceptibility, severity and spread of pandemic influenza: design of, and initial results from, a pre-pandemic and hibernating pandemic phase study using cross-sectional data from the Health Survey for England (HSE)

Background

Assessing severity and spread of a novel influenza strain at the start of a pandemic is critical for informing a targeted and proportional response. It requires community-level studies to estimate the burden of infection and disease. Rapidly initiating such studies in a pandemic is difficult. The study aims to establish an efficient system allowing real-time assessment of population susceptibility, spread of infection and clinical attack rates in the event of a pandemic.

Methods

We developed and appended additional survey questions and specimen collection to the Health Survey for England (HSE) – a large, annual, rolling nationally representative general population survey recruiting throughout the year – to enable rapid population-based surveys of influenza infection and disease during a pandemic. Using these surveys we can assess the spread of the virus geographically, by age and through time. The data generated can also provide denominators for national estimates of case fatality and hospitalisation rates.Phase 1: we compared retrospectively collected HSE illness rates during the first two infection waves of the 2009 pandemic with the Flu Watch study (a prospective community cohort). Monthly and seasonal age-specific rates of illness and proportion vaccinated were compared.Phase 2: we piloted blood specimen and data collection alongside the 2012–13 HSE. We are developing laboratory methods and protocols for real-time serological assays of a novel pandemic influenza virus using these specimens, and automated programmes for analysing and reporting illness and infection rates.Phase 3: during inter-pandemic years, the study enters a holding phase, where it is included in the yearly HSE ethics application and planning procedures, allowing rapid triggering in a pandemic.Phase 4: once retriggered, the study will utilise the methods developed in phase 2 to monitor the severity and spread of the pandemic in real time.

Results

Phase 1: the rates of reported illness during the first two waves in the HSE underestimated the community burden as measured by Flu Watch, but the patterns of illness by age and time were broadly comparable. The extent of underestimation was greatest for HSE participants interviewed later in the year compared with those interviewed closer to the pandemic. Vaccine uptake in the HSE study was comparable to independent national estimates and the Flu Watch study.Phases 2 and 3: illness data and serological samples from 2018 participants were collected in the 2012–13 HSE and transferred to the University College London Hospital. In the 2013 HSE and onwards, this project was included in the annual HSE ethics and planning rounds.

Conclusions

The HSE’s underestimation of illness rates during the first two waves of the pandemic is probably due to recall bias and the limitation of being able to report only one illness when multiple illnesses per season can occur. Changes to the illness questions (reporting only recent illnesses) should help minimise these issues. Additional prospective follow-up could improve measurement of disease incidence. The representative nature of the HSE allows accurate measurements of vaccine uptake.

Study registration

This study is registered as ISRCTN80214280.

Funding

This project was funded by the NIHR Public Health Research programme and will be published in full inPublic Health Research; Vol. 3, No. 6. See the NIHR Journals Library website for further project information.

Seven challenges in modeling vaccine preventable diseases

Vaccination has been one of the most successful public health measures since the introduction of basic sanitation. Substantial mortality and morbidity reductions have been achieved via vaccination against many infections, and the list of diseases that are potentially controllable by vaccines is growing steadily. We introduce key challenges for modeling in shaping our understanding and guiding policy decisions related to vaccine preventable diseases.

On the uniqueness of epidemic models fitting a normalized curve of removed individuals

The susceptible-infected-removed (SIR) and the susceptible-exposed-infected-removed (SEIR) epidemic models with constant parameters are adequate for describing the time evolution of seasonal diseases for which available data usually consist of fatality reports. The problems associated with the determination of system parameters starts with the inference of the number of removed individuals from fatality data, because the infection to death period may depend on health care factors. Then, one encounters numerical sensitivity problems for the determination of the system parameters from a correct but noisy representative of the number of removed individuals. Finally as the available data is necessarily a normalized one, the models fitting this data may not be unique. We prove that the parameters of the (SEIR) model cannot be determined from the knowledge of a normalized curve of "Removed" individuals and we show that the proportion of removed individuals, [Formula: see text], is invariant under the interchange of the incubation and infection periods and corresponding scalings of the contact rate. On the other hand we prove that the SIR model fitting a normalized curve of removed individuals is unique and we give an implicit relation for the system parameters in terms of the values of [Formula: see text] and [Formula: see text], where [Formula: see text] is the steady state value of [Formula: see text] and [Formula: see text] and [Formula: see text] are the values of [Formula: see text] and its derivative at the inflection point [Formula: see text] of [Formula: see text]. We use these implicit relations to provide a robust method for the estimation of the system parameters and we apply this procedure to the fatality data for the H1N1 epidemic in the Czech Republic during 2009. We finally discuss the inference of the number of removed individuals from observational data, using a clinical survey conducted at major hospitals in Istanbul, Turkey, during 2009 H1N1 epidemic.

Mathematical modelling and prediction in infectious disease epidemiology

We discuss to what extent disease transmission models provide reliable predictions. The concept of prediction is delineated as it is understood by modellers, and illustrated by some classic and recent examples. A precondition for a model to provide valid predictions is that the assumptions underlying it correspond to the reality, but such correspondence is always limited—all models are simplifications of reality. A central tenet of the modelling enterprise is what we may call the ‘robustness thesis’: a model whose assumptions approximately correspond to reality will make predictions that are approximately valid. To examine which of the predictions made by a model are trustworthy, it is essential to examine the outcomes of different models. Thus, if a highly simplified model makes a prediction, and if the same or a very similar prediction is made by a more elaborate model that includes some mechanisms or details that the first model did not, then we gain some confidence that the prediction is robust. An important benefit derived from mathematical modelling activity is that it demands transparency and accuracy regarding our assumptions, thus enabling us to test our understanding of the disease epidemiology by comparing model results and observed patterns. Models can also assist in decision-making by making projections regarding important issues such as intervention-induced changes in the spread of disease.

Inferring influenza infection attack rate from seroprevalence data

Seroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1∶40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN1∶20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3-12, 13-19, 20-29, 30-59 became MN1∶40 seropositive, which was much lower than the 90%-100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.

Developing model-based public health policy through knowledge translation: the need for a 'Communities of Practice'

OBJECTIVES

The 2009 influenza A (H1N1) pandemic prompted public health agencies worldwide to respond in a context of substantial uncertainty. While many lessons around successful management strategies were learned during the influenza A (H1N1) pandemic, the usefulness and impact of mathematical models to optimize policy decisions in protecting public health were poorly realized. The authors explored the experiences of modellers and public health practitioners in trying to develop model-based public health policies in the management of the 2009 influenza A (H1N1) pandemic in Canada.

STUDY DESIGN

A qualitative case study design based on interviews and other textual data was used.

METHODS

Individual interviews were conducted with mathematical modellers and public health professionals from academia and government health departments during the second wave of the 2009 influenza A (H1N1) pandemic (both prior to and following the vaccine roll-out), using a convergent interviewing process. Interviews were supplemented with discussions held during three separate workshops involving representatives from these groups on the role of modelling in pandemic preparedness and responses. NVivo9™ was used to analyse interview data and associated notes.

RESULTS

Mathematical models were underutilized during the response phase of the 2009 influenza A (H1N1) pandemic, largely because many public health professionals were unaware of modelling infrastructure in Canada. Challenges were reflected in three ways: 1) the relevance of models to public health priorities; 2) the need for clear communication and plain language around modelling and its contributions and limitations; and 3) the need for increased trust and collaboration to develop strong working relationships.

CONCLUSIONS

Developing a 'Communities of Practice' between public health professionals and mathematical modellers during inter-pandemic periods based on common targeted goals, using plain language, and where relationships between individuals and organizations are developed early, could be an effective strategy to assist the process of public health policy decision-making, particularly when characterized by high levels of uncertainty.

Case fatality risk of influenza A (H1N1pdm09): a systematic review

BACKGROUND

During the 2009 influenza pandemic, uncertainty surrounding the seriousness of human infections with the H1N1pdm09 virus hindered appropriate public health response. One measure of seriousness is the case fatality risk, defined as the probability of mortality among people classified as cases.

METHODS

We conducted a systematic review to summarize published estimates of the case fatality risk of the pandemic influenza H1N1pdm09 virus. Only studies that reported population-based estimates were included.

RESULTS

We included 77 estimates of the case fatality risk from 50 published studies, about one-third of which were published within the first 9 months of the pandemic. We identified very substantial heterogeneity in published estimates, ranging from less than 1 to more than 10,000 deaths per 100,000 cases or infections. The choice of case definition in the denominator accounted for substantial heterogeneity, with the higher estimates based on laboratory-confirmed cases (point estimates = 0-13,500 per 100,000 cases) compared with symptomatic cases (point estimates = 0-1,200 per 100,000 cases) or infections (point estimates = 1-10 per 100,000 infections). Risk based on symptomatic cases increased substantially with age.

CONCLUSIONS

Our review highlights the difficulty in estimating the seriousness of infection with a novel influenza virus using the case fatality risk. In addition, substantial variability in age-specific estimates complicates the interpretation of the overall case fatality risk and comparisons among populations. A consensus is needed on how to define and measure the seriousness of infection before the next pandemic.

Improving the modeling of disease data from the government surveillance system: a case study on malaria in the Brazilian Amazon

The study of the effect of large-scale drivers (e.g., climate) of human diseases typically relies on aggregate disease data collected by the government surveillance network. The usual approach to analyze these data, however, often ignores a) changes in the total number of individuals examined, b) the bias towards symptomatic individuals in routine government surveillance, and; c) the influence that observations can have on disease dynamics. Here, we highlight the consequences of ignoring the problems listed above and develop a novel modeling framework to circumvent them, which is illustrated using simulations and real malaria data. Our simulations reveal that trends in the number of disease cases do not necessarily imply similar trends in infection prevalence or incidence, due to the strong influence of concurrent changes in sampling effort. We also show that ignoring decreases in the pool of infected individuals due to the treatment of part of these individuals can hamper reliable inference on infection incidence. We propose a model that avoids these problems, being a compromise between phenomenological statistical models and mechanistic disease dynamics models; in particular, a cross-validation exercise reveals that it has better out-of-sample predictive performance than both of these alternative models. Our case study in the Brazilian Amazon reveals that infection prevalence was high in 2004–2008 (prevalence of 4% with 95% CI of 3–5%), with outbreaks (prevalence up to 18%) occurring during the dry season of the year. After this period, infection prevalence decreased substantially (0.9% with 95% CI of 0.8–1.1%), which is due to a large reduction in infection incidence (i.e., incidence in 2008–2010 was approximately one fifth of the incidence in 2004–2008).We believe that our approach to modeling government surveillance disease data will be useful to advance current understanding of large-scale drivers of several diseases.

Disease data collected by the government surveillance system are frequently used to understand the influence of large-scale phenomena (e.g., climate) on human health because these data often have a large temporal and/or geographical span. The down side is that a) these data are often biased towards individuals that come to the health facilities (i.e., symptomatic individuals); and b) the number of individuals examined can vary substantially regardless of concurrent changes in prevalence or incidence (e.g., due to shortage of personnel or supplies in health facilities), directly impacting the number of disease cases detected. Current modeling approaches typically ignore these peculiarities of the government data. Furthermore, current approaches do not take into account that observations directly influence disease dynamics since individuals with a positive diagnosis are often subsequently treated for the disease. In this article, we develop a novel model to circumvent these shortcomings and apply it to simulated data, highlighting how inference on infection incidence and prevalence might be misleading when some of the issues mentioned above are ignored. Finally, we illustrate this model using malaria data from the Brazilian Amazon, revealing the strong role of precipitation on infection prevalence seasonality and striking patterns in infection incidence.

Was mandatory quarantine necessary in China for controlling the 2009 H1N1 pandemic?

The Chinese government enforced mandatory quarantine for 60 days (from 10 May to 8 July 2009) as a preventative strategy to control the spread of the 2009 H1N1 pandemic. Such a prevention strategy was stricter than other non-pharmaceutical interventions that were carried out in many other countries. We evaluated the effectiveness of the mandatory quarantine and provide suggestions for interventions against possible future influenza pandemics. We selected one city, Beijing, as the analysis target. We reviewed the epidemiologic dynamics of the 2009 H1N1 pandemic and the implementation of quarantine measures in Beijing. The infectious population was simulated under two scenarios (quarantined and not quarantined) using a deterministic Susceptible-Exposed-Infectious-Recovered (SEIR) model. The basic reproduction number R0 was adjusted to match the epidemic wave in Beijing. We found that mandatory quarantine served to postpone the spread of the 2009 H1N1 pandemic in Beijing by one and a half months. If mandatory quarantine was not enforced in Beijing, the infectious population could have reached 1,553 by 21 October, i.e., 5.6 times higher than the observed number. When the cost of quarantine is taken into account, mandatory quarantine was not an economically effective intervention approach against the 2009 H1N1 pandemic. We suggest adopting mitigation methods for an influenza pandemic with low mortality and morbidity.

Multiple estimates of transmissibility for the 2009 influenza pandemic based on influenza-like-illness data from small US military populations

Rapidly characterizing the amplitude and variability in transmissibility of novel human influenza strains as they emerge is a key public health priority. However, comparison of early estimates of the basic reproduction number during the 2009 pandemic were challenging because of inconsistent data sources and methods. Here, we define and analyze influenza-like-illness (ILI) case data from 2009-2010 for the 50 largest spatially distinct US military installations (military population defined by zip code, MPZ). We used publicly available data from non-military sources to show that patterns of ILI incidence in many of these MPZs closely followed the pattern of their enclosing civilian population. After characterizing the broad patterns of incidence (e.g. single-peak, double-peak), we defined a parsimonious SIR-like model with two possible values for intrinsic transmissibility across three epochs. We fitted the parameters of this model to data from all 50 MPZs, finding them to be reasonably well clustered with a median (mean) value of 1.39 (1.57) and standard deviation of 0.41. An increasing temporal trend in transmissibility ([Formula: see text], p-value: 0.013) during the period of our study was robust to the removal of high transmissibility outliers and to the removal of the smaller 20 MPZs. Our results demonstrate the utility of rapidly available - and consistent - data from multiple populations.

The consortium for the standardization of influenza seroepidemiology (CONSISE): a global partnership to standardize influenza seroepidemiology and develop influenza investigation protocols to inform public health policy

CONSISE - The consortium for the Standardization of Influenza Seroepidemiology - is a global partnership to develop influenza investigation protocols and standardize seroepidemiology to inform health policy. This international partnership was formed in 2011 and was created out of a need, identified during the 2009 H1N1 pandemic, for timely seroepidemiological data to better estimate pandemic virus infection severity and attack rates to inform policy decisions. CONSISE has developed into a consortium of two interactive working groups: epidemiology and laboratory, with a steering committee composed of individuals from several organizations. CONSISE has had two international meetings with more planned for 2013. We seek additional members from public health agencies, academic institutions and other interested parties.

An infectious disease model on empirical networks of human contact: bridging the gap between dynamic network data and contact matrices

BACKGROUND

The integration of empirical data in computational frameworks designed to model the spread of infectious diseases poses a number of challenges that are becoming more pressing with the increasing availability of high-resolution information on human mobility and contacts. This deluge of data has the potential to revolutionize the computational efforts aimed at simulating scenarios, designing containment strategies, and evaluating outcomes. However, the integration of highly detailed data sources yields models that are less transparent and general in their applicability. Hence, given a specific disease model, it is crucial to assess which representations of the raw data work best to inform the model, striking a balance between simplicity and detail.

METHODS

We consider high-resolution data on the face-to-face interactions of individuals in a pediatric hospital ward, obtained by using wearable proximity sensors. We simulate the spread of a disease in this community by using an SEIR model on top of different mathematical representations of the empirical contact patterns. At the most detailed level, we take into account all contacts between individuals and their exact timing and order. Then, we build a hierarchy of coarse-grained representations of the contact patterns that preserve only partially the temporal and structural information available in the data. We compare the dynamics of the SEIR model across these representations.

RESULTS

We show that a contact matrix that only contains average contact durations between role classes fails to reproduce the size of the epidemic obtained using the high-resolution contact data and also fails to identify the most at-risk classes. We introduce a contact matrix of probability distributions that takes into account the heterogeneity of contact durations between (and within) classes of individuals, and we show that, in the case study presented, this representation yields a good approximation of the epidemic spreading properties obtained by using the high-resolution data.

CONCLUSIONS

Our results mark a first step towards the definition of synopses of high-resolution dynamic contact networks, providing a compact representation of contact patterns that can correctly inform computational models designed to discover risk groups and evaluate containment policies. We show in a typical case of a structured population that this novel kind of representation can preserve in simulation quantitative features of the epidemics that are crucial for their study and management.

Surveillance strategy for early detection of unusual infectious disease events

New pathogens continue to emerge, and the increased connectedness of populations across the globe through international travel and trade favors rapid dispersal of any new disease. The ability to respond to such events has increased but the question is what ‘preparedness’ means at the level of the clinician. Clinicians deal with patients with unexplained illness on a daily basis, and even with syndromes highly indicative of infectious diseases, the cause of illness is often not detected, unless extensive and costly diagnostic work-ups are done. This review discusses innovations in diagnostics and surveillance aimed at early detection of unusual disease. Risk based approaches are promising, but optimal preparedness planning requires multidisciplinary partnerships across domains, and a global translational research agenda to develop tools, systems, and evidence for interventions.

Infection fatality risk of the pandemic A(H1N1)2009 virus in Hong Kong

One measure of the severity of a pandemic influenza outbreak at the individual level is the risk of death among people infected by the new virus. However, there are complications in estimating both the numerator and denominator. Regarding the numerator, statistical estimates of the excess deaths associated with influenza virus infections tend to exceed the number of deaths associated with laboratory-confirmed infection. Regarding the denominator, few infections are laboratory confirmed, while differences in case definitions and approaches to case ascertainment can lead to wide variation in case fatality risk estimates. Serological surveillance can be used to estimate the cumulative incidence of infection as a denominator that is more comparable across studies. We estimated that the first wave of the influenza A(H1N1)pdm09 virus in 2009 was associated with approximately 232 (95% confidence interval: 136, 328) excess deaths of all ages in Hong Kong, mainly among the elderly. The point estimates of the risk of death on a per-infection basis increased substantially with age, from below 1 per 100,000 infections in children to 1,099 per 100,000 infections in those 60-69 years of age. Substantial variation in the age-specific infection fatality risk complicates comparison of the severity of different influenza strains.

Influenza serological studies to inform public health action: best practices to optimise timing, quality and reporting

BACKGROUND

Serological studies can detect infection with a novel influenza virus in the absence of symptoms or positive virology, providing useful information on infection that goes beyond the estimates from epidemiological, clinical and virological data. During the 2009 A(H1N1) pandemic, an impressive number of detailed serological studies were performed, yet the majority of serological data were available only after the first wave of infection. This limited the ability to estimate the transmissibility and severity of this novel infection, and the variability in methodology and reporting limited the ability to compare and combine the serological data.

OBJECTIVES

  To identify best practices for conduct and standardisation of serological studies on outbreak and pandemic influenza to inform public policy.

METHODS/SETTING

An international meeting was held in February 2011 in Ottawa, Canada, to foster the consensus for greater standardisation of influenza serological studies.

RESULTS

Best practices for serological investigations of influenza epidemiology include the following: classification of studies as pre-pandemic, outbreak, pandemic or inter-pandemic with a clearly identified objective; use of international serum standards for laboratory assays; cohort and cross-sectional study designs with common standards for data collection; use of serum banks to improve sampling capacity; and potential for linkage of serological, clinical and epidemiological data. Advance planning for outbreak studies would enable a rapid and coordinated response; inclusion of serological studies in pandemic plans should be considered.

CONCLUSIONS

Optimising the quality, comparability and combinability of influenza serological studies will provide important data upon emergence of a novel or variant influenza virus to inform public health action.

The return of the city-state: urban governance and the New York City H1N1 pandemic

This article examines New York City's response to the 2009 H1N1 pandemic in the context of the post-9/11 US security regime. While the federal level 'all-hazards' approach made for greater depth of support, it also generated unrealistic assumptions at odds with an effective local response. The combination of structurally induced opportunity and actor specific strengths (size, expertise) made for effective local governance by the New York City Department of Health and Mental Hygiene. By underlining the importance of locality as a first line of defence and linking defence function to policy initiative in regard to health governance, this study illustrates the continuing relevance of Weber's insight into the institutional structure of the city.

Estimating age-specific cumulative incidence for the 2009 influenza pandemic: a meta-analysis of A(H1N1)pdm09 serological studies from 19 countries

Background

The global impact of the 2009 influenza A(H1N1) pandemic (H1N1pdm) is not well understood.

Objectives

We estimate overall and age‐specific prevalence of cross‐reactive antibodies to H1N1pdm virus and rates of H1N1pdm infection during the first year of the pandemic using data from published and unpublished H1N1pdm seroepidemiological studies.

Methods

Primary aggregate H1N1pdm serologic data from each study were stratified in standardized age groups and evaluated based on when sera were collected in relation to national or subnational peak H1N1pdm activity. Seropositivity was assessed using well‐described and standardized hemagglutination inhibition (HI titers ≥32 or ≥40) and microneutralization (MN ≥ 40) laboratory assays. The prevalence of cross‐reactive antibodies to the H1N1pdm virus was estimated for studies using sera collected prior to the start of the pandemic (between 2004 and April 2009); H1N1pdm cumulative incidence was estimated for studies in which collected both pre‐ and post‐pandemic sera; and H1N1pdm seropositivity was calculated from studies with post‐pandemic sera only (collected between December 2009–June 2010).

Results

Data from 27 published/unpublished studies from 19 countries/administrative regions – Australia, Canada, China, Finland, France, Germany, Hong Kong SAR, India, Iran, Italy, Japan, Netherlands, New Zealand, Norway, Reunion Island, Singapore, United Kingdom, United States, and Vietnam – were eligible for inclusion. The overall age‐standardized pre‐pandemic prevalence of cross‐reactive antibodies was 5% (95%CI 3–7%) and varied significantly by age with the highest rates among persons ≥65 years old (14% 95%CI 8–24%). Overall age‐standardized H1N1pdm cumulative incidence was 24% (95%CI 20–27%) and varied significantly by age with the highest in children 5–19 (47% 95%CI 39–55%) and 0–4 years old (36% 95%CI 30–43%).

Conclusions

Our results offer unique insight into the global impact of the H1N1 pandemic and highlight the need for standardization of seroepidemiological studies and for their inclusion in pre‐pandemic preparedness plans. Our results taken together with recent global pandemic respiratory‐associated mortality estimates suggest that the case fatality ratio of the pandemic virus was approximately 0·02%.

Influenza infection rates, measurement errors and the interpretation of paired serology

Serological studies are the gold standard method to estimate influenza infection attack rates (ARs) in human populations. In a common protocol, blood samples are collected before and after the epidemic in a cohort of individuals; and a rise in haemagglutination-inhibition (HI) antibody titers during the epidemic is considered as a marker of infection. Because of inherent measurement errors, a 2-fold rise is usually considered as insufficient evidence for infection and seroconversion is therefore typically defined as a 4-fold rise or more. Here, we revisit this widely accepted 70-year old criterion. We develop a Markov chain Monte Carlo data augmentation model to quantify measurement errors and reconstruct the distribution of latent true serological status in a Vietnamese 3-year serological cohort, in which replicate measurements were available. We estimate that the 1-sided probability of a 2-fold error is 9.3% (95% Credible Interval, CI: 3.3%, 17.6%) when antibody titer is below 10 but is 20.2% (95% CI: 15.9%, 24.0%) otherwise. After correction for measurement errors, we find that the proportion of individuals with 2-fold rises in antibody titers was too large to be explained by measurement errors alone. Estimates of ARs vary greatly depending on whether those individuals are included in the definition of the infected population. A simulation study shows that our method is unbiased. The 4-fold rise case definition is relevant when aiming at a specific diagnostic for individual cases, but the justification is less obvious when the objective is to estimate ARs. In particular, it may lead to large underestimates of ARs. Determining which biological phenomenon contributes most to 2-fold rises in antibody titers is essential to assess bias with the traditional case definition and offer improved estimates of influenza ARs.

Each year, seasonal influenza is responsible for about three to five million severe illnesses and about 250,000 to 500,000 deaths worldwide. In order to assess the burden of disease and guide control policies, it is important to quantify the proportion of people infected by an influenza virus each year. Since infection usually leaves a “signature” in the blood of infected individuals (namely a rise in antibodies), a standard protocol consists in collecting blood samples in a cohort of subjects and determining the proportion of those who experienced such rise. However, because of inherent measurement errors, only large rises are accounted for in the standard 4-fold rise case definition. Here, we revisit this 70 year old and widely accepted and applied criterion. We present innovative statistical techniques to better capture the impact of measurement errors and improve our interpretation of the data. Our analysis suggests that the number of people infected by an influenza virus each year might be substantially larger than previously thought, with important implications for our understanding of the transmission and evolution of influenza – and the nature of infection.

Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm

BACKGROUND

Mathematical and computational models for infectious diseases are increasingly used to support public-health decisions; however, their reliability is currently under debate. Real-time forecasts of epidemic spread using data-driven models have been hindered by the technical challenges posed by parameter estimation and validation. Data gathered for the 2009 H1N1 influenza crisis represent an unprecedented opportunity to validate real-time model predictions and define the main success criteria for different approaches.

METHODS

We used the Global Epidemic and Mobility Model to generate stochastic simulations of epidemic spread worldwide, yielding (among other measures) the incidence and seeding events at a daily resolution for 3,362 subpopulations in 220 countries. Using a Monte Carlo Maximum Likelihood analysis, the model provided an estimate of the seasonal transmission potential during the early phase of the H1N1 pandemic and generated ensemble forecasts for the activity peaks in the northern hemisphere in the fall/winter wave. These results were validated against the real-life surveillance data collected in 48 countries, and their robustness assessed by focusing on 1) the peak timing of the pandemic; 2) the level of spatial resolution allowed by the model; and 3) the clinical attack rate and the effectiveness of the vaccine. In addition, we studied the effect of data incompleteness on the prediction reliability.

RESULTS

Real-time predictions of the peak timing are found to be in good agreement with the empirical data, showing strong robustness to data that may not be accessible in real time (such as pre-exposure immunity and adherence to vaccination campaigns), but that affect the predictions for the attack rates. The timing and spatial unfolding of the pandemic are critically sensitive to the level of mobility data integrated into the model.

CONCLUSIONS

Our results show that large-scale models can be used to provide valuable real-time forecasts of influenza spreading, but they require high-performance computing. The quality of the forecast depends on the level of data integration, thus stressing the need for high-quality data in population-based models, and of progressive updates of validated available empirical knowledge to inform these models.

Epidemic and intervention modelling--a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic

PROBLEM

Outbreak analysis and mathematical modelling are crucial for planning public health responses to infectious disease outbreaks, epidemics and pandemics. This paper describes the data analysis and mathematical modelling undertaken during and following the 2009 influenza pandemic, especially to inform public health planning and decision-making.

APPROACH

Soon after A(H1N1)pdm09 emerged in North America in 2009, the World Health Organization convened an informal mathematical modelling network of public health and academic experts and modelling groups. This network and other modelling groups worked with policy-makers to characterize the dynamics and impact of the pandemic and assess the effectiveness of interventions in different settings.

SETTING

The 2009 A(H1N1) influenza pandemic.

RELEVANT CHANGES

Modellers provided a quantitative framework for analysing surveillance data and for understanding the dynamics of the epidemic and the impact of interventions. However, what most often informed policy decisions on a day-to-day basis was arguably not sophisticated simulation modelling, but rather, real-time statistical analyses based on mechanistic transmission models relying on available epidemiologic and virologic data.

LESSONS LEARNT

A key lesson was that modelling cannot substitute for data; it can only make use of available data and highlight what additional data might best inform policy. Data gaps in 2009, especially from low-resource countries, made it difficult to evaluate severity, the effects of seasonal variation on transmission and the effectiveness of non-pharmaceutical interventions. Better communication between modellers and public health practitioners is needed to manage expectations, facilitate data sharing and interpretation and reduce inconsistency in results.

Hospitalization of influenza-like illness patients recommended by general practitioners in France between 1997 and 2010

Please cite this paper as: Pelat et al. (2012) Hospitalization of influenza‐like illness patients recommended by general practitioners in France between 1997 and 2010. Influenza and Other Respiratory Viruses DOI: 10.1111/j.1750‐2659.2012.00356.x.

Background  The case–hospitalization ratio (CHR) is a key quantity for the management of emerging pathogens such as pandemic influenza. Yet, few running surveillance systems prospectively monitor the CHR during influenza epidemics. Here, we analyze the proportion of recommended hospitalizations (PRH) among influenza‐like illness (ILI) patients attended in general practice in France and compare the PRH observed during the 2009–2010 A(H1N1) pandemic with the one of the twelve previous seasons.

Methods  ILI cases were recorded by general practitioners (GPs) involved in surveillance, who indicated for each case whether they recommended hospitalization. We stratify the analysis by age, sex, and viral subtype. We investigate the reasons why GPs recommended hospitalization and the presence of risk factors for pandemic A(H1N1) complications.

Results  The average PRH over the seasons 1997–1998 to 2008–2009 was 3·4‰ (3–3·9). It was three times higher during the 2009–2010 pandemic than during seasonal influenza epidemics (OR = 2·89, 95% CI: 2·28–3·64). The highest increase was among 20–39‐year‐old women: OR = 11·8 (5·04–29·59). Overall, the principal reasons for recommending hospitalization were “respiratory problems” and “bad general condition.” However, during the pandemic, “age” (mainly associated with infants), “pregnancy,” and “diagnostic” became more frequent than before (P < 0·001). Finally, pregnancy was the reported risk factor for pandemic A(H1N1) complications that had the largest impact on hospitalization recommendation during the pandemic (OR = 38·62, P < 0·001).

Conclusion  Easily implemented in surveillance systems, this protocol has the potential to reveal changes in hospitalization recommendation by GPs. Moreover, if the right data are collected alongside, it could give timely insights into epidemic severity.

Serologically confirmed household transmission of 2009 pandemic influenza A (H1N1) virus during the first pandemic wave--New York City, April-May 2009

BACKGROUND

Understanding transmissibility of influenza viruses within households is critical for guiding public health response to pandemics. We studied serologically confirmed infection and disease among household contacts of index case patients with 2009 pandemic influenza A (H1N1) virus (pH1N1) infection in a setting of minimal community pH1N1 transmission.

METHODS

We defined index case patients as students and staff of a New York City high school with laboratory-confirmed pH1N1 infection during the earliest phase of the pH1N1 outbreak in April 2009. We visited households of index case patients twice, once in early May and again in June/July 2009. At each visit, household members (both index case patents and household contacts) provided serum samples and completed questionnaires about illness and possible risk factors. Serologic testing was performed using microneutralization and hemagglutination-inhibition assays.

RESULTS

Of 79 eligible household contacts in 28 households, 19% had serologically confirmed pH1N1 infection, and 28% of those infected were asymptomatic. Serologically confirmed infection varied by age among household contacts: 36% of contacts younger than 10 years were infected, compared with 46% of contacts age 10-18 years, 8% of contacts aged 19-54 years, and 22% of contacts aged 55 years and older.

CONCLUSIONS

Infection rates were high for household contacts of persons with confirmed pH1N1, particularly for contacts aged 10-18 years, and asymptomatic infection was common. Efforts to reduce household transmission during influenza pandemics are important adjuncts to strategies to reduce community illness.

Age-dependent patterns of infection and severity explaining the low impact of 2009 influenza A (H1N1): evidence from serial serologic surveys in the Netherlands

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.

Using experimental human influenza infections to validate a viral dynamic model and the implications for prediction

The aim of this work was to use experimental infection data of human influenza to assess a simple viral dynamics model in epithelial cells and better understand the underlying complex factors governing the infection process. The developed study model expands on previous reports of a target cell-limited model with delayed virus production. Data from 10 published experimental infection studies of human influenza was used to validate the model. Our results elucidate, mechanistically, the associations between epithelial cells, human immune responses, and viral titres and were supported by the experimental infection data. We report that the maximum total number of free virions following infection is 10(3)-fold higher than the initial introduced titre. Our results indicated that the infection rates of unprotected epithelial cells probably play an important role in affecting viral dynamics. By simulating an advanced model of viral dynamics and applying it to experimental infection data of human influenza, we obtained important estimates of the infection rate. This work provides epidemiologically meaningful results, meriting further efforts to understand the causes and consequences of influenza A infection.

A cloud-based simulation architecture for pandemic influenza simulation

High-fidelity simulations of pandemic outbreaks are resource consuming. Cluster-based solutions have been suggested for executing such complex computations. We present a cloud-based simulation architecture that utilizes computing resources both locally available and dynamically rented online. The approach uses the Condor framework for job distribution and management of the Amazon Elastic Computing Cloud (EC2) as well as local resources. The architecture has a web-based user interface that allows users to monitor and control simulation execution. In a benchmark test, the best cost-adjusted performance was recorded for the EC2 H-CPU Medium instance, while a field trial showed that the job configuration had significant influence on the execution time and that the network capacity of the master node could become a bottleneck. We conclude that it is possible to develop a scalable simulation environment that uses cloud-based solutions, while providing an easy-to-use graphical user interface.