Synthesising evidence to estimate pandemic (2009) A/H1N1 influenza severity in 2009–2011 (original) (raw)
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Changes in severity of 2009 pandemic A/H1N1 influenza in England: a Bayesian evidence synthesis
British Medical Journal, 2011
"OBJECTIVE: To assess the impact of the 2009 A/H1N1 influenza pandemic in England during the two waves of activity up to end of February 2010 by estimating the probabilities of cases leading to severe events and the proportion of the population infected. DESIGN: A Bayesian evidence synthesis of all available relevant surveillance data in England to estimate severity of the pandemic. DATA SOURCES: All available surveillance systems relevant to the pandemic 2009 A/H1N1 influenza outbreak in England from June 2009 to February 2010. Pre-existing influenza surveillance systems, including estimated numbers of symptomatic cases based on consultations to the health service for influenza-like illness and cross sectional population serological surveys, as well as systems set up in response to the pandemic, including follow-up of laboratory confirmed cases up to end of June 2009 (FF100 and Fluzone databases), retrospective and prospective follow-up of confirmed hospitalised cases, and reported deaths associated with pandemic 2009 A/H1N1 influenza. Main outcome measures Age specific and wave specific probabilities of infection and symptomatic infection resulting in hospitalisation, intensive care admission, and death, as well as infection attack rates (both symptomatic and total). The probabilities of intensive care admission and death given hospitalisation over time are also estimated to evaluate potential changes in severity across waves. RESULTS: In the summer wave of A/H1N1 influenza, 0.54% (95% credible interval 0.33% to 0.82%) of the estimated 606,100 (419,300 to 886,300) symptomatic cases were hospitalised, 0.05% (0.03% to 0.08%) entered intensive care, and 0.015% (0.010% to 0.022%) died. These correspond to 3200 (2300 to 4700) hospital admissions, 310 (200 to 480) intensive care admissions, and 90 (80 to 110) deaths in the summer wave. In the second wave, 0.55% (0.28% to 0.89%) of the 1,352,000 (829,900 to 2,806,000) estimated symptomatic cases were hospitalised, 0.10% (0.05% to 0.16%) were admitted to intensive care, and 0.025% (0.013% to 0.040%) died. These correspond to 7500 (5900 to 9700) hospitalisations, 1340 (1030 to 1790) admissions to intensive care, and 240 (310 to 380) deaths. Just over a third (35% (26% to 45%)) of infections were estimated to be symptomatic. The estimated probabilities of infections resulting in severe events were therefore 0.19% (0.12% to 0.29%), 0.02% (0.01% to 0.03%), and 0.005% (0.004% to 0.008%) in the summer wave for hospitalisation, intensive care admission, and death respectively. The corresponding second wave probabilities are 0.19% (0.10% to 0.32%), 0.03% (0.02% to 0.06%), and 0.009% (0.004% to 0.014%). An estimated 30% (20% to 43%) of hospitalisations were detected in surveillance systems in the summer, compared with 20% (15% to 25%) in the second wave. Across the two waves, a mid-estimate of 11.2% (7.4% to 18.9%) of the population of England were infected, rising to 29.5% (16.9% to 64.1%) in 5-14 year olds. Sensitivity analyses to the evidence included suggest this infection attack rate could be as low as 5.9% (4.2% to 8.7%) or as high as 28.4% (26.0% to 30.8%). In terms of the probability that an infection leads to death in the second wave, these correspond, respectively, to a high estimate of 0.017% (0.011% to 0.024%) and a low estimate of 0.0027% (0.0024% to 0.0031%). CONCLUSIONS: This study suggests a mild pandemic, characterised by case and infection severity ratios increasing between waves. Results suggest low ascertainment rates, highlighting the importance of systems enabling early robust estimation of severity, to inform optimal public health responses, particularly in light of the apparent resurgence of the 2009 A/H1N1 strain in the 2010-11 influenza season."
Plos One, 2011
Estimating the age-specific incidence of an emerging pathogen is essential for understanding its severity and transmission dynamics. This paper describes a statistical method that uses likelihoods to estimate incidence from sequential serological data. The method requires information on seroconversion intervals and allows integration of information on the temporal distribution of cases from clinical surveillance. Among a family of candidate incidences, a likelihood function is derived by reconstructing the change in seroprevalence from seroconversion following infection and comparing it with the observed sequence of positivity among the samples. This method is applied to derive the cumulative and weekly incidence of A/H1N1 pandemic influenza in England during the second wave using sera taken between September 2009 and February 2010 in four age groups (1-4, 5-14, 15-24, 25-44 years). The highest cumulative incidence was in 5-14 year olds (59%, 95% credible interval (CI): 52%, 68%) followed by 1-4 year olds (49%, 95% CI: 38%, 61%), rates 20 and 40 times higher respectively than estimated from clinical surveillance. The method provides a more accurate and continuous measure of incidence than achieved by comparing prevalence in samples grouped by time period.
Ricerche di Matematica
Mathematical models of the spread and control of infectious diseases in humans should not simply be mathematically interesting. They ought also to provide some qualitative or quantitative information that can in some way be of interest for public health. In many cases, even simple mathematical models have been able to fulfill the difficult task of providing help to public health scientists to better understand the interplay/interdependence between the dynamics of the disease and the control measures to be enacted. Four pillars of public health campaigns for controlling the spread of infectious diseases are: treating infectious humans; vaccination to prevent infection; reduction of risky behaviors; and vector control, in the case of vector borne diseases. All these pillars depend on various factors of heterogeneity that must be taken into account if we want to pass from a vague utility to some more robust role for mathematical epidemiology. The first heterogeneity factor is the age structure of population. First and foremost, the risk of disease-induced mortality for many diseases is age dependent, with children and elderly with a higher risk of fatality. The pattern of contacts is also strongly B B. Buonomo
arXiv (Cornell University), 2019
Understanding age-group dynamics of infectious diseases is a fundamental issue for both scientific study and policymaking. Age-structure epidemic models were developed in order to study and improve our understanding of these dynamics. By fitting the models to incidence data of real outbreaks one can infer estimates of key epidemiological parameters. However, estimation of the transmission in an age-structured populations requires first to define the age-groups of interest. Misspecification in representing the heterogeneity in the age-dependent transmission rates can potentially lead to biased estimation of parameters. We develop the first statistical, data-driven methodology for deciding on the best partition of incidence data into age-groups. The method employs a top-down hierarchical partitioning algorithm, with a metric distance built for maximizing mathematical identifiability of the transmission matrix, and a stopping criteria based on significance testing. The methodology is tested using simulations showing good statistical properties. The methodology is then applied to influenza incidence data of 14 seasons in order to extract the significant age-group clusters in each season.
PLOS Medicine, 2019
Background Measures of the contribution of influenza to Streptococcus pneumoniae infections, both in the seasonal and pandemic setting, are needed to predict the burden of secondary bacterial infections in future pandemics to inform stockpiling. The magnitude of the interaction between these two pathogens has been difficult to quantify because both infections are mainly clinically diagnosed based on signs and symptoms; a combined viral-bacterial testing is rarely performed in routine clinical practice; and surveillance data suffer from confounding problems common to all ecological studies. We proposed a novel multivariate model for age-stratified disease incidence, incorporating contact patterns and estimating disease transmission within and across groups. Methods and findings We used surveillance data from England over the years 2009 to 2017. Influenza infections were identified through the virological testing of samples taken from patients diagnosed with influenza-like illness (ILI) within the sentinel scheme run by the Royal College of General Practitioners (RCGP). Invasive pneumococcal disease (IPD) cases were routinely reported to Public Health England (PHE) by all the microbiology laboratories included in the national surveillance system. IPD counts at week t, conditional on the previous time point t−1, were assumed to be negative binomially distributed. Influenza counts were linearly included in the model for the mean IPD counts along with an endemic component describing some seasonal background and an autoregressive component mimicking pneumococcal transmission. Using age-specific counts, Akaike information criterion (AIC)-based model selection suggested that the best fit was obtained when the endemic component was expressed as a function of observed temperature and rainfall. Pneumococcal transmission within the same age group was estimated to explain 33.0% (confidence interval [CI] 24.9%-39.9%) of new
Semi-parametric estimation of age-time specific infection incidence from serial prevalence data
Statistics in Medicine, 1999
Many infections cause lasting detectable immune responses, whose prevalence can be estimated from cross-sectional surveys. However, such surveys do not provide direct information on the incidence of infection. We address the issue of estimating age and time specific incidence from a series of prevalence surveys under the assumption that incidence changes exponentially with time, but make no assumption about the age specific incidence. We show that these assumptions lead to a proportional hazards model and estimate its parameters using semi-parametric maximum likelihood methods. The method is applied to tuberculin surveys in The Netherlands to explore age dependence of the risk of tuberculous infection in the presence of a strong secular decline in this risk.
Epidemics, 2014
Information about infectious disease outbreaks is often gathered indirectly, from doctor's reports and health board records. It also typically underestimates the actual number of cases, but the relationship between the observed proxies and the numbers that drive the diseases is complicated, nonlinear and potentially time- and state-dependent. We use a combination of data collection from the 2009-2010 H1N1 outbreak in Malta, compartmental modelling and Bayesian inference to explore the effect of using various sources of information (consultations, doctor's diagnose, swabbing and molecular testing) on estimation of the effective basic reproduction ratio, R(t). Different proxies and different sampling rates (daily and weekly) lead to similar behaviour of R(t) as the epidemic unfolds, although individual parameters (force of infection, length of latent and infectious period) vary. We also demonstrate that the relationship between different proxies varies as epidemic progresses, ...
Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London
Proceedings of the National Academy of Sciences, 2011
The tracking and projection of emerging epidemics is hindered by the disconnect between apparent epidemic dynamics, discernible from noisy and incomplete surveillance data, and the underlying, imperfectly observed, system. Behavior changes compound this, altering both true dynamics and reporting patterns, particularly for diseases with nonspecific symptoms, such as influenza. We disentangle these effects to unravel the hidden dynamics of the 2009 influenza A/H1N1pdm pandemic in London, where surveillance suggests an unusual dominant peak in the summer. We embed an age-structured model into a Bayesian synthesis of multiple evidence sources to reveal substantial changes in contact patterns and health-seeking behavior throughout the epidemic, uncovering two similar infection waves, despite large differences in the reported levels of disease. We show how this approach, which allows for real-time learning about model parameters as the epidemic progresses, is also able to provide a sequence of nested projections that are capable of accurately reflecting the epidemic evolution.
An analytical approach to evaluate the impact of age demographics in a pandemic
Stochastic Environmental Research and Risk Assessment
The time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process. This study introduces a sixstate compartmental model to explain and assess the impact of age demographics by designing a dynamic, explainable analytics model of the SARS-CoV-2 coronavirus. An age-stratified mathematical model taking the form of a deterministic system of ordinary differential equations divides the population into different age groups to better understand and assess the impact of age on mortality. It also provides a more accurate and effective interpretation of the disease evolution, specifically in terms of the cumulative numbers of infected cases and deaths. The proposed Kermack-Mckendrick model is incorporated into a non-linear least-squares optimization curve-fitting problem whose optimized parameters are numerically obtained using the Levenberg-Marquard algorithm. The curve-fitting model's efficiency is proved by testing the agestratified model's performance on three U.S. states: Connecticut, North Dakota, and South Dakota. Our results confirm that splitting the population into different age groups leads to better fitting and forecasting results overall as compared to those achieved by the traditional method, i.e., without age groups. By using comprehensive models that account for age, gender, and ethnicity, regional public health authorities may be able to avoid future epidemics from inflicting more fatalities and establish a public health policy that reduces the burden on the elderly population.
Pandemic versus Epidemic Influenza Mortality: A Pattern of Changing Age Distribution
Journal of Infectious Diseases, 1998
Almost all deaths related to current influenza epidemics occur among the elderly. However, mortality was greatest among the young during the 1918-1919 pandemic. This study compared the age distribution of influenza-related deaths in the United States during this century's three influenza A pandemics with that of the following epidemics. Half of influenza-related deaths during the 1968-1969 influenza A (H3N2) pandemic and large proportions of influenza-related deaths during the 1957-1958 influenza A (H2N2) and the 1918-1919 influenza A (H1N1) pandemics occurred among persons õ65 years old. However, this group accounted for decrementally smaller proportions of deaths during the first decade following each pandemic. A model suggested that this mortality pattern may be explained by selective acquisition of protection against fatal illness among younger persons. The large proportion of influenza-related deaths during each pandemic and the following decade among persons õ65 years old should be considered in planning for pandemics.