Model-based reconstruction of an epidemic using multiple datasets: understanding influenza A/H1N1 pandemic dynamics in Israel

Abstract

Intensified surveillance during the 2009 A/H1N1 influenza pandemic in Israel resulted in large virological and serological datasets, presenting a unique opportunity for investigating the pandemic dynamics. We employ a conditional likelihood approach for fitting a disease transmission model to virological and serological data, conditional on clinical data. The model is used to reconstruct the temporal pattern of the pandemic in Israel in five age-groups and evaluate the factors that shaped it. We estimate the reproductive number at the beginning of the pandemic to be R ¼ 1.4. We find that the combined effect of varying absolute humidity conditions and school vacations (SVs) is responsible for the infection pattern, characterized by three epidemic waves. Overall attack rate is estimated at 32% (28-35%) with a large variation among the age-groups: the highest attack rates within school children and the lowest within the elderly. This pattern of infection is explained by a combination of the age-group contact structure and increasing immunity with age. We assess that SVs increased the overall attack rates by prolonging the pandemic into the winter. Vaccinating school children would have been the optimal strategy for minimizing infection rates in all age-groups.

Evidence Synthesis for Stochastic Epidemic Models

“…Due to the presence of asymptomatic infection, this scale could not be derived while the epidemic is ongoing from data on general practice (GP) consultations for influenza-like illness and associated virological swabbing alone. A similar approach is applied to data from Israel [63], and [20] extend this work to look at the changes in the immunity profile of a population and the fluctuating transmissibility of the virus between temporally distinct waves of infection. Given the importance of serological data, [56] develops the approach further in application to Dutch A/H1N1pdm influenza, taking into account the sensitivity and specificity of the serological testing process.…”

“…These explanatory data can come in many forms: [6] and [48] use vaccination data to inform transition rates out of a susceptible state; [10] use commuting data to describe inter-region transmission; [63] relate transmission of A/H1N1pdm influenza in Israel to an index of 'mean absolute humidity'. One particularly successful example of this type of data has been the use of air traffic data in the GLEAM system for the global tracking (and prediction) of a pandemic influenza outbreak [e.g.…”

Characterising model dynamics using sparse grid interpolation: Parameter estimation of cholera

“…In the domain of epidemiology, many parameters are not easily derived from literature, nor directly observable, and yet are indispensable to characterizing the force of infection within a population. Parameter estimation in epidemiology usually relies on approaches like Bayesian [4,9,18], likelihood-based [19,34,35,38], evolutionary computing [1], and least squares methods [7,31]. We propose an alternative parameter estimation strategy that can provide dynamical snapshots of model behaviour within specified parameter spaces.…”

Modelling and parameter inference of predator–prey dynamics in heterogeneous environments using the direct integral approach

“…Fitting models to time-series data has become standard practice in epidemiology [18][19][20][21][22][23][24][25][26], while in ecology it is much less used, due mainly to the lack of sufficient data. Nevertheless, some important theoretical and practical contributions include [27][28][29][30].…”