Real-time epidemic forecasting for pandemic influenza

Hall, I.; Gani, Raymond; Hughes, Helen E.; Leach, Stephen

doi:10.1017/s0950268806007084

Cited by 84 publications

(90 citation statements)

References 21 publications

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“…Considering the large number of approaches that provide inference on influenza activity (16)(17)(18)(19), does this mean that the current version of GFT is not useful? No, greater value can be obtained by combining GFT with other near-real time health data (2,20).…”

Section: Big Data Hubrismentioning

confidence: 99%

The Parable of Google Flu: Traps in Big Data Analysis

Lazer

Kennedy

King

et al. 2014

Science

2,084

1,520

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Section: Big Data Hubrismentioning

confidence: 99%

The Parable of Google Flu: Traps in Big Data Analysis

Lazer

Kennedy

King

et al. 2014

Science

2,084

1,520

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show abstract

“…This type of approach is common to the literature on real-time modelling prior to 2009, in which the proposed methodologies are heavily reliant on an idealised set of circumstances and/or on ad hoc estimation methods. 4 Bayesian statistical epidemic models provide a natural, rigorous framework for the incorporation of relevant contemporaneous surveillance data into the modelling process, alongside collateral information that may be available from other sources. Such models have been used in the context of real-time monitoring for other infectious diseases.…”

Section: Discussionmentioning

confidence: 99%

Real-time modelling of a pandemic influenza outbreak

Birrell

Pebody

Charlett

et al. 2017

Health Technol Assess

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Background Real-time modelling is an essential component of the public health response to an outbreak of pandemic influenza in the UK. A model for epidemic reconstruction based on realistic epidemic surveillance data has been developed, but this model needs enhancing to provide spatially disaggregated epidemic estimates while ensuring that real-time implementation is feasible. Objectives To advance state-of-the-art real-time pandemic modelling by (1) developing an existing epidemic model to capture spatial variation in transmission, (2) devising efficient computational algorithms for the provision of timely statistical analysis and (3) incorporating the above into freely available software. Methods Markov chain Monte Carlo (MCMC) sampling was used to derive Bayesian statistical inference using 2009 pandemic data from two candidate modelling approaches: (1) a parallel-region (PR) approach, splitting the pandemic into non-interacting epidemics occurring in spatially disjoint regions; and (2) a meta-region (MR) approach, treating the country as a single meta-population with long-range contact rates informed by census data on commuting. Model discrimination is performed through posterior mean deviance statistics alongside more practical considerations. In a real-time context, the use of sequential Monte Carlo (SMC) algorithms to carry out real-time analyses is investigated as an alternative to MCMC using simulated data designed to sternly test both algorithms. SMC-derived analyses are compared with ‘gold-standard’ MCMC-derived inferences in terms of estimation quality and computational burden. Results The PR approach provides a better and more timely fit to the epidemic data. Estimates of pandemic quantities of interest are consistent across approaches and, in the PR approach, across regions (e.g. R 0 is consistently estimated to be 1.76–1.80, dropping by 43–50% during an over-summer school holiday). A SMC approach was developed, which required some tailoring to tackle a sudden ‘shock’ in the data resulting from a pandemic intervention. This semi-automated SMC algorithm outperforms MCMC, in terms of both precision of estimates and their timely provision. Software implementing all findings has been developed and installed within Public Health England (PHE), with key staff trained in its use. Limitations The PR model lacks the predictive power to forecast the spread of infection in the early stages of a pandemic, whereas the MR model may be limited by its dependence on commuting data to describe transmission routes. As demand for resources increases in a severe pandemic, data from general practices and on hospitalisations may become unreliable or biased. The SMC algorithm developed is semi-automated; therefore, some statistical literacy is required to achieve optimal performance. Conclusions Following the objectives, this study found that timely, spatially disaggregate, real-time pandemic inference is feasible, and a system that assumes data as per pandemic preparedness plans has been developed for rapid implementation. Future work recommendations Modelling studies investigating the impact of pandemic interventions (e.g. vaccination and school closure); the utility of alternative data sources (e.g. internet searches) to augment traditional surveillance; and the correct handling of test sensitivity and specificity in serological data, propagating this uncertainty into the real-time modelling. Trial registration Current Controlled Trials ISRCTN40334843. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in Health Technology Assessment; Vol. 21, No. 58. See the NIHR Journals Library website for further project information. Daniela De Angelis was supported by the UK Medical Research Council (Unit Programme Number U105260566) and by PHE. She received funding under the NIHR grant for 10% of her time. The rest of her salary was provided by the MRC and PHE jointly.

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“…Therefore an epidemic in one population system could evolve differently after spreading to another. However the variability of this kinetic process for the same strain could be usually modeled by a limited range of parameter values [29]. Pandemic influenza cases for example, are suggested to be latent for 2 days and infectious for 2.5 days [30].…”

Section: B Behavioral Similarity Of Epidemicsmentioning

confidence: 99%

Temporal and Spatial Monitoring and Prediction of Epidemic Outbreaks

Zamiri

Yazdi

Goli

2015

IEEE J. Biomed. Health Inform.

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This paper introduces a nonlinear dynamic model to study spatial and temporal dynamics of epidemics of susceptible-infected-removed type. It involves modeling the respective collections of epidemic states and syndromic observations as random finite sets. Each epidemic state consists of the number of infected individuals in an isolated population system and the corresponding partially known parameters of the epidemic model. The infectious disease could spread between population systems with known probabilities based on prior knowledge of ecological and biological features of the environment. The problem is then formulated in the context of Bayesian framework and estimated via a probability hypothesis density filter. Each population system under surveillance is assumed to be homogenous and fixed, with daily reports on the number of infected people available for monitoring and prediction. When model parameters are partially known, results of numerical studies indicate that the proposed approach can help early prediction of the epidemic in terms of peak and duration.

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Real-time epidemic forecasting for pandemic influenza

Cited by 84 publications

References 21 publications

The Parable of Google Flu: Traps in Big Data Analysis

The Parable of Google Flu: Traps in Big Data Analysis

Real-time modelling of a pandemic influenza outbreak

Temporal and Spatial Monitoring and Prediction of Epidemic Outbreaks

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