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

Tizzoni M, Bajardi P, Poletto C, Ramasco JJ, Balcan D, Gonçalves B, Perra N, Colizza V, Vespignani A - BMC Med (2012)

Bottom Line: In addition, we studied the effect of data incompleteness on the prediction reliability.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Epidemiology Laboratory, Institute for Scientific Interchange, ISI, Torino, Italy.

ABSTRACT

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.

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Related in: MedlinePlus

Natural history of influenza. After acquiring the infection, a susceptible individual enters the latent compartment, where he is infected but not yet infectious. After the average latency period, each infected individual becomes infectious, and may or may not show symptoms. Symptomatic cases are more infectious than asymptomatic cases. Finally, all infected individuals recover after the average infectious period and become immune to the disease.
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Figure 1: Natural history of influenza. After acquiring the infection, a susceptible individual enters the latent compartment, where he is infected but not yet infectious. After the average latency period, each infected individual becomes infectious, and may or may not show symptoms. Symptomatic cases are more infectious than asymptomatic cases. Finally, all infected individuals recover after the average infectious period and become immune to the disease.

Mentions: The epidemic model within each subpopulation considers a compartmental approach specific for the disease under study [55]. In the present application to the H1N1 pandemic, we assumed that each individual can be in one of the following discrete states: susceptible, latent, symptomatic infectious able to travel, symptomatic infectious unable to travel, asymptomatic infectious, and permanently recovered [2]. The model assumes homogeneous mixing within each subpopulation. The disease transmission rate of symptomatic infectious individuals is β, and it is assumed to be rescaled by a factor rβ = 50% for asymptomatic individuals [24,27]. After the infection, susceptible individuals enter the latent compartment, where they are infected but not yet contagious. After the latency period, assumed to be equal to the incubation period and of average duration ε-1, exposed individuals become infectious and have a probability (1-pa) of developing clinical symptoms, with pa considered to be the probability of becoming asymptomatic equal to 33% [56]. Change in traveling behavior after the onset of symptoms is modeled by setting to 50% the probability 1-pt that individuals would not travel when ill [24]. Eventually, infected individuals recover after the average infectious period μ-1, and they are no longer susceptible. Figure 1 shows the natural history of influenza simulated using the H1N1 GLEAM model. All the stochastic processes modeling the transitions of individuals in the different compartments and their mobility are mathematically defined by discrete stochastic chain binomial and multinomial processes [57,58] to preserve the discrete and stochastic nature of the processes. Individuals are discrete but indistinguishable, because no additional population structure (for example, households or workplaces) is being considered.


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

Tizzoni M, Bajardi P, Poletto C, Ramasco JJ, Balcan D, Gonçalves B, Perra N, Colizza V, Vespignani A - BMC Med (2012)

Natural history of influenza. After acquiring the infection, a susceptible individual enters the latent compartment, where he is infected but not yet infectious. After the average latency period, each infected individual becomes infectious, and may or may not show symptoms. Symptomatic cases are more infectious than asymptomatic cases. Finally, all infected individuals recover after the average infectious period and become immune to the disease.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585792&req=5

Figure 1: Natural history of influenza. After acquiring the infection, a susceptible individual enters the latent compartment, where he is infected but not yet infectious. After the average latency period, each infected individual becomes infectious, and may or may not show symptoms. Symptomatic cases are more infectious than asymptomatic cases. Finally, all infected individuals recover after the average infectious period and become immune to the disease.
Mentions: The epidemic model within each subpopulation considers a compartmental approach specific for the disease under study [55]. In the present application to the H1N1 pandemic, we assumed that each individual can be in one of the following discrete states: susceptible, latent, symptomatic infectious able to travel, symptomatic infectious unable to travel, asymptomatic infectious, and permanently recovered [2]. The model assumes homogeneous mixing within each subpopulation. The disease transmission rate of symptomatic infectious individuals is β, and it is assumed to be rescaled by a factor rβ = 50% for asymptomatic individuals [24,27]. After the infection, susceptible individuals enter the latent compartment, where they are infected but not yet contagious. After the latency period, assumed to be equal to the incubation period and of average duration ε-1, exposed individuals become infectious and have a probability (1-pa) of developing clinical symptoms, with pa considered to be the probability of becoming asymptomatic equal to 33% [56]. Change in traveling behavior after the onset of symptoms is modeled by setting to 50% the probability 1-pt that individuals would not travel when ill [24]. Eventually, infected individuals recover after the average infectious period μ-1, and they are no longer susceptible. Figure 1 shows the natural history of influenza simulated using the H1N1 GLEAM model. All the stochastic processes modeling the transitions of individuals in the different compartments and their mobility are mathematically defined by discrete stochastic chain binomial and multinomial processes [57,58] to preserve the discrete and stochastic nature of the processes. Individuals are discrete but indistinguishable, because no additional population structure (for example, households or workplaces) is being considered.

Bottom Line: In addition, we studied the effect of data incompleteness on the prediction reliability.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Computational Epidemiology Laboratory, Institute for Scientific Interchange, ISI, Torino, Italy.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus