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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

Schematic illustration of the model flowchart. The Global Epidemic and Mobility (GLEAM) computational model is based on a data-driven approach. The left column represents the three input databases; the center column represents the dynamic processes that are modeled at each time step, along with their determinants; and the right column indicates example quantities for the model output. Each box is color-coded according to the corresponding dynamic process.
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Figure 2: Schematic illustration of the model flowchart. The Global Epidemic and Mobility (GLEAM) computational model is based on a data-driven approach. The left column represents the three input databases; the center column represents the dynamic processes that are modeled at each time step, along with their determinants; and the right column indicates example quantities for the model output. Each box is color-coded according to the corresponding dynamic process.

Mentions: The GLEAM model is implemented in C/C++. A detailed description of the algorithmic structure of GLEAM has been reported previously [49]. Briefly, GLEAM is implemented in a modular manner, with each module performing a specific function. The compartmental model and the epidemic parameters are defined in a configuration text file that is loaded when the program starts. Subsequently, the program loads three data input files: the population database, the short-range mobility network, and the long-range mobility network. During each time step, which represents a full day, the following modules are called into the sequence: air travel, the compartmental transitions (where the force of infection takes into account both the infection dynamics and the short-range movement of individuals), and the partial aggregation of the results at the desired level of geographic resolution. After the last time step, the program generates the final output, which can be further processed for analysis. A schematic representation is provided in Figure 2.


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)

Schematic illustration of the model flowchart. The Global Epidemic and Mobility (GLEAM) computational model is based on a data-driven approach. The left column represents the three input databases; the center column represents the dynamic processes that are modeled at each time step, along with their determinants; and the right column indicates example quantities for the model output. Each box is color-coded according to the corresponding dynamic process.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Schematic illustration of the model flowchart. The Global Epidemic and Mobility (GLEAM) computational model is based on a data-driven approach. The left column represents the three input databases; the center column represents the dynamic processes that are modeled at each time step, along with their determinants; and the right column indicates example quantities for the model output. Each box is color-coded according to the corresponding dynamic process.
Mentions: The GLEAM model is implemented in C/C++. A detailed description of the algorithmic structure of GLEAM has been reported previously [49]. Briefly, GLEAM is implemented in a modular manner, with each module performing a specific function. The compartmental model and the epidemic parameters are defined in a configuration text file that is loaded when the program starts. Subsequently, the program loads three data input files: the population database, the short-range mobility network, and the long-range mobility network. During each time step, which represents a full day, the following modules are called into the sequence: air travel, the compartmental transitions (where the force of infection takes into account both the infection dynamics and the short-range movement of individuals), and the partial aggregation of the results at the desired level of geographic resolution. After the last time step, the program generates the final output, which can be further processed for analysis. A schematic representation is provided in Figure 2.

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