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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

Travel-related measures in the early stage of the epidemic. (A) Probability distribution of the arrival time (date of arrival of the first symptomatic case) in Germany for different values of traffic reduction, ϕ. The vertical dotted line indicates the observed arrival time in the country, as obtained from official reports, and the vertical solid line indicates the starting date of the travel restrictions (25 April, 2009), which was the day after the international alert. The probability distributions were obtained from 2,000 stochastic realizations, and data were binned over 7 days. (B) Cumulative probability distributions of the first seeding event from Mexico to Germany for different values of traffic reduction ϕ. We considered any source of infection in the seeding event, including symptomatic cases and non-detectable infected cases, such as latent and asymptomatic. (C) Delay in the case importation from Mexico to a given country compared with the reference stochastic forecast output (SFO) as a function of the travel reduction ϕ. The delay was measured in terms of the date at which the cumulative distribution of the seeding from Mexico (B) reached 90%.
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Figure 4: Travel-related measures in the early stage of the epidemic. (A) Probability distribution of the arrival time (date of arrival of the first symptomatic case) in Germany for different values of traffic reduction, ϕ. The vertical dotted line indicates the observed arrival time in the country, as obtained from official reports, and the vertical solid line indicates the starting date of the travel restrictions (25 April, 2009), which was the day after the international alert. The probability distributions were obtained from 2,000 stochastic realizations, and data were binned over 7 days. (B) Cumulative probability distributions of the first seeding event from Mexico to Germany for different values of traffic reduction ϕ. We considered any source of infection in the seeding event, including symptomatic cases and non-detectable infected cases, such as latent and asymptomatic. (C) Delay in the case importation from Mexico to a given country compared with the reference stochastic forecast output (SFO) as a function of the travel reduction ϕ. The delay was measured in terms of the date at which the cumulative distribution of the seeding from Mexico (B) reached 90%.

Mentions: Instead of measuring the average delay only, we considered for every country the probability distribution of the arrival time of the first symptomatic infectious individual, with no regard to the source of infection, which allowed us to take into account the stochasticity of these events in order to explore how the probability distributions would change for increasing travel reductions. Germany is an example where, based on our simulations, the arrival time probability distribution would have peaked a few days later than the real arrival date (Figure 4A). However, travel reductions of a magnitude equal to 60% or 90% would not be able to delay the distribution peak time, and would result only in a change in the tail of the distribution; a more rapid drop after the peak would then be followed by an increase later on, owing to the arrival of cases from countries other than Mexico. By focusing only on the seeding from Mexico, we were able to compute the cumulative distribution of all seeding events, taking into account latent, symptomatic, and asymptomatic infected individuals. We found that the cumulative probability distribution of the seeding could be reduced by travel-related measures, resulting in a slower importation rate (Figure 4B). By fixing the cumulative probability at 90%, we computed the delay induced by the travel reductions for a set of countries (Figure 4C). Even with an unfeasibly large traffic drop of 90%, the achieved delay was less than 20 days. This would offer additional time to activate the pandemic preparedness plans of each country to control the initial local transmission of a novel strain, such as by enhancing surveillance, but it would provide little or no benefit in gaining time for vaccination interventions, given that the scale of vaccine development, production, and distribution is about 6 months.


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)

Travel-related measures in the early stage of the epidemic. (A) Probability distribution of the arrival time (date of arrival of the first symptomatic case) in Germany for different values of traffic reduction, ϕ. The vertical dotted line indicates the observed arrival time in the country, as obtained from official reports, and the vertical solid line indicates the starting date of the travel restrictions (25 April, 2009), which was the day after the international alert. The probability distributions were obtained from 2,000 stochastic realizations, and data were binned over 7 days. (B) Cumulative probability distributions of the first seeding event from Mexico to Germany for different values of traffic reduction ϕ. We considered any source of infection in the seeding event, including symptomatic cases and non-detectable infected cases, such as latent and asymptomatic. (C) Delay in the case importation from Mexico to a given country compared with the reference stochastic forecast output (SFO) as a function of the travel reduction ϕ. The delay was measured in terms of the date at which the cumulative distribution of the seeding from Mexico (B) reached 90%.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Travel-related measures in the early stage of the epidemic. (A) Probability distribution of the arrival time (date of arrival of the first symptomatic case) in Germany for different values of traffic reduction, ϕ. The vertical dotted line indicates the observed arrival time in the country, as obtained from official reports, and the vertical solid line indicates the starting date of the travel restrictions (25 April, 2009), which was the day after the international alert. The probability distributions were obtained from 2,000 stochastic realizations, and data were binned over 7 days. (B) Cumulative probability distributions of the first seeding event from Mexico to Germany for different values of traffic reduction ϕ. We considered any source of infection in the seeding event, including symptomatic cases and non-detectable infected cases, such as latent and asymptomatic. (C) Delay in the case importation from Mexico to a given country compared with the reference stochastic forecast output (SFO) as a function of the travel reduction ϕ. The delay was measured in terms of the date at which the cumulative distribution of the seeding from Mexico (B) reached 90%.
Mentions: Instead of measuring the average delay only, we considered for every country the probability distribution of the arrival time of the first symptomatic infectious individual, with no regard to the source of infection, which allowed us to take into account the stochasticity of these events in order to explore how the probability distributions would change for increasing travel reductions. Germany is an example where, based on our simulations, the arrival time probability distribution would have peaked a few days later than the real arrival date (Figure 4A). However, travel reductions of a magnitude equal to 60% or 90% would not be able to delay the distribution peak time, and would result only in a change in the tail of the distribution; a more rapid drop after the peak would then be followed by an increase later on, owing to the arrival of cases from countries other than Mexico. By focusing only on the seeding from Mexico, we were able to compute the cumulative distribution of all seeding events, taking into account latent, symptomatic, and asymptomatic infected individuals. We found that the cumulative probability distribution of the seeding could be reduced by travel-related measures, resulting in a slower importation rate (Figure 4B). By fixing the cumulative probability at 90%, we computed the delay induced by the travel reductions for a set of countries (Figure 4C). Even with an unfeasibly large traffic drop of 90%, the achieved delay was less than 20 days. This would offer additional time to activate the pandemic preparedness plans of each country to control the initial local transmission of a novel strain, such as by enhancing surveillance, but it would provide little or no benefit in gaining time for vaccination interventions, given that the scale of vaccine development, production, and distribution is about 6 months.

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