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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

Peak timing: effect of sampling of the mobility network limited to the top 500 airports. Difference in the median peak weeks in the reference stochastic forecast output (SFO) set, where the full mobility dataset was considered, and the top 500 scenario, for the 500 busiest airports, as a function of the median peak week in the reference SFO set. Dots are color-coded according to the corresponding airport's climate zone. In the inset, we show the box plot indicating the distribution of the differences (in days) between the peak week of the reference SFO set and the SFO set considering only the top 500 airports. The differences were considerable, with median differences of about 3 weeks.
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Figure 14: Peak timing: effect of sampling of the mobility network limited to the top 500 airports. Difference in the median peak weeks in the reference stochastic forecast output (SFO) set, where the full mobility dataset was considered, and the top 500 scenario, for the 500 busiest airports, as a function of the median peak week in the reference SFO set. Dots are color-coded according to the corresponding airport's climate zone. In the inset, we show the box plot indicating the distribution of the differences (in days) between the peak week of the reference SFO set and the SFO set considering only the top 500 airports. The differences were considerable, with median differences of about 3 weeks.

Mentions: As a consequence of the low transmissibility and the small seasonal effect, the spread of the pandemic on the sampled network appeared to be faster in the northern hemisphere, leading to an earlier activity peak for most of the airports, except for a few cases, which showed a delay of up to 4 weeks compared with the full database scenario, because of their reduced connectivity in the sampled network (Figure 14). The largest airports in Europe and North America, such as Paris in France and Atlanta in the USA, experienced the largest shift in the activity peak, ranging from 20 to 45 days earlier than the corresponding timing of the SFO that integrated the full dataset. In the southern hemisphere and the Tropics, the activity peak was postponed with respect to the reference SFO set. For large airports, the delay was limited to 2 weeks, as shown in the cases of Santiago in Chile and Buenos Aires in Argentina; however, for some less connected airports, such as Shenzhen in China and Campinas in Brazil, the delay in the influenza activity peak was 45 days.


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)

Peak timing: effect of sampling of the mobility network limited to the top 500 airports. Difference in the median peak weeks in the reference stochastic forecast output (SFO) set, where the full mobility dataset was considered, and the top 500 scenario, for the 500 busiest airports, as a function of the median peak week in the reference SFO set. Dots are color-coded according to the corresponding airport's climate zone. In the inset, we show the box plot indicating the distribution of the differences (in days) between the peak week of the reference SFO set and the SFO set considering only the top 500 airports. The differences were considerable, with median differences of about 3 weeks.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 14: Peak timing: effect of sampling of the mobility network limited to the top 500 airports. Difference in the median peak weeks in the reference stochastic forecast output (SFO) set, where the full mobility dataset was considered, and the top 500 scenario, for the 500 busiest airports, as a function of the median peak week in the reference SFO set. Dots are color-coded according to the corresponding airport's climate zone. In the inset, we show the box plot indicating the distribution of the differences (in days) between the peak week of the reference SFO set and the SFO set considering only the top 500 airports. The differences were considerable, with median differences of about 3 weeks.
Mentions: As a consequence of the low transmissibility and the small seasonal effect, the spread of the pandemic on the sampled network appeared to be faster in the northern hemisphere, leading to an earlier activity peak for most of the airports, except for a few cases, which showed a delay of up to 4 weeks compared with the full database scenario, because of their reduced connectivity in the sampled network (Figure 14). The largest airports in Europe and North America, such as Paris in France and Atlanta in the USA, experienced the largest shift in the activity peak, ranging from 20 to 45 days earlier than the corresponding timing of the SFO that integrated the full dataset. In the southern hemisphere and the Tropics, the activity peak was postponed with respect to the reference SFO set. For large airports, the delay was limited to 2 weeks, as shown in the cases of Santiago in Chile and Buenos Aires in Argentina; however, for some less connected airports, such as Shenzhen in China and Campinas in Brazil, the delay in the influenza activity peak was 45 days.

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