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Forecasting Influenza Epidemics in Hong Kong.

Yang W, Cowling BJ, Lau EH, Shaman J - PLoS Comput. Biol. (2015)

Bottom Line: Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead.Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads.These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.

View Article: PubMed Central - PubMed

Affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.

ABSTRACT
Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.

No MeSH data available.


Related in: MedlinePlus

Comparison of forecast accuracy for Hong Kong (HK) and New York City (NYC) using the EAKF and PF filters, as well as random sampling from historical records for HK.Forecast accuracy was evaluated by grouping predictions based on (A) predicted lead time (i.e. how far in the future the peak is predicted) or (B) actual forecast week relative to the observed peak. Positive leads indicate that a peak is forecast in the future; negative leads indicate that a peak is forecast in the past; a 0 week lead indicates that a peak is forecast for the week of forecast initiation.
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pcbi.1004383.g005: Comparison of forecast accuracy for Hong Kong (HK) and New York City (NYC) using the EAKF and PF filters, as well as random sampling from historical records for HK.Forecast accuracy was evaluated by grouping predictions based on (A) predicted lead time (i.e. how far in the future the peak is predicted) or (B) actual forecast week relative to the observed peak. Positive leads indicate that a peak is forecast in the future; negative leads indicate that a peak is forecast in the past; a 0 week lead indicates that a peak is forecast for the week of forecast initiation.

Mentions: We also compared with the ILI+ forecasts generated for New York City, a temperate city with population size and density comparable to Hong Kong (8.4 million vs 7.2 million; 10,725 people/km2 vs 6,544 people/km2), to those of Hong Kong. Over the 2003–2013 period and using epidemic curves aggregated for all circulating strains [20], peak prediction accuracy for Hong Kong is lower (Fig 5A). This is likely due to the more complex influenza transmission dynamics in Hong Kong, e.g., longer outbreak duration and multiple peaks in a year (S1 Fig). Indeed, this gap disappeared when forecast accuracy was evaluated by timing relative to the observed peak, as opposed to the predicted lead week (Fig 5B). For those forecasts initiated 3 weeks prior to the observed local peak or thereafter, accuracies for Hong Kong were comparable to or higher than those for New York City (Fig 5B). Moreover, when compared with a simple analog method, both filter methods clearly were more accurate (Fig 5).


Forecasting Influenza Epidemics in Hong Kong.

Yang W, Cowling BJ, Lau EH, Shaman J - PLoS Comput. Biol. (2015)

Comparison of forecast accuracy for Hong Kong (HK) and New York City (NYC) using the EAKF and PF filters, as well as random sampling from historical records for HK.Forecast accuracy was evaluated by grouping predictions based on (A) predicted lead time (i.e. how far in the future the peak is predicted) or (B) actual forecast week relative to the observed peak. Positive leads indicate that a peak is forecast in the future; negative leads indicate that a peak is forecast in the past; a 0 week lead indicates that a peak is forecast for the week of forecast initiation.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4520691&req=5

pcbi.1004383.g005: Comparison of forecast accuracy for Hong Kong (HK) and New York City (NYC) using the EAKF and PF filters, as well as random sampling from historical records for HK.Forecast accuracy was evaluated by grouping predictions based on (A) predicted lead time (i.e. how far in the future the peak is predicted) or (B) actual forecast week relative to the observed peak. Positive leads indicate that a peak is forecast in the future; negative leads indicate that a peak is forecast in the past; a 0 week lead indicates that a peak is forecast for the week of forecast initiation.
Mentions: We also compared with the ILI+ forecasts generated for New York City, a temperate city with population size and density comparable to Hong Kong (8.4 million vs 7.2 million; 10,725 people/km2 vs 6,544 people/km2), to those of Hong Kong. Over the 2003–2013 period and using epidemic curves aggregated for all circulating strains [20], peak prediction accuracy for Hong Kong is lower (Fig 5A). This is likely due to the more complex influenza transmission dynamics in Hong Kong, e.g., longer outbreak duration and multiple peaks in a year (S1 Fig). Indeed, this gap disappeared when forecast accuracy was evaluated by timing relative to the observed peak, as opposed to the predicted lead week (Fig 5B). For those forecasts initiated 3 weeks prior to the observed local peak or thereafter, accuracies for Hong Kong were comparable to or higher than those for New York City (Fig 5B). Moreover, when compared with a simple analog method, both filter methods clearly were more accurate (Fig 5).

Bottom Line: Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead.Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads.These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.

View Article: PubMed Central - PubMed

Affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.

ABSTRACT
Recent advances in mathematical modeling and inference methodologies have enabled development of systems capable of forecasting seasonal influenza epidemics in temperate regions in real-time. However, in subtropical and tropical regions, influenza epidemics can occur throughout the year, making routine forecast of influenza more challenging. Here we develop and report forecast systems that are able to predict irregular non-seasonal influenza epidemics, using either the ensemble adjustment Kalman filter or a modified particle filter in conjunction with a susceptible-infected-recovered (SIR) model. We applied these model-filter systems to retrospectively forecast influenza epidemics in Hong Kong from January 1998 to December 2013, including the 2009 pandemic. The forecast systems were able to forecast both the peak timing and peak magnitude for 44 epidemics in 16 years caused by individual influenza strains (i.e., seasonal influenza A(H1N1), pandemic A(H1N1), A(H3N2), and B), as well as 19 aggregate epidemics caused by one or more of these influenza strains. Average forecast accuracies were 37% (for both peak timing and magnitude) at 1-3 week leads, and 51% (peak timing) and 50% (peak magnitude) at 0 lead. Forecast accuracy increased as the spread of a given forecast ensemble decreased; the forecast accuracy for peak timing (peak magnitude) increased up to 43% (45%) for H1N1, 93% (89%) for H3N2, and 53% (68%) for influenza B at 1-3 week leads. These findings suggest that accurate forecasts can be made at least 3 weeks in advance for subtropical and tropical regions.

No MeSH data available.


Related in: MedlinePlus