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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

Performance of the SIR-EAKF (A and B) and the SIR-PF (C and D) for individual strains predictions of peak timing (A and C) and magnitude (B and D).
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pcbi.1004383.g004: Performance of the SIR-EAKF (A and B) and the SIR-PF (C and D) for individual strains predictions of peak timing (A and C) and magnitude (B and D).

Mentions: Forecast accuracy differs by filter, timing of forecast initiation, and the metric as well as the time series being forecast. Tallied over all forecasts, the PF in general produces more accurate predictions of peak timing (within ±1 wk of observation), peak magnitude (within ±20% of observation), and epidemic duration (within ±2 wk of observation), while the EAKF is more accurate predicting onset timing (within ±1 wk of observation, Fig 3). However, neither filter was able to predict outbreak onset or duration in advance of these events. Given the great irregularity in epidemic timing in Hong Kong, this outcome is not surprising. Some epidemics last for over a year in Hong Kong (Fig 1 and S1 Fig); in such instances, even 10 weeks after the outbreak peak, the conclusion of the epidemic remains difficult to predict accurately. Both filters were able to more accurately predict peak timing and peak magnitude by individual strain than for the aggregate time series combining all circulating strains (Fig 4). This finding suggests that strain specific observations may provide cleaner signals that enable more accurate forecast using the single strain SIR model.


Forecasting Influenza Epidemics in Hong Kong.

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

Performance of the SIR-EAKF (A and B) and the SIR-PF (C and D) for individual strains predictions of peak timing (A and C) and magnitude (B and D).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4520691&req=5

pcbi.1004383.g004: Performance of the SIR-EAKF (A and B) and the SIR-PF (C and D) for individual strains predictions of peak timing (A and C) and magnitude (B and D).
Mentions: Forecast accuracy differs by filter, timing of forecast initiation, and the metric as well as the time series being forecast. Tallied over all forecasts, the PF in general produces more accurate predictions of peak timing (within ±1 wk of observation), peak magnitude (within ±20% of observation), and epidemic duration (within ±2 wk of observation), while the EAKF is more accurate predicting onset timing (within ±1 wk of observation, Fig 3). However, neither filter was able to predict outbreak onset or duration in advance of these events. Given the great irregularity in epidemic timing in Hong Kong, this outcome is not surprising. Some epidemics last for over a year in Hong Kong (Fig 1 and S1 Fig); in such instances, even 10 weeks after the outbreak peak, the conclusion of the epidemic remains difficult to predict accurately. Both filters were able to more accurately predict peak timing and peak magnitude by individual strain than for the aggregate time series combining all circulating strains (Fig 4). This finding suggests that strain specific observations may provide cleaner signals that enable more accurate forecast using the single strain SIR model.

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