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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

Time series of ILI+ for each strain: (A) seasonal A(H1N1), (B) pandemic A(H1N1), (C) A(H3N2), (D) Influenza B, and (E) all strains.Black lines are ILI+ observations; red horizontal lines are baselines; blue vertical lines are the identified onsets; cyan vertical lines are identified endings; grey vertical lines are year divisions.
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pcbi.1004383.g001: Time series of ILI+ for each strain: (A) seasonal A(H1N1), (B) pandemic A(H1N1), (C) A(H3N2), (D) Influenza B, and (E) all strains.Black lines are ILI+ observations; red horizontal lines are baselines; blue vertical lines are the identified onsets; cyan vertical lines are identified endings; grey vertical lines are year divisions.

Mentions: In subtropical and tropical regions, however, the seasonal characteristics of influenza are more diverse. Hong Kong is one such area that experiences highly irregular influenza epidemics from year to year [6–8]. Hong Kong is located on the south coast of China, with a humid subtropical climate. It is one of the most densely populated cities, with a population of over seven million people and a population density of 6544 per km2 [9] (cp. 33.7 per km2 in the U.S. [10]). In addition, Hong Kong is highly connected with mainland China and other regions around the world, attracting over 50 million visitors per year [11]. This large influx of visitors may increase the importation of influenza cases and further facilitate local transmission. Due to these unique climatic and socioeconomic features, influenza epidemics in Hong Kong can persist year-round in one year, whereas one or more distinct epidemics can occur in another year (Fig 1). In addition, outbreak intensity, duration, and time from onset to the peak is more variable in Hong Kong than in temperate regions (S1 Fig). This irregularity poses challenges for operational influenza prediction. For instance, initialization of the system at the beginning of the season, as done for temperate regions, would not be possible. As such, it is not clear whether the same forecast system, proven to be valuable for temperate regions with regular epidemics, could be applied to generate forecasts in real time for subtropical and tropical regions.


Forecasting Influenza Epidemics in Hong Kong.

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

Time series of ILI+ for each strain: (A) seasonal A(H1N1), (B) pandemic A(H1N1), (C) A(H3N2), (D) Influenza B, and (E) all strains.Black lines are ILI+ observations; red horizontal lines are baselines; blue vertical lines are the identified onsets; cyan vertical lines are identified endings; grey vertical lines are year divisions.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004383.g001: Time series of ILI+ for each strain: (A) seasonal A(H1N1), (B) pandemic A(H1N1), (C) A(H3N2), (D) Influenza B, and (E) all strains.Black lines are ILI+ observations; red horizontal lines are baselines; blue vertical lines are the identified onsets; cyan vertical lines are identified endings; grey vertical lines are year divisions.
Mentions: In subtropical and tropical regions, however, the seasonal characteristics of influenza are more diverse. Hong Kong is one such area that experiences highly irregular influenza epidemics from year to year [6–8]. Hong Kong is located on the south coast of China, with a humid subtropical climate. It is one of the most densely populated cities, with a population of over seven million people and a population density of 6544 per km2 [9] (cp. 33.7 per km2 in the U.S. [10]). In addition, Hong Kong is highly connected with mainland China and other regions around the world, attracting over 50 million visitors per year [11]. This large influx of visitors may increase the importation of influenza cases and further facilitate local transmission. Due to these unique climatic and socioeconomic features, influenza epidemics in Hong Kong can persist year-round in one year, whereas one or more distinct epidemics can occur in another year (Fig 1). In addition, outbreak intensity, duration, and time from onset to the peak is more variable in Hong Kong than in temperate regions (S1 Fig). This irregularity poses challenges for operational influenza prediction. For instance, initialization of the system at the beginning of the season, as done for temperate regions, would not be possible. As such, it is not clear whether the same forecast system, proven to be valuable for temperate regions with regular epidemics, could be applied to generate forecasts in real time for subtropical and tropical regions.

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