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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

Accuracy predicting gross epidemic activity.Four measures, sensitivity (i.e., true positive rate, TPR), specificity (i.e., true negative rate, SPC), precision (i.e., positive predictive value, PPV), and negative predictive value (NPV) are shown for (A) the SIR-EAKF and (B) the SIR-PF forecast system. Results are tallied over forecast of H1N1 (orange), H3N2 (red), Type B (green), all strains combined time series (blue), and all forecasts (white).
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pcbi.1004383.g002: Accuracy predicting gross epidemic activity.Four measures, sensitivity (i.e., true positive rate, TPR), specificity (i.e., true negative rate, SPC), precision (i.e., positive predictive value, PPV), and negative predictive value (NPV) are shown for (A) the SIR-EAKF and (B) the SIR-PF forecast system. Results are tallied over forecast of H1N1 (orange), H3N2 (red), Type B (green), all strains combined time series (blue), and all forecasts (white).

Mentions: Fig 2 shows the sensitivity (i.e., true positive rate), specificity (i.e., true negativity rate), precision (i.e., positive predictive value), and negative predictive value for the two forecast systems. Both forecast systems can accurately detect/predict an ongoing epidemic (sensitivity>~80%) and do not falsely predict epidemics during dormant periods (specificity >~90%). Tallied over all weekly forecasts, the PF had slightly higher sensitivity (90% vs. 88%) and specificity (95% vs. 94%) than the EAKF. For both filters, the sensitivity and specificity vary by strain; forecasts for H1N1 and Type B in general had lower sensitivity and specificity (e.g., for the EAKF, sensitivity of 83% for H1N1 and 81% for influenza B vs. 93% for H3N2). Supplemental S1–S4 Movies present the forecasts for each of the three strains and all strains combined epidemics at each week.


Forecasting Influenza Epidemics in Hong Kong.

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

Accuracy predicting gross epidemic activity.Four measures, sensitivity (i.e., true positive rate, TPR), specificity (i.e., true negative rate, SPC), precision (i.e., positive predictive value, PPV), and negative predictive value (NPV) are shown for (A) the SIR-EAKF and (B) the SIR-PF forecast system. Results are tallied over forecast of H1N1 (orange), H3N2 (red), Type B (green), all strains combined time series (blue), and all forecasts (white).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004383.g002: Accuracy predicting gross epidemic activity.Four measures, sensitivity (i.e., true positive rate, TPR), specificity (i.e., true negative rate, SPC), precision (i.e., positive predictive value, PPV), and negative predictive value (NPV) are shown for (A) the SIR-EAKF and (B) the SIR-PF forecast system. Results are tallied over forecast of H1N1 (orange), H3N2 (red), Type B (green), all strains combined time series (blue), and all forecasts (white).
Mentions: Fig 2 shows the sensitivity (i.e., true positive rate), specificity (i.e., true negativity rate), precision (i.e., positive predictive value), and negative predictive value for the two forecast systems. Both forecast systems can accurately detect/predict an ongoing epidemic (sensitivity>~80%) and do not falsely predict epidemics during dormant periods (specificity >~90%). Tallied over all weekly forecasts, the PF had slightly higher sensitivity (90% vs. 88%) and specificity (95% vs. 94%) than the EAKF. For both filters, the sensitivity and specificity vary by strain; forecasts for H1N1 and Type B in general had lower sensitivity and specificity (e.g., for the EAKF, sensitivity of 83% for H1N1 and 81% for influenza B vs. 93% for H3N2). Supplemental S1–S4 Movies present the forecasts for each of the three strains and all strains combined epidemics at each week.

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