This paper is in the following e-collection/theme issue:

Public Health Surveillance Practice (163) Influenza and Influenza-Like-Illness (ILI) Surveillance (217)

Published on in Vol 9, No 2 (2017):

Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms

Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms

Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms

Authors of this article:

Susannah Paul1 ;   Osaro Mgbere2 ;   Raouf Arafat3 ;   Biru Yang3 ;   Euncie Santos3
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    Objective

    The objective was to forecast and validate prediction estimates of influenza activity in Houston, TX using four years of historical influenza-like illness (ILI) from three surveillance data capture mechanisms.

    Background

    Using novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to earlier detection of influenza activity increases and decreases. Anticipating surges gives public health professionals more time to prepare and increase prevention efforts.

    Methods

    Data was obtained from three surveillance systems, Flu Near You, ILINet, and hospital emergency center (EC) visits, with diverse data capture mechanisms. Autoregressive integrated moving average (ARIMA) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. Estimates were then compared to actual ILI percentages for the same period.

    Results

    Forecasted estimates had wide confidence intervals that crossed zero. The forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ILINet forecasted estimates and actual percentages had the least differences. ILINet performed best when forecasting influenza activity in Houston, TX.

    Conclusion

    Though the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ILI activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. Further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness.