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